Content uploaded by Samaneh Movassaghi
Author content
All content in this area was uploaded by Samaneh Movassaghi on Mar 06, 2014
Content may be subject to copyright.
IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 1
Wireless Body Area Networks: A Survey
Samaneh Movassaghi, Student Member, IEEE, Mehran Abolhasan, Senior Member, IEEE,
Justin Lipman, Member, IEEE, David Smith, Member, IEEE, and Abbas Jamalipour, Fellow, IEEE.
Abstract—Recent developments and technological advance-
ments in wireless communication, MicroElectroMechanical Sys-
tems (MEMS) technology and integrated circuits has enabled
low-power, intelligent, miniaturized, invasive/non-invasive micro
and nano-technology sensor nodes strategically placed in or
around the human body to be used in various applications,
such as personal health monitoring. This exciting new area
of research is called Wireless Body Area Networks (WBANs)
and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j
standards, specifically standardized for medical WBANs. The
aim of WBANs is to simplify and improve speed, accuracy,
and reliability of communication of sensors/actuators within, on,
and in the immediate proximity of a human body. The vast
scope of challenges associated with WBANs has led to numerous
publications. In this paper, we survey the current state-of-art of
WBANs based on the latest standards and publications. Open
issues and challenges within each area are also explored as a
source of inspiration towards future developments in WBANs.
Index Terms—IEEE 802.15.6, Medium access control, Physical
Layer, Routing, Wireless Body Area Networks, Wireless Sensor
Networks
I. INTRODUCTION
WORLD population growth is facing three major chal-
lenges [1, 2]: demographic peak of baby boomers,
increase of life expectancy leading to aging population and
rise in health care costs. In Australia, life expectancy has
increased significantly from 70.8 years in 1960 to 81.7 years
in 2010 and in the United States from 69.8 years in 1960
to 78.2 years in 2010, an average increase of 13.5%1.Given
the U.S. age pyramid2shown in Fig. 1, the number of adults
ranging from 60 to 80 years old in 2050 is expected to be
double that of the year 2000 (from 33 million to 81 million
people) due to retirement of baby boomers3. It is expected that
NICTA is funded by the Australian Government as represented by the
Department of Broadband, Communications and the Digital Economy and the
Australian Research Council through the ICT Centre of Excellence program.
Manuscript received March, 07, 2013; revised August 28, 2013.
S. Movassaghi and M. Abolhasan are with the Centre of Real
Time Information Networks (CRIN), School of Communication and
Computing, Faculty of Engineering and Information Technology,
University of Technology, Sydney, NSW 2007, Australia, (e-
mail: Seyedehsamaneh.Movassaghigilani@student.uts.edu.au and
Mehran.Abolhasan@uts.edu.au).
J. Lipman is with Intel IT Labs, Shanghai, China, (e-mail:
Justin.Lipman@intel.com).
D. B. Smith is with National ICT Australia (NICTA), Canberra, ACT 2601,
Australia, and also with Australian National University (ANU), Canberra,
ACT 0200, Australia , (e-mail: David.Smith@nicta.com.au.)
A. Jamalipour is with School of Electrical and Information En-
gineering, University of Sydney, NSW, 2006, Australia, (e-mail: Ab-
bas.Jamalipour@sydney.edu.au.)
Digital Object Identifier 10.1109/SURV.2013.121313.00064
1http://www.worldlifeexpectancy.com/history-of-life-expectancy
2http://thesocietypages.org/socimages/2010/03/08/the-graying-of-america
3http://thesocietypages.org/socimages/2010/03/08/the-graying-of-america
Fig. 1. U.S. Age Pyramid
this increase will overload health care systems, significantly
affecting the quality of life. Further, current trends in total
health care expenditure are expected to reach 20% of the Gross
Domestic Product (GDP) in 2022, which is a big threat to the
US economy. Moreover, the overall health care expenditures in
the U.S. has significantly increased from 250 billion in 1980
to 1.85 trillion in 2004, even though 45 million Americans
were uninsured4. These statistics necessitate a dramatic shift
in current health care systems towards more affordable and
scalable solutions.
On the other hand, millions of people die from cancer, car-
diovascular disease, Parkinson’s, asthma, obesity, diabetes and
many more chronic or fatal diseases every year. The common
problem with all current fatal diseases is that many people
experience the symptoms and have disease diagnosed when
it is too late. Research has shown that most diseases can be
prevented if they are detected in their early stages. Therefore,
future health care systems should provide proactive wellness
management and concentrate on early detection and prevention
of diseases. One key solution to more affordable and proactive
health care systems is through wearable monitoring systems
capable of early detection of abnormal conditions resulting
in major improvements in the quality of life. In this case,
even monitoring vital signals such as the heart rate allows
patients to engage in their normal activities instead of staying
at home or close to a specialized medical service. This can
only be achieved through a network consisting of intelligent,
low-power, micro and nano-technology sensors and actuators,
which can be placed on the body, or implanted in the human
4http://healthcare-economist.com/2006/01/30/trends-in-health-care-
spending
1553-877X/13/$31.00 c
2013 IEEE
2IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
body (or even in the blood stream), providing timely data.
Such networks are commonly referred to as Wireless Body
Area Networks. In addition to saving lives, prevalent use of
WBANs will reduce health care costs by removing the need
for costly in-hospital monitoring of patients.
The latest standardization of WBANs, IEEE 802.15.6 [3],
aims to provide an international standard for low power, short
range (within the human body) and extremely reliable wireless
communication within the surrounding area of the human
body, supporting a vast range of data rates from 75.9 Kbps
(narrowband) up to 15.6 Mbps ultra wide band; for different
sets of applications [4]. This standard will be introduced in
more detail in Section III.
WBANs may interact with the Internet and other existing
wireless technologies like ZigBee, WSNs, Bluetooth, Wire-
less Local Area Networks (WLAN), Wireless Personal Area
Network (WPAN), video surveillance systems and cellular
networks. Hence, marketing opportunities for services and
advanced consumer electronics will thoroughly expand, allow-
ing for a new generation of more intelligent and autonomous
applications necessary for improving one’s quality of life
[5]. WBANs are expected to cause a dramatic shift in how
people manage and think about their health, similar to the
way the Internet has changed the way people look for infor-
mation and communicate with each other [2]. WBANs are
capable of transforming how people interact with and benefit
from information technology. WBAN sensors are capable of
sampling, monitoring, processing and communicating various
vital signs as well as providing real time feedback to the
user and medical personnel without causing any discomfort
[2, 6, 7]. The use of a WBAN allows continuous monitoring
of one’s physiological parameters thereby providing greater
mobility and flexibility to patients. Importantly, as WBANs
provide large time intervals of data from a patient’s natural
environment, doctors will have a clearer view of the patient’s
status [8]. However, formidable technical and social challenges
must be dealt with to allow for their practical adoption. These
challenges offer various system design and implementation
opportunities with the major objectives of minimum delay,
maximum throughput, maximum network lifetime and and
reducing unnecessary communication related energy consump-
tion (e.g. control frame overhead, idle listening and frame
collisions). The user-oriented requirements of WBANs are
equally challenging and have been defined as: ease of use,
security, privacy, compatibility, value and safety [2, 9].
Our intent in this paper is to investigate recent studies in
WBANs, present the challenges in each underlying subfield,
and survey the important results in the field. Existing WBAN
surveys have explored recent academic literature, but do
not cover the actual standardization efforts. Most of these
papers have mentioned some aspects of WBANs in terms of
applications but do not comprehensively provide detailed study
on all the important criteria in WBANs. Therefore, the need
for such a survey in an area with such a fast growth is crucial
to researchers to provide the latest updates on WBANs, their
characteristics, challenges and open issues.
One of the benefits of this paper is that it draws from
the existing WBAN surveys and provides the following main
contributions:
•An overview of research conducted thus far in different
sectors of Wireless Body Area Networks.
•An investigation of the many strict WBAN constraints
from different perspectives.
•A classification of the various applications of WBANs in
different sectors of medical and non-medical.
•A detailed review and classification of routing protocols
and address allocation schemes for these networks.
•An in-depth insight into security challenges in WBANs
and proposed protocols.
•Open issues in each area of research for WBANs and
explanation of why further research is required.
•The most detailed recent compilation of WBAN projects
and publications.
The remaining sections of this paper are reminiscent of a
layered architecture. Section II describes WBAN applications.
The latest standard on WBANs is presented in Section III.
The requirements of WBANs based on the IEEE 802.15.6
standard are provided in Section IV. Characteristics inherent
to WBANs are explored in Section V with emphasis on
system architecture, topology and types of WBAN nodes.
The individual layers that comprise a WBAN are presented
in Section VI. The respective channel models, data rates and
power requirements are described in Section VII. Important
issues associated with WBAN security are described in Section
VIII. WBAN routing protocols are described and classified
in Section IX. WBAN address allocation schemes and their
challenges are discussed in Section X. WBAN specific radio
technologies are presented in Section XI. A comparison of
WBANs with respect to other wireless networks is provided in
Section XII. In Section XIII, the important WBAN challenges
and open issues are described. Lastly, Section XIV concludes
the paper.
II. APPLICATIONS OF WBANS
WBAN applications span a wide area such as military,
ubiquitous health care, sport, entertainment and many other
areas. IEEE 802.15.6 categorizes WBAN applications into
medical and non-medical (Consumer Electronics) as can be
seen in Table I. The main characteristic in all WBAN appli-
cations is improving the user’s quality of life [8]. However,
the technological requirements of WBANs are application-
specific. Some in-body and on-body applications are shown
in Table II.
A. Medical Applications
WBANs have a huge potential to revolutionize the future
of health care monitoring by diagnosing many life threatening
diseases and providing real time patient monitoring [10]. De-
mographers have predicted that the worldwide population over
65 will have doubled in 2025 to 761 million from the 1990
population of 357 million. This implies that by 2050 medical
aged care will become a major issue. By 2009, the health care
expenditure in the United States was about 2.9 trillion and is
estimated to reach 4 trillion by 2015, almost 20% of the gross
domestic product. Also, one of the leading causes of death
is related to cardiovascular disease, which is estimated to be
as much as 30 percent of deaths worldwide [11, 12]. Based
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 3
TAB L E I
APPLICATIONSOF WBANS
WBAN Applications
Medical
Wearable WBAN
Assessing Soldier Fatigue and Battle Readiness
Aiding Professional and Amature Sport Training
Sleep Staging
Asthma
Wearable Health Monitoring
Implant WBAN Cardiovascular Diseases
Cancer Detection
Remote Control of Medical Devices
Ambient Assisted Living (AAL)
Patient Monitoring
Tele-medicine Systems
Non-Medical
Real Time Streaming
Entertainment Applications
Emergency (non-medical)
on advances in technology (in micro-electronic miniaturization
and integration, sensors, the Internet and wireless networking)
the deployment and servicing of health care services will be
fundamentally changed and modernized. The use of WBANs
is expected to augment health care systems to enable more
effective management and detection of illnesses, and reaction
to crisis rather than just wellness [2, 12].
Using WBANs in medical applications allows for continu-
ous monitoring of one’s physiological attributes such as blood
pressure, heart beat and body temperature. In cases where
abnormal conditions are detected, data being collected by the
sensors can be sent to a gateway such as a cell phone. The
gateway then delivers its data via a cellular network or the
Internet to a remote location such as an emergency center
or a doctor’s room based on which an action can be taken
[13, 14]. Additionally, WBANs will be a key solution in early
diagnosis, monitoring and treatment of patients with possibly
fatal diseases of many types, including diabetes, hypertension
and cardiovascular related diseases. Medical applications of
WBANs can be further classified into three subcategories as
follows:
1) Wearable WBAN: Wearable medical applications of
WBANs can further be classified into the following two
subcategories: a) Disability Assistance, b) Human Perfor-
mance Management. Some of these applications are mentioned
bellow:
Assessing Soldier Fatigue and Battle Readiness – The activ-
ity of soldiers in the battlefield can be monitored more closely
by WBANs. This can be achieved through a WBAN consisting
of cameras, biometric sensors, GPS (Global Positioning Sys-
tem) and wireless networking combined with an aggregation
device for communication with other soldiers and centralized
monitoring. However, in order to prevent ambushes, a secure
communication channel should exist among the soldiers [16].
WBANs can also be used by policemen and fire-fighters [8].
The use of WBANs in harsh environments can be instrumental
in reducing the probability of injury while providing improved
monitoring and care in case of injury.
Aiding Professional and Amature Sport Training – The
training schedules of athletes can easily be tuned via WBANs
as they provide monitoring parameters, motion capture and
rehabilitation. Moreover, the realtime feedback provided to the
user in these networks allows for performance improvement
and prevents injuries related to incorrect training [17].
Sleep Staging – Sleep is an important behavior and regu-
lar physiological function which consumes one-third of our
everyday life. A large population is suffering from sleep
disorders - an average of 27% of the world population5.The
consequences of such disorders can be quite severe and lead to
cardiovascular diseases, sleepiness at work place and drowsy
driving. The effect of sleep disorder on work performance is
estimated to cost 18 billion in lost productivity. Therefore,
sleep monitoring has gained great interest in the recent years.
Sleep disorders can be realized through a polysomnography
test which requires analysis of a number of biopotentials
recorded over night in a sleep laboratory. However, these
measurements require a lot of cables that run from the head
to a box connected to the patient’s belt and interrupt the
patient from falling sleep. It also disturbs the patient’s motion
and initiates artifacts and noise that reduce the signal quality.
WBANs are capable of delocalization of the intelligence and
instruments in their sensor nodes and removal of all cables
[18].
Asthma – A WBAN and accompanying sensors are capable
of monitoring allergic agents in the air and providing real time
feedback to a physician, which can help millions of patients
suffering from asthma.
Wearable Health Monitoring – WBANs in conjunction with
sensors and other devices on the human body can provide real
time health monitoring. For instance, a Gluecocellphone which
is a cell phone with a glucose module can be used for patients
with diabetes. The cellphone receives glucose diagnoses from
the glucose module which may then be stored or sent to a
doctor for analysis [17].
2) Implant WBAN: This class of applications is relative to
nodes implanted in the human body either underneath the skin
or in the blood stream.
Diabetes control – 6.4% of the world’s adult population,
which represent 285 million people, suffered from diabetes in
2010. This number is estimated to reach 438 million by 2030,
7.8% of the adult population6. Research has shown Diabetes
to result in long-term medical issues if not carefully monitored
and treated7. Frequent monitoring provided by WBANs is
capable of reducing the risk of fainting, enables proper dosing,
and eliminates risks of loss of circulation, later life blindness
and more complications.
Cardiovascular Diseases – Cardiovascular diseases are
known as the major cause of death for 17 million people
5http://www.chinadaily.com.cn/china/2013-03/21/content 16320337.htm
6http://www.worlddiabetesfoundation.org/composite-35.htm
7http://my.clevelandclinic.org/disorders/diabetes mellitus/hic longterm
problems for people with diabetes.aspx
4IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
TAB L E I I
CHARACTERISTICS OF IN/ON-BODY APPLICATIONS [15]
Application Type Sensor Node Data Rate Duty Cycle (per de-
vice) % per time Power Consumption QoS (Sensitive
to Latency) Privacy
In-Body Application
Glucose sensor Few Kbps <1% Extremely low Ye s High
Pacemaker Few Kbps <1% low Yes High
Endoscope Capsule >2Mbps <50% low Yes Medium
On-Body Medical Application
ECG 3 Kbps <10% Low Yes High
SpO2 32 Kbps <1% low Yes High
Blood Pressure <10 bps <1% High Yes Medium
On-Body Non-Medical Application
Music for Headsets 1.4 Mbps High Relatively High Yes Low
Forgotten Things Monitor 256 Kbps Medium Low No Low
Social Networking <200 Kbps <1% Low No High
annually8, which can be significantly reduced or prevented
with appropriate health care strategies. Myocardial Infarction
(MI) can be greatly reduced by monitoring episodic events
and other abnormal conditions through WBAN technology.
Cancer Detection – Cancer death rates are estimated to
increase by 50%, reaching up to 15 million by 20209.WBAN
based sensors capable of monitoring cancer cells in the human
body will enable physicians to continually diagnose tumors
without biopsy providing more timely analysis and treatment.
3) Remote Control of Medical Devices: The ubiquitous
Internet connectivity of WBANs allows for networking of the
devices and services in home care known as Ambient Assisted
Living (AAL), where each WBAN wirelessly communicates
with a back-end medical network [19]. AAL aims to prolong
the self-conducted care of patients that are assisted in their
home, minimizing the dependency on intensive personal care,
increasing quality of life and decreasing society costs. In fact,
ambient assisted living will foster a new generation of IT
systems with characteristics such as anticipatory behaviour,
context awareness, user friendliness and flexibility [20].
Patient Monitoring – One key application of WBANs is its
use in monitoring vital signals, as well as providing real time
feedback and information on the recovery process in health
monitoring applications. More specifically, they sense and
wirelessly transmit vital signal measurements such as heart
rate, body temperature, respiration rate, blood pressure, body
implant parameters and chest sounds. WBANs are also capable
of adminstration of drugs in hospitals, remote monitoring of
human physiological data, aid rehabilitation and provide an
interface for diagnostics. Continuous patient monitoring, and
providing necessary medication when required, are considered
as important development areas for WBANs. As WBANs can
provide interconnection amongst various devices in or around
the body such as hearing aids, digital spectacles and so on,
their application could go beyond patient monitoring and also
include post-treatment follow-up, pharmaceutical research,
trauma care, remote assistance in accidents and research in
chronic diseases.
Telemedicine Systems – Available telemedicine systems
either use a power demanding protocol like Bluetooth, which
is open to interference from other devices working in a similar
frequency, or dedicated wireless channels for transferring in-
formation to remote stations. Therefore, they restrict prolonged
monitoring. Whereas integrating WBANs in a telemedicine
8http://www.who.int/cardiovascular diseases/resources/atlas.en
9http://www.who.int/mediacentre/news/releases/2003/pr27/en
system allows for long periods of unobtrusive ambulatory
health monitoring.
B. Non-Medical Applications:
Non-Medical applications of WBANs can be further clas-
sified into five subcategories as follows:
1) Real Time Streaming: This class of applications involve
video streaming such as capturing a video clip by the camera
in a cellular phone, trading shows for sport goods along with
the latest fashion designs and 3D video. Audio streaming is
also possible through voice communication for headsets for
instance listening to explanation of art at the museum or
listening to the bus schedule information on the bus stop,
multicasting for conference calls, browsing music samples in
a music CD store. This category also includes stream transfer
which is used for remote control of entertainment devices,
body gesture recognition/motion capture, vital sign and body
information-based entertainment service, identification, emo-
tion detection and to monitor forgotten things by sending an
alert to the owner.
2) Entertainment Applications: This category consists of
gaming applications and social networking. Appliances such
as microphones, MP3-players, cameras, head-mounted dis-
plays and advanced computer appliances can be used as
devices integrated in WBANs. They can be used in virtual
reality and gaming purposes (game control with hand gesture,
mobile body motion game and virtual world game), personal
item tracking, exchanging digital profile/business card and
consumer electronics.
3) Emergency (non-medical): Off-body sensors (eg. built
into the house) are capable of detecting a non-medical emer-
gency such as fire in the home or flammable/poisionous gas
in the house and must urgently communicate this information
to body-worn devices to warn the wearer of the emergency
condition [17].
4) Emotion Detection: Recent research has shown the
effective realization of human emotions via speech and visual
data analysis. More specifically, wearable sensing technologies
have enabled emotion detection through the induction of
physical manifestations throughout the body that leads to the
production of signals to be measured via simple bio-sensors.
For instance, fear increases respiration rate and heart-beat,
which results in palm sweating and more. Therefore, one’s
emotional status can be monitored anywhere and anytime
through monitoring emotion-related physiological signals like
ElectroCardioGraph (ECG), ElectroMyoGraph (EMG), Elec-
troEncephaloGraph (EEG), Electrodermal Activity (EDA), etc.
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 5
This can be achieved through wearable bio-sensors that can
be integrated in blood pressure sensors, earrings or watches,
respiration sensors in T-shirts, conductivity sensors deployed
in shoes and more.
5) Secure Authentication: This application refers to utiliz-
ing both physiological and behavioral biometrics such as iris
recognition, fingerprints and facial patterns. This is one of the
key applications of WBANs due to duplicability and forgery,
which has motivated the use of new behaviorial/physical
characteristics of the human body, in essence multi-modal
biometric, gait and electroencephalography [5].
III. HISTORY OF THE IEEE 802.15.6 STANDARD
Early developments in Wireless Personal Area Networks
(WPANs) were first made in the 90s by different groups work-
ing at MIT (Massachusetts Institute of Technology). Their
initial aim was to interconnect information devices attached
to the human body. They also intended to use electric field
sensing to determine body positioning, through which the
capability of modulating the electric field for data transmission
throughout the body was realized.
Recent developments in wireless technologies has a major
focus on increasing network throughput which shifts the
focus of WPANs to short range, low power and low cost
technologies [21]. Network lifetime has a greater importance
in WBANs as devices are expected to perform over longer
periods of time. Also, WPANs do not satisfy the medical
communication requirements because of close proximity to
the human body tissue. Thus, a standard model was required
for the successful implementation of Body Area Networks
addressing both its consumer electronics and medical appli-
cations.
The IEEE 802 working group had a number of success
stories in the realization of the international standardization for
WBANs [10]. A standing committee, Wireless Next Genera-
tion (WNG), was established in January 2006, within WG15
(Working Group) aiming for the examination of new topics
and directions [22]. In May 2006, an interest group of WBAN,
namely, (IG-WBAN) was initially established. The executive
committee of IEEE 802 WG15, formally approved IG-WBAN
as a Study Group namely SG-WBAN [22]. In January 2008,
SG-WBAN was further certified as a Task Group (TG6)
under 802.15 [3]. The call for WBAN applications by TG6
was later closed in May 2008 and compiled all submitted
application into a single document [23]. The IEEE 802.15.6
working group established the first draft of the communication
standard of WBANs in April 2010, optimized for low-power
on-body/in-body nodes for various medical and non-medical
applications [10]. The approved version of the IEEE 802.15.6
standard was ratified in February 2012 [3] and describes
its aim as follows: “To develop a communication standard
for low power devices and operation on, in or around the
human body (but not limited to humans) to serve a variety of
applications including medical, consumer electronics, personal
entertainment and other.”
IV. REQUIREMENTS OF WBANSINIEEE 802.15.6
The main requirements of IEEE 802.15.6 standard are listed
below [16, 17, 24–26]:
•WBAN links should support bit rates in the range of 10
Kb/s to 10 Mb/s.
•Packet Error Rate (PER) should be less than 10% for
a 256 octet payload for a majority (95%) of the best-
performing links based on PER.
•Nodes should be capable of being removed and added to
the network in less than 3 seconds.
•Each WBAN has to be capable of supporting 256 nodes
(Section IV provides additional clarification on number
of nodes).
•Nodes should be capable of reliable communication even
when the person is on the move. Although it is acceptable
for network capacity to be reduced, data should not be
lost due to unstable channel conditions. The considered
applications include postural body movements relative to
sitting, walking, twisting, turning, running, waving arms
and dancing among others which result in the shadowing
effect and channel fading. Nodes in a WBAN may move
individually with respect to each other, however the
WBAN itself may move location resulting in interference.
•Jitter, latency and reliability should be supported for
WBAN applications that require them. Latency should be
less than 125 ms in medical applications and less than
250 ms in non-medical applications whilst jitter should
be less than 50 ms.
•On-body and in-body WBANs should be capable of
coexisting within range.
•Up to 10 randomly distributed, co-located WBANs
should be supported by the physical layer in a 6m3cube.
•All devices should be capable of transmitting at 0.1
mW (-10 dBm) and the maximum radiated transmission
power should be less than 1 mW (0 dBm). This complies
with the Specific Absorption Rate (SAR) of the Federal
Communications Commission’s 1.6 W/Kg in 1g of body
tissue 10.
•WBANs should be capable of operating in a heteroge-
nous environment where networks of different standards
cooperate amongst each other to receive information.
•A WBAN can incorporate UWB technology with a
narrow-band transmission to cover different environments
and support high data rates. For instance, some medical
application such as ECG monitoring might require a
UWB-based WBAN to support higher data rates.
•WBANs must incorporate QoS management features to
be self-healing and secure as well as allowing priority
services.
•Power saving mechanisms should be incorporated to
allow WBANs to operate in a power constrained envi-
ronment.
V. C HARACTERISTICS OF WBANS
A. Types of Nodes in a WBAN
Anode in a WBAN is defined as an independent device with
communication capability. Nodes can be classified into three
different groups based on their functionality,implementation
and role in the network. The classification of nodes in WBANs
based on functionality is as follows:
10http://www.fcc.gov/oet/rfsafety/sar.html
6IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
Personal Device (PD) – This device is in charge of col-
lecting all the information received from sensors and actuators
and handles interaction with other users. The PD then informs
the user through an external gateway, a display/LEDs on the
device or an actuator. This device may also be called body-
gateway, sink, Body Control Unit (BCU) or PDA in some
applications [8].
Sensor – Sensors in WBANs measure certain parameters
in one’s body either internally or externally. These nodes
gather and respond to data on a physical stimuli, process
necessary data and provide wireless response to information.
These sensors are either physiological sensors, ambient sen-
sors or biokinetics [8, 9]. Some existing types of these sensors
could be used in one’s wrist watch, mobile, or earphone
and consequently, allow wireless monitoring of a person
anywhere, anytime and with anybody. A list of different types
of commercially available sensors used in WBANs are as
follows: EMG, EEG, ECG, Temperature, Humidity, Blood
pressure, Blood glucose, Pulse Oximetry (SpO2), CO2Gas
sensor, Thermistor, Spirometer, Plethysmogram, DNA Sensor,
Magnetic Biosensor, Transmission Plasmon Biosensor, Motion
(Gyroscope/Accelerometer/Tri-Axial Accelerometer), etc.
Actuator – The actuator interacts with the user upon receiv-
ing data from the sensors [8]. Its role is to provide feedback
in the network by acting on sensor data, for example pumping
the correct dose of medicine into the body in ubiquitous health
care applications [27].
IEEE 802.15.6 has proposed another classification for nodes
in a WBAN based on the way they are implemented within
the body, which is provided as follows [13, 28]:
Implant Node – This type of node is planted in the human
body, either immediately underneath the skin or inside the
body tissue.
Body Surface Node – This type of node is either placed on
the surface of the human body or 2 centimeters away from it.
External Node – This type of node is not in contact with
the human body and rather a few centimeters to 5 meters away
from the human body.
The classification of nodes in WBANs based on their rol e
in the network is as follows:
Coordinator – The coordinator node is like a gateway to
the outside world, another WBAN, a trust center or an access
coordinator. The coordinator of a WBAN is the PDA, through
which all other nodes communicate.
End Nodes – The end nodes in WBANs are limited to
performing their embedded application. However, they are not
capable of relaying messages from other nodes.
Relay – The intermediate nodes are called relays. They
have a parent node, possess a child node and relay messages.
In essence if a node is at an extremity (e.g. a foot), any data
sent is required to be relayed by other nodes before reaching
the PDA. The relay nodes may also be capable of sensing
data.
B. Number of Nodes in a WBAN
In [29–32], which are drafts of IEEE standards related to
technical requirements of WBANs, the number of nodes in
a WBAN is stated to range from a few actuators or sensors
communicating with a portable handset reaching up to tens
to hundreds of actuators or sensors communicating with a
gateway to the Internet. A typical medical network based on
WBANs is stated to have 6 nodes with a scalable configuration
that supports up to 256 nodes [32]. The requirements stated
in [30] also mention an operating range of 3mfor WBANs,
reaching up to 10 piconets per person with the support of 256
nodes in each network within a 6m3cube [26, 33, 34]. Only
one hub is allowed to exist in a WBAN with its number of
nodes ranging from 0 to nM axBAN Size; which is defined
to be 64 in the IEEE 802.15.6 standard due to limitations in
transmission strategy [35]. But, since 2-4 WBANs are stated to
coexist on the same person (per m2) [31], a maximum of 256
nodes can exist per network. In respect to address allocation,
a one-octet WBAN identifier (WBAN ID) is utilized for
allocating an abbreviated address to a node, hub or WBAN
in its frame exchanges [36]. The value of this octet ranges
between x00 and xF F (0-255).
Although the number of nodes in a WBAN is not generally
limited, due to limitations in the nature of the network in
terms of communication protocols, network architecture and
transmission techniques the numbers can be limited in real
application scenarios [8]. For instance, in [2] the maximum
number of nodes in a WBAN is stated to be 20 nodes as
each superframe of length 1 second is divided into non-
overlapping 50 ms time slots; thus, enabling 20 nodes to
transmit orthogonally. In [36], the maximum number of nodes
is considered to be 50, which has been determined as a total of
50 orthogonal channels designed to be allocated to the nodes
in each time frame. In fact, the type and number of devices
that form a WBAN is stated to change over time based on
their interaction with other WBANs and the environment.
C. Topology used in WBANs
The IEEE 802.15.6 working group has considered WBANs
to operate in either a one-hop or two-hop star topology with
the node in the center of the star being placed on a location
like the waist [21, 37]. Two feasible types [38] of data
transmission exist in the one-hop star topology: transmission
from the device to the coordinator and transmission from the
coordinator to the device.
The communication methods that exist in the star topology
are beacon mode and non-beacon mode.Inthebeacon mode,
the network coordinator, which is the node in the center of
the star topology controls the communication. It transmits
periodic beacons to define the beginning and the end of a
superframe to enable network association control and device
synchronization. The duty cycle of the system, which is the
length of the beacon period, can be specified by the user
and based on WBAN’s standard [38, 39]. In the non-beacon
mode, a node in the network is capable of sending data to the
coordinator and can use Carrier Sense Multiple Access with
Collision Avoidance (CSMA/CA) when required. The nodes
need to power up and poll the coordinator to receive data.
However, the coordinator cannot communicate with the nodes
at all times as the nodes must wait till they are invited to
participate in a communication [38].
Since both one-hop and two-hop star topologies exist in
WBANs, careful considerations need to be taken into account
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 7
TABLE III
COMPARISON OF ONE-HOP STAR NETWORK AND MULTI-HOP NETWORK [40]
Comparison criteria Star Networks Multi-Hop Networks
Energy Consumption
For nodes in close proximity to the PDA, the power used to
transmit to the PDA will be low. The nodes further away, however,
will consistently require more power to be able to transmit
information.
The nodes that are closest to the PDA consume more energy as
they will have to forward not only their own information but also
information from other nodes.
Transmission Delay
The star network presents the least possible delay present in
transmission from any sensor to the PDA, as there is only a single
hop.
Dependent on how the network is configured. In terms of delay,
the nodes closest to the PDA can get their information through
quickly, without any intermediate relay.
Interference Sensors that are farther away from the PDA require transmission
with higher power, increasing the amount of interference.
Since each node is only transmitting to its neighbor nodes, the
energy of transmission is kept low and hence mitigates the effects
of interference.
Node Failure and Mobility Only the failed node will be affected and the rest of the network
can perform as needed.
The part of the network that involves the failed node has to be
reconfigured. Overheads are involved.
Fig. 2. Communication Tiers in a Wireless Body Area Network
upon the choice of the one-hop or the two-hop topology. When
all nodes in the network are directly connected to the sink,
the network is considered to have a one-hop star topology.
In a WBAN, the coordinator is known as the sink node to
which all nodes talk. However, in a multi-hop architecture
nodes are connected to access points via other nodes. Table
III provides a comparison between a multi-hop network and
a one-hop star topology [40]. This table shows that multi-
hop transmission has a higher delay and lower transmission
power compared to the one-hop star topology. The multi-
hop configuration involves overheads along with its network
operation; as increasing the number of hops could lead to a
high complexity. More specifically, using relays in WBANs
assists in reducing the concentration of the transmission power
from the source to its destination. Thus, the further apart the
source and destination are in distance, the higher transmission
power is required. Through the use of relays, the transmission
heat will be distributed and a convenient heat will be obtained
for the surrounding area of the transmitting sensor. As per
the latest version of the IEEE standard proposed for WBANs
in February 2012 [3], only two hops are supported in IEEE
WBAN standards compliant communication. Proprietary sys-
tems could use more than two hops, but then inter-operability
would be a problem, as they would not be standard-compliant.
D. Communication Architecture of WBANs
The communication architecture of WBANs can be sepa-
rated into three different tiers as follows:
•Tier-1: Intra-WBAN communication
•Tier-2: Inter-WBAN communication
•Tier-3: Beyond-WBAN communication
Fig. 2 illustrates these communication tiers in an efficient,
component-based system for WBANs. In Fig. 2, the devices
are scattered all over the body in a centralized network
architecture where the exact location of a device is application-
specific [41]. However, as the body may be in motion (e.g.
running, walking) the ideal body location of sensor nodes is
not always the same; hence, WBANs are not regarded as being
static [8].
Tier-1: Intra-WBAN communication – Tier-1 depicts the
network interaction of nodes and their respective transmission
ranges (∼2meters) in and around the human body. Fig.
2 illustrates WBAN communication within a WBAN and
between the WBAN and its multiple tiers. In Tier-1, variable
sensors are used to forward body signals to a Personal Server
(PS), located in Tier-1. The processed physiological data is
then transmitted to an access point in Tier-2.
Tier-2: Inter-WBAN communication – This communication
tier is between the PS and one or more access points (APs).
The APs can be considered as part of the infrastructure, or
even be placed strategically in a dynamic environment to
handle emergency situations. Tier-2 communication aims to
interconnect WBANs with various networks, which can easily
be accessed in daily life as well as cellular networks and the
Internet [5]. The more technologies supported by a WBAN,
the easier for them to be integrated within applications.
The paradigms of inter-WBAN communication are divided
into two subcategories as follows:
Infrastructure based architecture – The architecture shown
in Fig. 3 is used in most WBAN applications as it facilitates
dynamic deployment in a limited space such as a hospital
8IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
Fig. 3. Inter-WBAN Communication: Infrastructure-based mode
as well as providing centralized management and security
control. The AP can act as a database server related to its
application [5].
Ad-hoc based architecture – In this architecture, multiple
APs transmit information inside medical centers as shown in
Fig. 4. The APs in this architecture form a mesh construction
that enables flexible and fast deployment, allowing for the
network to easily expand, provide larger radio coverage due
to multi-hop dissemination and support patient mobility. The
coverage range of this configuration is much larger compared
to the infrastructure based architecture, and therefore facili-
tates movement around larger areas. In fact, this interconnec-
tion extends the coverage area of WBANs from 2 meters to
100 meters, which is suitable for both short and long term
setups [5].
Tier-3: Beyond-WBAN Communication – The design of
this communication tier is for use in metropolitan areas. A
gateway such as a PDA can be used to bridge the connection
between Tier-2 and this tier; in essence from the Internet to the
Medical Server (MS) in a specific application [8]. However,
the design of Tier-3 for communication is application-specific.
In essence, in a medical environment a database is one of the
most important components of Tier-3 as it includes the medical
history and profile of the user. Thus, doctors or patients can be
notified of an emergency status through either the Internet or
a Short Message Service (SMS). Additionally, Tier-3 allows
restoring all necessary information of a patient which can
be used for their treatment [5]. However, depending on the
application, the PS in Tier-1 can use GPRS/3G/4G instead of
talking to an AP.
VI. LAYE R S O F WBANS
Generally, all approved standards of 802.15.xpropose PHY
and MAC layers. They do not supply any network, transport
or application layer and therefore call for other parties to
develop them. The IEEE 802.15.6 (WBAN) working group
has defined new Physical (PHY) and Medium Access Control
(MAC) layers for WBANs that provide low complexity, low
cost, high reliability, ultra-low power and short range wireless
communication in or around the human body. The standard
has also mentioned that “There may be a logical node man-
agement entity (NME) or hub management entity (HME) that
Fig. 4. Inter-WBAN Communication: Ad-Hoc based mode
exchanges network management information with the PHY
and MAC as well as with other layers”.
A. Physical Layer
The PHY layer of IEEE 802.15.6 is responsible for the
following tasks: activation and deactivation of the radio
transceiver, Clear channel assessment (CCA) within the cur-
rent channel and data transmission and reception. The choice
of the physical layer depends on the target application:
medical/non-medical, in, on and off-body. The PHY layer
provides a procedure for transforming a physical layer service
data unit (PSDU) into a physical layer protocol data unit
(PPDU). IEEE 802.15.6 has specified three different physical
layers: Human Body Communication (HBC), Narrow Band
(NB) and Ultra-Wide Band (UWB).
NB PHY is responsible for data transmission/reception,
activation or deactivation of the radio transceiver and Clear
Channel Assessment (CCA) in the current channel. Based
on the NB specifications, in order to construct PPDU, the
PSDU has to be pre-appended with a Physical Layer Preamble
(PLCP) and a physical layer header (PSDU) shown in Fig.
6. The PCLP preamble aids the receiver in carrier-offset
recovery, packet detection and timing synchronization. The
PCLP header is sent after the PCLP preamble via the data
rates given in its operating frequency band. It transfers the
necessary information required for successfully decoding a
packet to its receiver. The PSDU, which is the last component
of PPDU contains a MAC header, a MAC frame body and a
Frame Check Sequence (FCS) [10]. NB PHY uses Differential
8-Phase-shift Keying (D8PSK), Differential Binary Phase-
shift Keying (DBPSK) and Differential Quadrature Phase-
shift Keying (DQPSK) modulation techniques except at 420-
450 MHz where it uses the Gaussian Minimum Shift Keying
(GMSK) modulation technique.
HBC PHY provides the Electrostatic Field Communication
(EFC) requirements that covers modulation, preamble/Start
Frame Delimiter (SFD) and packet structure. The structure
of the Physical Protocol Data Unit (PPDU) is composed
of the PLCP preamble, Start Frame Delimiter (SFD), PLCP
Header, and PHY Payload (PSDU) as shown in Fig. 5. The
SFD and preamble are specified data patterns. They are pre-
generated and sent before the payload and packet header. The
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 9
Fig. 5. HBC PPDU Structure of IEEE 802.15.6
SFD sequence is only transmitted once, whereas the preamble
sequence is sent four times to assure packet synchronization.
The initial PLCP preamble is created as a 64-bit gold code
sequence which is repeated four times and is spread using
a Frequency Shift Code (FSC). The SFD sequence is also
created by using a 64-bit gold code generator that is spread
using an FSC. Once the receiver has received the packet, it
uses the preamble sequence to detect the beginning of the
packet. It then detects the start of the frame using the SFD
[10]. The PHY header consists of the following fields: data
rate, pilot information, synchronization, WBAN ID, payload
length and a CRC calculated over the PHY header.
The UWB physical layer is used for communication be-
tween on-body devices and for communication between on-
body and off-body devices. Transceivers in a UWB PHY
generate similar signal power levels to that used in the MICS
band and also allow low implementation complexity. Based on
the specifications for UWB PHY, the PPDU bits are converted
into RF signals for transmission in the wireless medium.
UWB PPDU consists of a Synchronization Header (SHR),
a PHY Header (PHR) and PSDU. The SHR is made up of
repetitions of Kasami intervals of length 63. It consists of two
subfields: the first subfield is a preamble that is intended for
packet detection, timing synchronization, and frequency offset
recovery; and the second subfield is the SFD shown in Fig. 8.
The physical header carries information about the scrambler
seed, length of the payload and the data rate of the PSDU.
The receiver uses the information in the PHR to decode the
PSDU.
A key issue in the development of the IEEE 802.15.6
standard was the selection of physical layer frequency bands
to be utilized given varying worldwide regulations. Fig. 7
shows the different frequency bands used by WBANs in
IEEE 802.15.6. Ultrawideband frequencies offer higher data
rates and higher throughput whilst lower frequencies have
less shadowing and attenuation from the body [42]. Table.
IV specifies the frequency bands and channel bandwidths for
these propagation methods.
The HBC PHY has a bandwidth of 4 MHz and operates
in two frequency bands centered at 16 MHz and 27 MHz.
The United States, Japan and Korea support both of these
frequency bands whereas Europe only supports the 27 MHz
operating band.
The NB PHY uses seven different frequency bands shown
in Table. IV. It offers various bit rates, channels and mod-
ulation schemes. The first licensed band in NB PHY is the
Medical Implant Communication Service (MICS) utilized for
implant communication with a range of 402-405 MHz in most
countries. The next licensed band in NB PHY is the Wireless
Medical Telemetry Services (WMTS) utilized in medical
telemetry systems. Neither MICS nor WMTS support high
data rate applications. The Industrial, Scientific and Medical
(ISM) band is available worldwide and supports high data
rate applications. But, since various wireless devices such as
IEEE 802.15.4 and IEEE 802.15.1 use the ISM band, there is
a high probability for interference [10]. The sixth band (2360-
2400 MHz) of NB PHY is assigned for medical device use.
The seventh band (2400-2483.5 MHz) is a license-free ISM
band that has been used most commonly [21]. Importantly
the 2360-2400 band is not an ISM band; hence, interference
is significantly reduced compared to the 2400+ ISM band.
Two frequency bands exist in the UWB PHY: high band
and low band; each of which are divided into channels with a
bandwidth of 499.2 MHz. The low band only has 3 channels:
(1-3). Channel 2 is considered as a mandatory channel with
the central frequency of 3993.6 MHz. The high band has eight
channels: (4-11). Channel seven is considered as a mandatory
channel with the central frequency of 7987.2 MHz. All other
channels are considered to be optional. At least one of the
mandatory channels has to be supported by a UWB device.
Data rates are typically in the range of 0.5 Mbps to 10 Mbps
with 0.4882 Mbps for the mandatory channel [10].
B. MAC Layer
The IEEE 802.15.6 working group defines a MAC layer
on top of the PHY layer in order to control channel access.
The hub (or coordinator) divides the entire channel (or time
axis) into a chain of superframes for time referenced resource
allocations. The hub also chooses beacon periods of equal
length to bound the superframes. The offsets of the beacon
periods can also be shifted by the hub. The beacons are
normally sent in each beacon period unless prohibited by
regulations in the MICS band or inactive superframes [10].
The coordinator is responsible for channel access coordina-
tion through one of the following three access modes:
1) Beacon Mode with Beacon Period Superframe Bound-
aries: In this channel access mode, the hub sends beacons
in each beacon period unless prohibited by restrictions in
the MICS band or inactive superframes. The hubs manage
the communication of the superframe structure using Timed
frames (T-poll) or beacon frames. The superframe structure of
IEEE 802.15.6 is shown in Fig. 9. It consists of an Exclusive
Access Phase 1 (EAP1), a Random Access Phase 1 (RAP1),
a Type I/II phase, an Exclusive Access Phase 2 (EAP 2), a
Random Access Phase 2 (RAP 2), a Type I/II phase, and a
Contention Access Phase (CAP). In CAPs, RAPs and EAPs,
nodes strive for resource allocation via either the slotted Aloha
access procedure or CSMA/CA. EAP1 and EAP2 are utilized
for high priority traffic such as reporting emergency events;
while CAP, RAP1 and RAP2 are only used for regular traffic.
Type I/II phases are utilized for bi-link allocation intervals,
downlink allocation intervals, uplink allocation intervals, and
delay bi-link allocation intervals. Polling is used in type
10 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
Fig. 6. NB PPDU Structure of IEEE 802.15.6
I/II phases for resource allocation. Based on the application
requirements, any of these periods can be disabled by setting
the duration length to zero. This channel access mode is most
considered by researchers and developers.
2) Non-beacon mode with superframe boundaries: This
access mode is not capable of transmitting beacons and is
forced to use the Timed frames (T-poll) of the superframe
structure. The whole superframe is either covered by one Type
I or one Type II access phase, but not both.
3) Non-beacon mode without superframe boundaries: In
this access mode, only unscheduled Type II polled allocation
is provided by the coordinator, meaning each node has to
establish its own time schedule independently.
Three categories of access mechanisms exist in each period
of the superframe, which are as follows:
(a) Scheduled access and variants (connection-oriented
contention-free access) – This access mechanism schedules
slot allocation in one or multiple upcoming superframes also
named after 1-periodic or m-periodic allocations.
(b) Unscheduled and improvised access (connectionless
contention-free access) – This access mechanism utilizes
posting or polling for resource allocation.
(c) Random access mechanism – In this access mechanism
either the slotted Aloha procedure or CSMA/CA are used for
resource allocation.
VII. CHANNEL MODEL
Nodes in WBANs are scattered in and over the whole body
[8], which creates multiple transmission channels between the
nodes based on their location in/on the body. The channel
models proposed by IEEE 802.15.6 SGBAN are shown in
Table V. In scenarios S1,S2and S3in cases where a hundred
sensors are attached to a person’s body, the system becomes
quite bulky to be carried around. Thus, the USA Federal
Communications Commission (FCC) and communication au-
thorities of other countries have allocated the MICS band
at 402-405 MHz with 300KHz channels to enable wireless
communication with implanted medical devices. This leads
to better penetration through the human tissue compared to
higher frequencies, high level of mobility, comfort and better
patient care in implant to implant (S1), implant to body surface
(S2) and implant to external (S3) scenarios. Additionally,
the 402-405 MHz frequencies have conducive propagation
characteristics for the transmission of radio signals in the
human body and do not cause severe interference for other
radio operations in the same band. In fact, the MICs band is an
unlicensed, ultra-low power, mobile radio service for transmit-
ting data to support therapeutic or diagnostic operation related
to implant medical devices and is internationally available. It is
specifically chosen to provide low-power, small size, fast data
transfer as well as a long communication range. The frequency
range of the MICS band allows high-level integration with
the radio frequency IC (RFIC) technology, which leads to
miniaturization and low power consumption. In summary, high
level integration is difficult at lower frequencies, and higher
frequencies cause severe penetration loss. In fact there is a
severe amount of penetration loss at high frequencies (10 dB
for 10mm tissue penetration) [43].
Table VI provides a list of different frequency bands based
on which the WBAN channel model can be built [28]. These
scenarios are established based on the distance of the commu-
nication nodes, which are body surface, implant and external;
and grouped in classes represented by the Channel Model
(CM). External devices are considered to reach a maximum
distance of up to 5 meters.
Another important approach is to differentiate electromag-
netic wave propagation from devices in or around the body.
However, due to the complex structure of the body shape
and human tissue, a simple path loss model cannot be easily
modeled for WBANs. Moreover, as the node’s antenna is
either placed in or on the body, the influence of the body
on radio propagation also needs to be considered [28].
The authors of [44] have used three types of antennas to
characterize two on-body channels at 2.45 GHz, 5.8 GHz and
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 11
Fig. 7. WBAN Frequency bands
Fig. 8. UWB PPDU Structure of IEEE 802.15.6
TAB L E I V
FREQUENCY BAND AND BANDWIDTH OF DIFFERENT PHY LAYERS OF
IEEE 802.15.6
Human-Body Communication
Frequency Bandwidth
16 MHz 4MHz
27 MHz 4MHz
Narrowband Communication
Frequency Bandwidth
402-405 MHz 300 kHz
420-450 MHz 300 kHz
863-870 MHz 400 kHz
902-928 MHz 500 kHz
956-956 MHz 400kHz
2360-2400 MHz 1MHz
2400-2438.5 MHz 1MHz
UWB Communication
Frequency Bandwidth
3.2-4.7 GHz 499 MHz
6.2- 10.3 GHz 499 MHz
10 GHz. Additionally, they have shown long term fading to
best fit to a log-normal distribution and short-term fading of
the combined signals and envelopes of the branch to be Rician
for the fading environment of on-body channels. In [45], two
path loss models have been studied that operate at 2.4 GHz and
5.8 GHz and consider propagation channel characterization
among two wearable devices that had been placed on a
human body. On-body propagation channels for narrowband
propagation have shown to present high variability due to
relative movement of the body parts. As for UWB channel
characterization for WBANs, in cases where surface waves
were dominant amongst all waves traveling along the human
body via the use of Self-complementary printed horn (HSCA)
TAB L E V
SCENARIOS AND DES CRIPTION OF CHANNEL MODELS IN IEEE 802.15.6
[28]
Scenario Description Frequency Band Channel
Model
S1 Implant to Implant 402 - 405 MHz CM1
S2 Implant to Body Surface 402 - 405 MHz CM2
S3 Implant to External 402 - 405 MHz CM2
S4 Body Surface to Body Sur-
face (LOS)
13.5, 50, 400, 600, 900 MHz,
2.4, 3.1 - 10.6 GHz
CM3
S5 Body Surface to Body Sur-
face (NLOS)
13.5, 50, 400, 600, 900 MHz,
2.4, 3.1 - 10.6 GHz
CM3
S6 Body Surface to External
(LOS)
13.5, 50, 400, 600, 900 MHz,
2.4, 3.1 - 10.6 GHz
CM4
S7 Body Surface to External
(NLOS)
13.5, 50, 400, 600, 900 MHz,
2.4, 3.1 - 10.6 GHz
CM4
antenna, decrement in mean Root Mean Square (RMS) delay
spread has been noticed [46]. In [47], the physical layer of
WBANs is characterized and an estimation on path loss and
delay spread in between two nodes using two half-wave length
dipoles around the human body has been considered. The
path loss exponent is calculated to be 3.1 for line of sight
communication using a realistic human body phantom in free
space and a multipath environment. The legs and arms have
been considered to have similar path loss and the torso has
been investigated as having the highest amount of path loss.
The RMS delay spread and mean excess delay are shown to
increase when antennas are separated. These parameters are
used in the path loss model determination and have shown
excellent verification to the measured results. Also, a log-
normal distribution is described for the extracted models and
the distribution function of the derivation of excess delay and
RMS delay spread.
In [24], a lognormal distribution is stated to be the
best model for small-scale fading in UWB communications.
Whilst, either a lognormal distribution or a gamma distribution
is stated to be the best fit for small-scale fading in narrowband
communications. Importantly, the generalized gamma distribu-
tion consists of both of these distributions. Combined models
that consider all on-body transceiver locations, which include
a typical path loss and small scale fading model with respect
to particular locations, are suitable to describe general WBAN
applications. In [24], the statistical models used in literature,
12 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
Fig. 9. Superframe Structure of IEEE 802.15.6 [10]
TAB L E V I
LIST OF FREQUENCY BANDS FOR IEEE 802.15.6 [28]
Description Frequency Band
Implant 402 - 405 MHz
On-body 13.5 MHz
On-body 5-50MHz (HBC)
On-body 400 MHz
On-body 600 MHz
On-body 900 MHz
On-body 2.4 GHz
On-body 3.1-10.6 GHz
applied to the simulated or measured channel gain data to
describe fading in WBANs, have been compared [48–52].
The most commonly attempted distribution fit is lognormal,
followed by Nakagami-m then Ricean. Whilst the best-fit for
a particular distribution at any given attempt is most often
Weibull, lognormal and gamma11. Even though Nakagami-
m is most often used as a fit, it has a smaller success rate;
and Ricean has considerably smaller success rate compared
to Nakagami-m. The Rayleigh distribution, however, is a poor
fit for most scenarios and environments where it is used. The
details of this comparison can be further studied in [24].
A. Interference
Since a WBAN is most likely to encounter other WBANs,
inter-WBAN interference is of the utmost importance. The
IEEE 802.15.6 task group requires the system to function
properly within a transmission range of up to 3 meters when up
to 10 WBANs are co-located [3]. The different types of radio
interference that can be encountered in WBANs are shown
in Fig. 10. One type of interference occurs when numerous
people wearing WBAN devices step into each others range,
which makes coordination infeasible (off-body interference).
Collision from external sensors may also lead to this type
of interference [53]. Additionally, the unpredictable nature of
postural body movements leads to networks easily moving into
and out of each others range [42]. This issue becomes even
more crucial in the case of wireless technologies with higher
coverage areas.
Generally interference occurs when no-coordination exists
amongst multiple WBANs that coexist in each other’s vicinity
11considering those distributions tested 10 or more times [24]
Fig. 10. Interference in WBANs
(Inter-WBAN Interference) [54, 55]. However, due to the
nature of a WBAN and its high mobility it is infeasible to
allocate a global coordinator to control coexistence amongst
multiple WBANs [56]. In cases where co-located WBANs
use the same channel (similar frequencies), transmissions can
conflict; as the active periods can overlap. Moreover, with the
increase in the number of WBANs that can coexist in short
proximity of each other, the communication link can suffer
performance degradation. Even in cases where small number
of WBANs are deployed in each other’s vicinity, the received
signal strength of the interfering signal can be quite high,
which affects the performance of a particular WBAN [54].
B. Data Rates and Power Requirements
One of the main constraints in WBANs is their limited
power supply. Fig. 11 shows a comparison between power
requirements and data rates in WBANs compared to other
wireless technologies. Accordingly, WBAN protocols require
higher power efficiency when compared to the other existing
protocols. The sensors in WBANs are capable of transmitting
data in a wide range of data rates from 1 Kbit/s to 10 Mbit/s
[57]. In essence, the data rate of in-body nodes vary from
few Kbps in a pacemaker to quite a few Mbps in a capsular
endoscope. Fig. 11 shows that the current technologies meet
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 13
the speed requirement of IEEE 802.15.6 in terms of data rates
but not the power requirements of less than 10mW in WBANs.
Currently, most devices used in WBANs store their recorded
data or transmit them to a monitoring station that uses IEEE
802.15.4 (Bluetooth) or 802.15.1 (Zig-Bee), which do not meet
the power requirements for WBANs.
C. Antenna Design
One major challenge for antenna design in WBANs is
related to alterations in the antenna topology based on the
shape of the human body, which specifies the need for flexible
and textile antennas. However, these types of antennas are
not easily adjustable to body dynamics as they are mainly
built on top of substrates with little deformation capability
[58]. One other major challenge is due to the electromagnetic
interaction between the human body and the antenna. The
human body is considered as a large inhomogeneous object
with high loss and permittivity, which effects the properties
of an antenna being placed in its close proximity. Therefore,
the most important factors towards the practical deployment
of body-antennas can be evaluated through numerical analysis
and measurement setup of the radiation signature outside the
body, and the resonance characteristics of implanted antennas.
Additionally, the surrounding environment of an antenna must
be given in-depth consideration [59]. Various other parameters
of a user such as weight loss/gain, posture and skin change
with age also need to be considered for antenna design in
WBANs. Also the limitations of shape, size, material and
the intrinsic environment need to be taken into account. In
addition, the location of an antenna in the body has major
control on the size and shape of the antenna being used,
therefore restricting the designer. Moreover skin tissue, muscle
and fat change characteristics in respect to heating effects of
the electric field should also be considered in WBAN antenna
design. Existing antennas in WBANs may be classified into
two groups [28]:
1) Magnetic antennas: Magnetic antennas, such as loop
antennas, generate an E-field that is mostly tangential to
the body tissue and, therefore are not capable of coupling
as strongly as the electric antennas. Consequently, body fat
does not heat up. Some partially similar antennas to the
magnetic antennas are the helical-coil antennas, which have
the same heating characteristics as the electrical antennas.
Tissue heating is mainly a result of the strong Electrical Field
(E-field) existing between the coils [28]. Additionally, the
Specific Absorption Rate (SAR) of the far field transmitting
antenna is mainly related to the E-field, whereas the SAR of
the near field transmitting antenna is related to the Magnetic
Field (H-field) [28].
2) Electric antennas: Electric antennas such as dipole
antennas form a large amount of E-field perpendicular to the
body that is absorbed and increases the temperature of the
human tissue. This is because the boundary requirement of
the E-field is discontinuous by the ratio of its permittivities at
the E-field. Since muscle has higher permittivity than fat, the
E-field of the fat tissue is generally higher [28].
The human body is not considered as an ideal medium
for electromagnetic wave transmission at radio frequencies.
Fig. 11. Power Requirements and Data Rates in WBANs [60]
In Table VII, the electrical properties of the body at three
different frequencies are shown. As also seen from Table VII,
muscle and fat have different characteristic impedances Z(Ω),
conductivities ρand dielectric constants r. Consequently,
based on the utilized frequency, high path loss occurs in the
human body due to central frequency shift, power absorption
and alterations in the radiation pattern. Additionally, absorp-
tion effects differ in magnitude based on the characteristics
of the tissue and the frequency of the applied field [28].
In general, propagation throughout the body is affected in
numerous ways due to the electrical properties of the body,
which are as follows:
(1) Body tissue is semi-conductive and therefore capable
of absorbing some of the signal.
(2) Body tissue can react as a parasitic radiator.
(3) The electrical length of the electric field antennas like
dipoles increase as dielectric constant increases.
The antennas designed for WBANs are classified into two
groups based on their location to be either placed on the body
or in the body. A brief explanation of these classifications is
provided as follows:
In-body Antenna Design – As for the antennas being im-
planted in the body, only specific types of materials such as
titanium or platinum can be used due to their bio-compatible
and non-corrosive chemistry, whilst a copper antenna has bet-
ter performance [5]. The MICS band which is from 402-405
MHz is allocated for in-body communication. The wavelength
of this frequency is 744mm and the half wave dipole is
372mm. However, an antenna with such dimensions is not
applicable to in-body operation and therefore these constraints
lead to a much smaller size than the optimum.
On-body Antenna Design – Two key requirements for on-
body communication of antennas are the antenna radiation
pattern and the sensitivity of antennas to the human body. In
[61], a comparison of antenna combinations for on-body com-
munication is provided. Various antennas have been designed
14 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
TAB L E V I I
ELECTRICAL PROPE RTIE S OF TH E HUMAN BODY [15]
Frequency (MHz) Muscle Fat
rρ(S.m−1)Z0(Ω) rρ(S.m−1)Z0(Ω)
100 66.2 0.73 31.6 12.7 0.07 92.4
400 58 0.82 43.7 11.6 0.08 108
900 56 0.97 48.2 11.3 0.11 111
and constructed in the 2.5 GHz and ISM band, such as loop
antennas, patch antennas, patch array antennas and monopole
antennas. Amongst which, the monopole and monopole com-
binations provide the least link loss and the highest path gain
(path gain interprets as the product of all transfer functions
along a path) [61]. Whereas, patch antennas that do not require
additional space are capable of reducing the spread of the
path gain and therefore eliminate multi-path fading [62]. Some
other existing antennas such as the spiral, the bow tie, the
trailing wire, the Planar Inverted-F Antenna (PIFA) and the
loaded PIFA are also applicable in different scenarios.
VIII. SECURITY IN WBANS
Even though security issues are made a high priority in most
networks, little study has been done in this area for WBANs.
Additionally, due to stringent resource constraints in terms
of power, memory, communication rate and computational
capability as well as inherent security vulnerabilities, the
security specifications proposed for other networks are not
applicable to WBANs. The practical deployment of WBANs
and the integration of convenient security mechanisms requires
knowledge of the security requirements of WBANs which are
provided as follows [63]:
1. Secure Management – The decryption and encryption
operation requires secure management at the coordinator in or-
der to provide key distribution to wireless body area networks.
The WBAN coordinator adds and removes WBAN nodes in
a secure manner during association and disassociation.
2. Availability – The availability of the patient’s information
to the physician needs to be ensured at all times. An attack to-
wards availability in WBANs could be capturing and disabling
an ECG node leading to loss of life. Therefore, the operation,
maintenance and capability to switch to another WBAN in
case of availability loss is essential.
3. Data Authentication – Medical and non-medical appli-
cations require data authentication. Both WBAN nodes and
the coordinator node require verification that data is being
sent from the trust center and not a false adversary. Network
nodes in a WBAN and the coordinator node compute a
Message Authentication Code (MAC) for all data by sharing a
secret key. When the correct MAC is calculated, the network
coordinator will realize that the received message is being sent
by a trusted node.
4. Data integrity – When data is transmitted to an insecure
WBAN, its information can be altered. An adversary will
then be capable of modifying a patient’s information prior
to reaching the network coordinator, thus endangering the
patient’s health and maybe even their life. Therefore, the
received data needs to be assured of not being altered by
an adversary through proper data integrity by using data
authentication protocols.
5. Data confidentiality – Protection of data from disclosure
is achievable through data confidentiality. WBAN nodes in
medical applications transmit sensitive information regarding
the status of a patient’s health. Critical information can be
overheard and eavesdropping is possible in communication,
which may cause a considerable amount of damage towards
a patient as the data can be issued for illegal purposes.
Data confidentiality can be achieved through encryption of
a patient’s data via a shared key on a communication channel
secured among the WBAN nodes and their coordinator.
6. Data Freshness – Data integrity and confidentiality can
only be supported if data freshness techniques are used. An
adversary is capable of capturing data in transmissions and
later replaying to create confusion for the WBAN coordinator.
Data freshness assures that data is not reused and its frames
are in order. Two types of data freshness exist which are
as follows: strong freshness that guarantees delay as well
as frame ordering, and weak freshness which provides no
guarantee in terms of delay. Strong freshness is necessary in
synchronization while a beacon is being transmitted to the
WBAN coordinator, whereas weak freshness is necessary for
WBAN nodes with low-duty cycle.
The IEEE 802.15.6 standard has proposed a security
paradigm for WBANs shown in Fig. 12, that defines three
levels of security as follows [64]:
a) Level 0- Unsecured Communication – This is the lowest
level of security in which data is transmitted in unsecured
frames and provides no measure for integrity validation,
authenticity and replay defense, privacy protection and confi-
dentiality.
b) Level 1- Authentication but no Encryption – In this
level of security, data is transmitted in authenticated but un-
encrypted frames. It consists of measures for integrity valida-
tion, authenticity and replay defense. However, it provides no
privacy protection or confidentiality.
c) Level 2- Authentication and Encryption – This is the
highest level of security in which messages are transmitted
in authenticated and encrypted frames; therefore, providing
measures for integrity validation, authenticity, replay defense,
privacy protection and confidentiality. It covers the issues
related to level 0 and level 1. During the association process,
the required security level is selected. In unicast communica-
tion, a pre-shared master key (MK) or a new key (generated
through unauthenticated association) is activated. In the next
step, a Pairwise Temporal Key (PTK) is generated that is
used only once per session. In multicast communication a
Group Temporal Key (GTK) is generated that is shared with
its corresponding group [10].
All nodes and coordinators in a WBAN have to go through
certain stages at the MAC layer before data exchange. This
way frames are required or permitted to exchange between a
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 15
Fig. 12. Security Paradigm of IEEE.802.15.6
hub and a node at each state. The first state is the Orphan
state at where the node does not have any relationship with
the coordinator for secured communication. In fact, this is
the initial stage at which the node enters a relation with its
coordinator. The coordinator and the node are only allowed to
transmit Security Association and control unsecured frames
at this stage in order to share a new key or activate a pre-
shared one. If the coordinator and the node fail to activate/
establish a shared MK, they are not allowed to proceed to
the Associated state. At the Associated state, the node holds
a shared MK with the coordinator for their pairwise temporal
key (PTK) creation, which means the node is associated. The
node and hub are allowed to exchange PTK frames with each
other to confirm the possession of a shared MK, create a PK
and transit to the next state, Secured state. If the MK is invalid/
missing during the PTK creation, they have to move back to
the Orphan state.
At the Secured state, the node is secured as it holds a
PTK with the coordinator for secure frame exchanges. The
node and the coordinator can exchange the following frames:
security disassociation, connection assignment secure frames,
connection request and control unsecured frames. The node
can exchange Connection Request and Connection Assign-
ment frames with the hub to form a connection and transit
to the final state, Connected state. At the Connected state the
node is connected and holds an assigned C onnected NID,a
wakeup arrangement and one or more scheduled allocations
with the coordinator, desired wakeup and optionally scheduled
and unscheduled access.
One of the key techniques for secure communication in
WBANs is known to be via biometrics. This means that the
body itself will be used for managing cryptographic keys for
sensors attached to the body, which allows for secure dis-
tribution of the symmetric key for encryption and decryption.
Most biometric security approaches in WBANs have proposed
to protect WBANs by generating session keys from the
ECG signal and distributing them amongst nodes throughout
the network. However, these methods have accuracy of key
recoverability less than 100% amongst all nodes throughout
the network [65].
In [66], a novel biometrics technique has been proposed
that uses the timing information of heartbeat as an intrinsic
characteristic of the human body for authentication identity or
as a method to secure cipher key distribution for inter-WBAN
communication as well as an identity for entity authentication.
This approach is set upon a symmetric cryptosystem, which
considers the availability of a secured and robust key distribu-
tion scheme. The proposed security approach has less memory
and computational requirements compared to the traditional
cryptosystems and therefore is convenient for use in e-health
and telemedicine applications of WBANs.
The BioSec security technique proposed in [67] uses a
group of similar random numbers obtained from a combination
of biometrics of the human body at different sites to encrypt
and decrypt the symmetric key for secured distribution. In
addition, a fuzzy commitment scheme was proposed to ensure
tolerable recovery of the encryption key due to variations in
the biometric trait obtained from different locations of the
body. The biometric trait at the transmitting terminal was used
to commit the key. At the receiver side, the biosensor received
a copy of the trait and would then decommit it to obtain the
key.
In [68], the biometric features shared amongst body sensors
positioned at various positions of a human body are utilized in
the security framework proposed for wireless body area net-
works. Secure data communication amongst these sensors is
obtained from authentication and selective encryption schemes
with less resource usage in terms of bandwidth, memory
and low computational power when compared with schemes
proposed so far. Accurate authentication is obtained via a
wavelet-domain Hidden Markov Model (HMM) based on non-
Gaussian statistics of ECG signals. Moreover, the biometric
information received from the ECG signals are further used
for encryption. This protocol achieves accurate performance
without requiring strict time synchronization and extra key
distribution. In [69], Cyber-physical solutions (CPSS) towards
security in WBANs are given providing transparent and plug-
n-play explanations to its users. In particular, a specific
Plethysmogram based Key Agreement (PKA) is implemented
on an FPGA considering accuracy, low latency and minimal
resource usage. This design can lead to validation of any
CPSS. More specifically, CPSS-integration of cryptographic
primitives with signal processing aims to provide usable
security solutions in WBANs.
A traditional approach to security in most networks is based
on the public key cryptography. However, this method con-
sumes much more resources and cannot be directly deployed
for sensors in WBANs due to their considerable limitation of
resources and computational capabilities. The authors of [70]
propose a novel light-weight security architecture for WBANs,
which includes key management, random number generation
and a three-step security model. In the first step auto-shared
secret (ASS) is generated by a certain kind of the bio-channel
via each node under a synchronization indication from the
master node. In fact, the ASS is a set of biometric values
generated simultaneously by the nodes from physiological data
16 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
Fig. 13. Energy Consumption of Security Protocols
during a certain time period. In the next step, the initialization
key kinit is distributed under ASS protection. Next, session
keys are then distributed under the protection of kinit.This
approach is unique in using a combination of bio-channel and
wireless channel for secure information transmission, as well
as multiple usage of physiological data for numerous security
aspects.
The authors in [71] have proposed the use of random chan-
nel measurements to create the seed for AES-style scenarios.
Therefore, a unique 128 bit AES key can be generated every
minute. Therefore, significant improvements to the one-off
AES key will be made, even though the Shannon one-time pad
level of security is not met. The key sharing rate for WBAN
channel model based on RSSI measurements has shown to be
very low ( 4 bit/sec), close to 2 bit/sec. This interprets that
key sharing is possible and unconditional security is cannot
be achieved for practical communication rates. A lightweight
secure sensor association and key management scheme was
proposed in [72] for Wireless Body Area Networks where a
group of sensor nodes establish initial trust via Group Device
Pairing (GDP) without prior secret sharing before the meeting.
GDP is an authenticated group key management protocol
which allows for visual verification of the legitimacy of each
member node by an individual person. After deployment,
various types of secret keys can be generated on demand.
The GDP protocol does not require extra hardware devices,
supports batch deployment of sensor nodes to avoid time
wastage and mostly relies on symmetric key cryptography,
as well as allowing batch node addition and revocation.
In [73], a secure sensor allocation for WBANs has been
proposed, where each sensor node is provided with public key
based authentication one-by-one by the controller. Association
is verified by the user through a comparison amongst the LED
blinking patterns. Unfortunately, the overall association time is
quite long as batch deployment is not supported. Additionally,
the assumption of sensor nodes being pre-distributed with the
public key from a trusted authority is impractical.
Fig. 13 shows the percentage of energy consumption relative
to employing security protocols in WBANs. As can be seen
in this figure, a small portion of energy consumed in fresh-
ness transmission is relative to computation (3%), encryption
transmission (1%) and encryption computation (1%). The use
of stream cipher is considered to be the most effective solution
to energy required for overhead transmission, with the cipher
text having the same size as a plain text. Hence, only 16 bytes
of the 60 bytes of a data frame are used with no requirement
for a Cyclic Redundancy Check (CRC) as data integrity can
be self-maintained through the MAC.
IX. WBAN ROUTING
Numerous routing protocols have been designed for Ad-
hoc networks [74] and WSNs [75]. WBANs are similar to
MANETs in terms of the moving topology with group-based
movement rather than node-based movement [76]. However,
WBANs have more strict energy constraints in terms of
transmit power compared to traditional sensor and Ad Hoc
networks as node replacements particularly for implant nodes
can be quite uncomfortable and might require surgery in some
scenarios. Therefore, it is crucial for WBANs to have a longer
network lifetime to avoid constant recharging and replacement
of nodes attached to a person. Additionally, a WBAN has more
frequent topology changes and a higher moving speed,whilst
a WSN has static or low mobility scenarios [76]. Due to the
aforementioned issues and specific WBANs challenges, the
routing protocols designed for MANETs and WSNs are not
applicable to WBANs [16].
A. Challenges of Routing in WBANs
1. Postural Body Movements – Node mobility, energy
management and environmental obstacles increase dynamism
in WBANs, including frequent changes in topology and net-
work components that amplifies the complexity of Quality of
Service (QoS). Additionally, the link quality between nodes
in WBANs varies as a function of time due to various body
movements [77]. Therefore, the proposed routing algorithm
should be adaptive to different topology changes. In this
regard, the authors of [78] have considered WBANs to be
in the category of Delay Tolerant Networks (DTN) due to
disconnection and frequent partitioning relative to postural
body movements. Moreover, some body segments and clothing
result in signal blockage that intensifies RF attenuation. More
specifically, the mobility pattern in WBANs changes with the
order of movements within tens of centimeters whereas the
scale of mobility in WSNs is in the order of meters and tens
of meters.
2. Temperature Rise and Interference – In terms of the
available energy and computing power, the energy level of
nodes needs to be considered in the proposed routing protocol.
Also, in order to minimize interference and avoid tissue heat-
ing, the transmission power of nodes needs to be extremely
low [16].
3. Local Energ y Awaren ess – The proposed routing protocol
has to disperse its communication data among nodes in the
network to balance power usage and minimize failure to
battery supply drainage.
4. Global Network Lifetime – Network lifetime in WBANs
is referred to as the time interval from when the network starts
to the time the network is significantly damaged, which leads
to network partitioning such that the destination cannot be
reached. As battery replacement and charging is not feasible
in implant medical devices, network lifetime is of more
importance in WBANs compared to WPANs and WSNs [21].
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 17
5. Efficient Transmission Range – The low RF transmission
range in WBANs leads to frequent partitioning and discon-
nection amongst sensors in WBANs, which results in similar
performance to DTNs [78]. In cases where the transmission
range of sensors are less than a threshold value, there are
fewer choices for routing to adjacent sensors which leads to a
higher number of transmissions leading to overall temperature
rise. Also, the fewer the number of neighbors, the lower the
probability for packets to arrive at the destination within a
certain hop count. Thus, packets will take longer to arrive at
the destination which leads to an average increase in overall
temperature rise [79].
6. Limitation of Packet Hop Count – According to the IEEE
802.15.6 standard draft for WBANs [30], one-hop or two-
hop communication is allowed in WBANs. Whilst multihop
transmission provides stronger links leading to overall increase
in system reliability. The larger the number of hops, the
higher the energy consumption [80]. However, the limita-
tion of packet hop count has not been considered in most
WBAN routing protocols. Additionally, half-duplex devices
in WBANs reduce the bandwidth as successive hops are
introduced.
7. Heterogeneous environment – Specific applications of
WBANs may require heterogeneous data collection from dif-
ferent sensors with different sampling rates. Therefore, QoS
support in WBANs may be quite challenging.
8. Limitation of resources – Data capacity, energy and
device lifetime of WBANs is strictly limited as they require
a small form factor. Due to limitation of available resources
in WBANs, therefore, WBAN nodes are bound to fail due to
unavailable battery power, memory and bandwidth limitations,
which are major threats to QoS.
This section provides an overview of research being done
in routing protocols for WBANs, which have only been devel-
oped in the past few years, to assist in overall knowledge of
routing challenges in WBANs and possible solutions. Routing
protocols in WBANs can be classified into five groups based
on their location, network structure, temperature, layer and
QoS metrics.
B. Classification of Routing Protocols in WBANs
1) Cluster-based Algorithms: The first class of routing
protocols in WBANs are Cluster-based routing algorithms that
divide nodes in WBANs into different clusters and assign
a cluster-head for each cluster. Data is routed through the
cluster-heads from the sensors to the sink. The aim of this
class of routing protocols is to decrease the number of direct
transmissions from the sensors to the base station. However,
the huge overhead and delay relative to cluster selection are
the main drawbacks of these protocols.
In [81], a data gathering protocol, namely Anybody is
proposed to reduce the number of direct transmissions to a
base station. The proposed approach is based on LEACH [82]
and spreads energy dissipation by choosing its cluster-head at
regular time intervals. The data is then collected and sent to the
base station via the cluster-head. LEACH assumes all nodes
to be in the sending range of the base station whereas Any-
body solves this issue by changing the cluster-head selection
and building a backbone network consisting of cluster-heads.
However, reliability is not considered and energy-efficiency
is not completely investigated. One other improvement to the
LEACH protocol is Hybrid Indirect Transmissions (HIT) [83]
that forms chains by combining the clusters that improve
energy efficiency.
2) Probabilistic Algorithms: Probabilistic routing protocols
periodically update their cost function based on the link state
information, and establish their path among routes with min-
imum cost. However, these protocols require a large number
of transmissions for updating link-state information.
Movassaghi et. al [84] have proposed an energy efficient,
thermal and power aware routing algorithm for WBANs,
named Energy Efficient Thermal and Power Aware routing
(ETPA). This protocol calculates a cost function for route
allocation based on a node’s temperature, energy level and
received power from adjacent nodes. ETPA has shown to
significantly decrease temperature rise and power consump-
tion and provide a more efficient use of available resources.
Additionally, it has a considerably high depletion time that
guarantees longer lasting communication among nodes in
WBANs.
A routing protocol is proposed in [77] for WBANs, the
moving nature of the body is considered in the routing protocol
through an opportunistic scheme that ensures high communi-
cation probability with the sink at all times. Consequently, it
defines two scenarios for communication between the sensor
node and the sink node. For one thing, in cases where the
wrist is at the back of the body, non line of sight (NLOS)
communication is considered where the sensor node will send
data to the relay node and then to the sink node. Whereas,
when the wrist is in the front, line of sight (LOS) communi-
cation exists between the sensor node and sink. Liang et. al
[85] have proposed the distributed (PSR) routing framework
for WBANs where each node nimaintains the matrix Mi
(s×p). This way it stores link quality measurements between
itself and all other nodes in the network during the past p
time slots (pis a predefined parameter and the initial matrix
is empty). PRPLC [86] sets a Link Likelihood Factor (LLF)
namely Pt
ij (0 ≤Pt
ij ≤1), which denotes the likelihood for
link Lij between node iand jto be connected over a discrete
time slot t. LLF is determined to be dynamically updated after
the tth time slot. When a node iwants to route data to a node
d(sink node) and meets node j, node iforwards the packet
to node jif and only if Pt
i,d ≤Pt
j,d is valid.
DVRPLC [78] proposes that all nodes preserve the cumu-
lative path cost to the common sink node. As in PRPLC,
this protocol chooses high likelihood paths to decrease end-to-
end packet delivery delay and decrease intermediate storage
delay relative to storing packets at nodes with low link
likelihood. DVRPLC specifies a Link Cost Factor (LCF) of
Ct
ij (0 ≤Ct
ij ≤Cmax), which stands for the routing cost of
link Lij in a discrete time slot t. OBSFR [87] attempts to avoid
network partitioning, which may arise, by allowing each node
to maintain its source id, seq No and list of node-ids that
demonstrate its path from the source node. Hence, once a
packet arrives at a node for the first time, the node continues
to store the packet until it meets at least one node that is not
listed in the node-ids of the packet.
18 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
3) Cross-Layer Algorithms: The third category is cross
layer routing protocols that combines the challenges of the
network layer with other layers. Even though these protocols
have low energy consumption, high throughput and fixed
end-to-end delay, they cannot supply high performance in
scenarios with high path loss and body motion. The Wireless
Autonomous Spanning Tree Protocol (WASP) proposed in
[80] sets up a spanning tree and divides the time axis into slots
referred as WASP-cycles in a distributed manner to provide
medium access coordination and traffic routing using the same
spanning tree, which leads to higher throughput and lower
energy consumption. The Controlling Access with Distributed
Slot Assignment protocol (CICADA) [88] is a low energy
cross layer routing protocol for WBANs based on multi-
hop TDMA scheduling. CICADA enhances reliability by the
definition of a lognormal distribution for link probability
rather than a circular coverage region. Also, it provides two
way communication, which is an improvement to the WASP
protocol.
Timezone Coordinated Sleeping Mechanism (TICOSS) [89]
adjusts all nodes as Full Functional Devices (FDD) and en-
hances the IEEE 802.15.4 standard by configuring the shortest
path route to the WBAN coordinator, preserving energy and
minimizing hidden terminal collisions through V-scheduling
(due to V-shape communication flow), which doubles the
operational lifetime of IEEE 802.15.4 for high traffic scenarios
and extending IEEE 802.15.4 to support mobility. BIOCOMM
is another cross-layer routing protocol for WBANs designed
based on the interaction of the network and MAC layer to op-
timize overall network performance [90]. Adaptive Multihop
tree-based routing (AMR), proposed in [91] is a distributed
spanning-tree based approach which considers battery level,
Received Signal Strength Indicator (RSSI) and number of
hops. AMR balances energy consumption amongst nodes by
which it provides extended network lifetime and an efficient
number of transmissions per delivered packet.
4) Temperature-based Algorithms : Radio signals gener-
ated via wireless communication generate electric and mag-
netic fields. The exposure of these electromagnetic fields leads
to radiation absorption, which results in average temperature
rise in the human body [92]. Thus, blood flow will be reduced
and sensitive organs may face severe thermal damage. More
specifically, prolonged temperature rise within the body tissue
may result in tissue damage, blood flow reduction in certain
organs and effect enzymatic reactions [93]. The amount of
radiation energy absorbed by the body tissue is defined as the
Specific Absorption Rate (SAR) shown in (1) [92].
SAR =σ|E|2
ρ(W/kg)(1)
where Eis the electric field induced by radiation, σis the
electrical conductivity of the tissue, and ρis the density of
tissue. Exposure to SAR of 8 W/kg for 15 minutes has shown
to result in severe tissue damage [92]. In fact, SAR specifies
the exact upper bound of the allowable transmit power. Thus,
WBAN routing protocols have to actively decrease radiation
emission and temperature. Accordingly, even routes with light
traffic and short delay might not be efficient in terms of
temperature, which makes routing and forwarding infeasible.
The main objective of all temperature based routing algorithms
studied in the literature is to avoid routing to hot-spots.
Heating affects and radiation absorption on the body are
the most important issues when considering wireless commu-
nication on or around the body. Tissue heating can be reduced
with the use of traffic control algorithms and by limiting the
radio’s transmission power. To achieve this aim, communica-
tion between the sensor nodes must be balanced. One existing
solution is the Thermal Aware Routing Algorithm (TARA)
[92], which routes data through low temperature zones. More
specifically, packets are withdrawn from high temperature
zones and rerouted via alternative paths. In cases where the
number of hops reaches three, shortest path routing is selected
via the proposed routing algorithm. However, TARA does not
consider reliability, has a high packet loss ratio, a low network
lifetime and does not consider reliability. Improvements to
TARA where given by the Least Temperature Routing (LTR)
[94] and Adaptive Least Temperature Routing (ALTR) [94],
which reduce the irrelevant loops and hops by maintaining a
list of recently visited nodes. In cases where a predetermined
number of hops are reached, ALTR switches to shortest path
routing in order to eliminate energy consumption. One other
solution is the Least Total Route Temperature (LTRT) [79]
that is a combination of short path routing and LTR where
the temperature of the nodes is translated into graph weights
leading to minimum temperature routes. Through this routing
protocol, lower temperature rise and better energy efficiency
is obtained. However, since each node needs to know the
temperature of all other nodes, overhead becomes a significant
drawback [8].
HPR [93] is a biomedical sensor network routing algorithm
for delay-sensitive applications such as medical monitoring.
It aims to decrease average packet delay and avoid hotspot
formation. HPR chooses the route with minimum hops from
the sender node to the destination unless a hotspot exists in that
path. The routing algorithm for networks of homogenous and
Id-less biomedical sensor nodes (RAIN) [95] is fault tolerant
and operates efficiently even with the depletion of a number
of nodes due to lack of energy. Thermal-Aware Shortest
Hop Routing (TSHR) is another temperature based routing
protocol specifically designed for applications that require a
high priority for transmitting a packet to the destination. The
main drawback of temperature routing approaches is that they
overlook network lifetime and reliability. Movassaghi et al.
[96], have provided a detailed comparison amongst the routing
protocols proposed thus far for WBANs.
5) QoS-based Routing Algorithms: The last category is
QoS routing protocols, which mainly provide a modular
approach by presenting separate modules for different QoS
metrics that operate in coordination with each other. The
modules used in this method are the power efficiency module,
the reliability-sensitive module,thedelay-sensitive module
and the neighbor manager. Hence, these approaches provide
higher reliability, lower end-to-end delay and higher packet
delivery ratio. However, these protocols mainly suffer from
high complexity due to the design of several modules based
on different QoS metrics. A novel QoS-based routing protocol
(LOCALMOR) has been proposed in [97] for biomedical ap-
plications of sensor networks. The proposed protocol functions
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 19
in a localized, distributed, computation and memory efficient
way. It also classifies data traffic into several categories based
on the required QoS metrics where different techniques and
routing metrics are provided for each category.
Razzaque et. al [98] have proposed a data-centric multi-
objective QoS-aware routing protocol, namely DMQoS, for
delay and reliability domains in WBANs. The proposed
protocol provides customized QoS services for each traffic
category based on their generated data types. It employs a
modular design architecture that consists of different units that
operate in coordination with each other to support multiple
QoS services. A reinforcement learning based routing protocol
with QoS support (RL-QRP) has been proposed in [99], which
uses the basic idea of location information. Sensor nodes can
compute the available QoS routes based on the link qualities
of the available routes and the QoS requirements of the data
packet, and then forward data packets to one of the neighbor
nodes. This procedure is continued in forwarding the data
packets to the sink node and is repeated at each relaying node
until the packets reach the sink node. Liang et. al [100] have
proposed a QoS-aware routing protocol for biomedical sensor
networks with the aim of providing differential QoS support
and prioritized routing service in the network. This procedure
is accomplished via the following tasks: establishment and
maintenance of QoS-aware routes, prioritized packet routing,
feedback on network conditions to user application, adaptive
network traffic balance and Application Programming Inter-
faces (API).
In summary, each classification of routing protocols only
aims to satisfy a specific requirement of WBANs. Thus,
designs of new routing protocols are required to meet all the
conditions for operations of WBANs..
X. ADDRESS ALLOCATION IN WBANS
Each node has to be assigned with a unique address for
effective information exchange. As some nodes in a WBAN
are actuators, for the purpose of proper functionality infor-
mation has to reach the nodes at the appropriate time and
in the same order as they were first transmitted. On the
other hand, sensor nodes in health monitoring applications
have to send their data such that it reaches health monitoring
devices in an appropriate time and provides the medical staff
with efficient management of the WBAN. Recently, various
addressing methods have been proposed for WPANs using
different technologies. These addressing schemes normally
rely on IP addressing, mathematical graph algorithms, etc.
However, given the unique characteristics of WBANs, address
allocation in these networks introduce various challenges
which are described as follows:
1. Limitation of address space in WBANs – Due to the
stringent limitation of resources (memory, space, etc.) in
WBANs, their address space is limited. This short addressing
space is kept for identifying child nodes, routers and the
network coordinator [2]. In [101], this issue has been solved
by an appropriate choice of the prime numbers used in the
original Prophet scheme to minimize the number of collisions
for different topologies. In fact, the closer the prime numbers,
the higher the number of collisions.
2. Address interference – Each node has to be assigned with
a unique address once it wants to join the network. Existing ad-
dressing schemes [101, 102] provide short addresses for each
node but do not guarantee collision-free address allocation.
In fact, as shown in [101], there is a trade off between the
number of collisions and the effective address space.
3. Node mobility for address allocation – Postural body
movements generate a dynamic environment in WBANs which
affects the density and topology of nodes in the network as
will move in or out of range.
4. Address wastage and duplication– In order to make
efficient use of the limited address space in WBANs and
prevent address duplication, address reuse should be allowed
once a node wants to join or leave the network.
5. Issues of static addressing – Static addressing assigns
a static address to each node to avoid address interference.
However, the network topology changes once a node leaves
or joins the network. Most of the addressing architectures
proposed thus far have used flat or static addressing, which
requires individual tracking of each node. If not, the network is
flooded in each route request which causes a massive overhead
that is proportional to the size of the network.
Recently, two address allocation schemes have been specif-
ically proposed for WBANs. Movassaghi et al. [103], have
proposed an addressing scheme for WBANs called Hierar-
chical Collision-free Addressing Protocol (HCAP). The pro-
posed protocol is collision-free, tackles the address wastage
problem and eliminates power consumption. The usability and
efficiency of the proposed protocol in WBANs is evaluated
across two scenarios, random location and fixed location.
Movassaghi et al have enhanced the performance of an
address allocation scheme, namely Prophet allocation for use
in WBANs. Each node in the Prophet algorithm receives a
message, which consists of a state vector and an address. In
the next step these addresses and states are updated for each
node. The most important issue with the Prophet algorithm
is the number of collisions related to a change of topology.
In fact, the percentage of collisions in a network with more
number of hops is more than a network with less hops
[101]. This issue has been addressed in the Optimized Prophet
Address Allocation method (OPAA) [101]. It considers the
requirement of each node in a WBAN to be assigned a free
address before participating in any sort of communication. The
allocation scheme is a fully decentralized addressing scheme,
which is applicable to WBANs as it provides low latency,
low communication overhead and low complexity. Theoretical
analysis and simulation experiments have also been conducted
to demonstrate the benefits of this allocation scheme when
compared to other potential schemes. It also solves the issues
related to network partitioning and merging effectively.
In [104], the OPAA and HCAP schemes have been com-
pared. Through simulations it has been shown that the HCAP
scheme outperforms the OPAA scheme in terms of address
interference but has a higher energy consumption.
XI. RADIO TECHNOLOGIES USED IN WBANS
Given the complexities associated with implementing
WBANs, an appropriate wireless technology is required. In
20 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
essence, data being sent from a patient to a central health care
system needs to have continuous awareness of the patient’s
vital functions to provide suitable solutions in case of alerts.
Therefore, communication of WBANs with other wireless
networks becomes crucial [41]. Accordingly, the central node
of a WBAN is capable of communicating with the outside
world using a standard telecommunication structure such as
Bluetooth, WLANs or 2G/3G/4G cellular networks in different
projects.
One important factor in the choice of a wireless technology
is its power usage, which is tied with the design of a power
efficient WBAN. Existing wireless technologies have a huge
peak current and usually reduce the average current drawn
by duty cycling the radio between sleep and active modes.
Technically, WBANs in IEEE 802.15.6 have to be scalable
and have a peak power consumption up to 30 mW in fully
active mode and between 0.001-0.1 mW in stand-by mode
[9].
Even though WBAN devices are low-power and there is
not enough power available for the whole-body SAR to be a
concern, the device may be in close proximity to, or inside
a human body or the localized SAR could be quite large if
all the available power is concentrated in a small volume.
Therefore, the localized SAR into the body must be at its
minimum. Based on the IEEE 802.15.6 standard, WBAN
devices must follow local or international SAR regulations. In
Europe, the European Council Recommendation 519/1999/EC
for exposure guidelines has complied with the recommenda-
tions made by the International Commission on Non-Ionising
Radiation Protection (ICNIRP Guidelines 1998). In the US,
the Federal Communications Commission (FCC) has set the
safety guidelines of the radio frequency which is required from
all phones before being sold in the US. This interprets that
SAR has set certain limits for local exposure (Head); which
is2W/kgin10graminEUand1.6W/kgin1graminUS.
This limits the TX power in EU to 20 mW and in US to
1.6 mW [26].
On the other hand, a WBAN deployed on a human body
may need to support different applications with different re-
quirements in power usage, reliability, data rate and frequency.
Therefore, consistent data transfer amongst different wireless
technologies being used is required to be scalable, provide
uninterrupted connectivity, promote information exchange, en-
sure efficient migration across networks and interconnect plug
and play devices. Thus the chosen wireless technology has to
handle the mixture of these requirements. In terms of QoS,
periodic parametric data, episodic data, real time wave form
data and emergency alarms need to be supported with peer to
peer latency of 10 ms-250 ms and a BER of 10−10 to 10−3
[9].
The existing and emerging radio technologies [105] suitable
for WBANs are provided in Table VIII which are listed
as follows: UWB, ZigBee and IEEE 802.15.4, Bluetooth,
Bluetooth Low Energy and a few more leading competitors in
recent WBAN markets such as Z-Wave12, Zarlink13,RFID,
12http://www.z-wave.com
13http://www.zarlink.com/zarlink/hs/medical Wireless Telemetry.htm
Rubee14, ANT15 and Sensium16. An appropriate radio tech-
nology for WBANs can be decided upon based on the specific
requirements of a WBAN application. For instance, UWB
is considered most appropriate for short-range (5-10 m) and
high speed applications with high data rate requirements (110
- 480 Mbps). It has no restrictions on the frequency being
used as it has the dominant features of potentially high data
rates and low power consumption. On the other hand, the
Zarlink technology is most appropriate for medical implant
applications requiring low frequency and low data rates.
Rubee does not require line of sight communication for
its operation, which is quite advantageous compared to other
radio technologies provided in this literature. Additionally,
Rubee has the advantage of efficient transmission distance,
high security level, ultra-low power consumption, stable op-
eration providence and long battery lifetime, which makes
convenient for use in patient monitoring, storehouse manage-
ment, mobile health care, tracking purposes, management asset
and deployment in controls, sensors, actuators and indicators
[5, 106]. Moreover, due to Rubee’s low operation frequency it
will not be attenuated by metal or liquid and can be deployed
in any environment that RFID cannot handle [106, 107].
One significant disadvantage of Zigbee for WBAN appli-
cations is due to interference with WLAN transmission, spe-
cially in 2.4 GHz where numerous wireless systems operate.
Other deficiencies of Zigbee are related to its low data rate
(250 Kbps), which makes it inappropriate for large-scale and
real time WBAN applications [108] whilst appropriate for
deployment in home automation and control, and industrial
areas [109]. Whereas, Bluetooth LEE is more likely for use
in the deployment of low power devices around a human
body due to its lower cost and lower power consumption
(92 nJ/b). On the other hand, Bluetooth is advantageous
in supporting applications with different data rates, network
coverage and power requirements and most convenient for
short-term high data rate applications in which two peer to
peer devices are connected in an ad hoc configuration, such
as between two personal servers of two WBANs or between a
WBAN and a PC [5]. Thus, different operational environments
and characteristics of different WBAN applications require an
appropriate choice of the wireless radio technology being used
[107].
XII. COMPARISON WITH OTHER WIRELESS NETWORKS
Fig. 14 shows the realm of WBANs when compared with
other wireless networks such as Wireless Personal Area Net-
works (WPAN), Wireless Local Area Networks (WLAN),
Wireless Metropolitan Area Networks (WMAN) and Wireless
Wide Area Networks (WWAN). As shown in Fig. 14, wireless
networks can be categorized based on their geographical
coverage [10]. A WBAN operates close to the human body
with a restricted communication range of up to 1-2 meters.
It primarily deals with the interconnection of one’s wearable
devices, whereas a WPAN is a broader network environment
that surrounds the person. Even the WPAN communication
14www.rubee.com
15www.thisisant.com
16http://www.toumaz.com/page.php/page.sensium intro
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 21
TABLE VIII
CHARACTERISTICS OF WI RELESS TECHNOLOGIES USED I N WBANS
Technology Frequency Data Rate Coverage Modulation Network Topology
Bluetooth V.1 802.15.1 2.4 GHZ ISM 780 Kbps 10-150 m (on-body only) GFSK star
Bluetooth V.2 + Enhanced Data
Rate (EDR) 2.4 GHZ ISM 3 Mbps 10-100 m (on-body only) GFSK,PSK,8-
DPQSK,π/4DQPSK star
Bluetooth V3.0 + High Speed (HS) 2.4 GHZ ISM and 5 GHz 3-24 Mbps 10 m (on-body only) GFSK star
Bluetooth V4.0 + Low End Exten-
sion (LEE) 2.4 GHZ ISM 1 Mbps 10 m (on-body only) GFSK star
ZigBee (IEEE 802.15.4) 868 MHz, 915 MHz, 2.4
GHz ISM 20,40,250Kbps 10-100 m (on-body only) O-QPSK,BPSK(+
ASK)
star, mesh, cluster-
tree
Ultra Wideband (UWB) 3.1-10.6 GHz 110-480Mbps 5-10 m (on-body only) OFDM,DS-
UWB,BPSK,QPSK star
RFID (ISO/IEC 18000-6) 860 to 960 MHz 10 to 100Kbps 1 to 100 m FSK,PSK,ASK peer-to-peer
Near Field Communication (NFC) 13.56 MHz
106,212,424 Kbps
(1 Mbps planned
for future)
up to 20 cm ASK peer-to-peer
Sensium 868 MHz,915 MHz 50 Kbps 1-5 m (on-body only) BFSK star
Zarlink (ZL70101) 402-405MHz,433-434
MHz 200-800 Kbps 2 m (in-body only) 2FSK,4FSK peer to peer
RuBee (IEEE 1902.1) 131 KHz 9.6 Kbps 30 m ASK,BPSK,BMC peer-to-peer
Z-wave 900 MHz ISM 9.6 Kbps 30 m BFSK,FSK mesh
ANT 2.4 GHz ISM 1 Mbps 30 m (on-body only) GFSK star, mesh, peer to
peer, tree
Fig. 14. WBANs vs. Other Wireless Technologies
range reaches over several tens of meters for low data rate
applications and up to 10 meters for high data rate appli-
cations. However, previous WPANs do not fulfill medical
(within close vicinity of the human tissue) and communication
regulations for specific applications. Additionally, they do not
support the combination of data rate and reliability required
for the broad range of WBAN applications [4]. A WLAN has
a communication range of up to 100 meters. WWANs cover
the largest geographical region such as in mobile telephone
systems and satellite communication [110]. In summary, IEEE
802.15.6 overcomes the constraints of the aforementioned
wireless technologies as its focus is specifically on networking
within and around the body.
WBANs are considered to be a subset of WSNs or Wireless
Sensor or Actuator network (WSAN) [8]. However, they tackle
their own challenges related to the interaction of the human
body with different environments, and general challenges of
WSNs in human body monitoring. The entire WBAN network
is in motion due to postural body movements, the base-station
are mobile and weak, and coordination amongst WBANs is
impossible [14]. Since the base stations of WBANs are mobile;
they can move in and out of each others range, very quickly,
or may stay in each others range for long periods [4]. Table
IX provides an overview of challenges in WBAN compared
to WSNs, which is explained in more detail as follows [8, 13,
111]:
22 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
A. Network Structure
Nodes that comprise the topology of a WBAN are attached
to different parts of the body. Some parts of the body (e.g.
chest, waist) are stationary with respect to other parts (e.g.
arms, legs, head), which move given the body’s ambulatory
motion. The mixed motion of nodes differs from WSNs.
WBAN’s therefore need to be more robust and respond more
quickly to changes in their topology. In terms of network
dimensions, few to several thousands of nodes can be deployed
in WSNs over an area from meters to kilometers, dependent
on the mentioned environment, whereas WBANs have a dense
distribution of nodes limited by the human body size. Different
factors are considered by users in the choice of number of
actuator/sensor nodes to be deployed. Nodes in a WBAN are
usually either hidden under clothing or strategically placed
on the human body. Additionally, WBANs do not have any
redundant nodes. In fact, all devices have equal importance
and can be added when required to avoid different types of
failure.
B. Limitation of Resources
Communication capabilities, available memory and compu-
tational power is limited for nodes in WBANs, specifically
those implanted inside the body. Also recharging or changing
batteries in WBANs is not feasible even for devices that re-
quire a long lifetime. Each node has an extremely low transmit
power due to their very small form factor (normally less than
1cm3). These considerations aim to minimize interference as
well as coping with health concerns [8]. Additionally, the
implantable settings in WBANs causes inaccessibility and
replacement difficulties in providing battery and power supply.
However, the lifetime of nodes in both WBANs and WSNs is
application-specific.
C. Data Requirements
WBANs are deployed to register one’s physiological actions
and activities in a periodic manner resulting in constant data
rates, whereas WSNs are applied in event-based monitoring
that can happen in sporadic time intervals [5]. They are
usually heterogenous and therefore have different demands or
different resources regarding reliability, data rates and power
consumption [8]. In addition, latency is considered a key
requirement in WBAN applications. However, it can be traded
for improved energy consumption and reliability in WSNs.
Consequently, maximizing battery life-time at the expense of
higher latency may be necessary in WSNs [5].
D. Technological Requirements
The surrounding area of a human body is regarded as a
lossy medium for wave propagation. Consequently, signals in
WBAN devices are attenuated considerably before they reach
the receiver [8]. Also, WBANs have an ultra short communica-
tion range and a small scale structure [111]. Therefore, context
awareness is quite significant in WBANs due to their sensitive
content exchange whereas in WSNs, as their environment is
generally static, this is not a significant concern.
E. Security
In terms of security, the strictly confidential and private
character of medical data has to be ensured via stringent
security mechanisms required in WBANs as their devices
operate in hostile environments and collect life-critical in-
formation [63], whereas a lower level security is required
in WSNs which can be application-specific [8]. Also, the
limitation of communication and power efficiency in WBANs
leads to many security challenges compared to WSNs since
the integration of a high-level security mechanism in resource-
constraint and low-power sensor increases communication,
computational and management costs [63].
XIII. CHALLENGES AND OPEN ISSUES OF WBANS
The major challenges in the realization of WBANs are
summarized as follows:
1. Environmental Challenges – WBANs experience high
path loss due to body absorption that must be minimized
through heterogenous and multi-hop links with different types
of sensors at various locations. Additionally, change in op-
erational conditions may lead to error-prone and incomplete
sensor data relative to inherent sensor limitation, human
postures and motions, sensor breakdown and interference.
As health care facilities and human subjects have specific
regulations, the design of implants and wearable devices
becomes crucial. Channel models are far more complex due
to mobility and multi-path. Even more challenging issues rise
in terms of antenna design because of certain constraints of
WBANs in shape of antenna, material, size and malicious
RF environment. In essence, there are design restrictions in
size and shape relative to the organ and its location [35, 112–
114]. In fact, the location of the implant dictates its antenna
options. For instance, a urethra valve has to be replaced at
regular intervals without surgery. The length is restricted and
its available diameter is 4mm to 6mm; which means a path
antenna cannot be used and maintaining a dipole or monopole
antenna would be quite difficult. The best solution would be
a helical antenna integrated into the shape of a valve implant
[115]. Additionally, as implants can only use bio-compatible
and non-corrosive material like titanium and platinum, their
antennas are not as strong as a copper antenna.
2. Physical Layer Challenges – PHY layer protocols
should be designed to minimize power consumption without
compromising reliability. PHY protocols should be convenient
for interference-agile places where high-power devices use
the unlicensed bands. One important metric in the choice of
an appropriate wireless technology for WBANs is related to
its power usage, which is undoubtedly tied with the design
of a power efficient WBAN. Current wireless technologies
have a high peak current and mainly minimize the average
current drawn by duty cycling the radio between active
and sleep (standby) modes. Hence, further improvements in
radio hardware, sensing technologies, integrated circuits and
miniaturization is required to dramatically decrease the peak
current drawn [9]. Advancements in low power RF technology
is expected to significantly lower the peak power consumption,
which leads to the production of small, disposable and low
cost patches. Technically, WBANs are required to be scalable
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 23
TAB L E I X
COMPARISON OF WBANS AND WSNS
Comparison criteria Wireless Sensor Network Wireless Body Area Networks
Network Dimensions Few to several thousand nodes over an area from meters to kilometers Dense distribution limited by body size
Topology Random, Fixed/Static One-hop or two-hop star topology
Node Size Small size preferred (no major limitation in most cases) Miniaturization required
Node Accuracy Accuracy outweighs large number of nodes and allows for result
validation Each of the nodes have to be accurate and robust
Node Replacement Easily performed (some nodes are disposable) Difficultly in replacement of implanted nodes
Bio-compatibility Not a concern in most applications Essential for implants and some external sensors
Power Supply and Battery Accessible, Capable of changing more frequently and easily Difficultly in replacement and accessibility of implanted settings
Node Lifetime Several years / months / weeks (application-dependant) Several years / months (application-dependant)
Power Demand Power is more easily supplied, hence apparently greater Energy is supplied more difficultly hence apparently lower
Energy Scavenging Wind and Solar power are most apparent candidates Thermal (body heat) and Motion are most apparent candidates
Data Rate More frequently homogenous More frequently heterogenous
Data Loss Impact Data loss over wireless transfer is compensated by the large number
of nodes
Data loss is considered more significant (may need additional measures
to ensure real time data interrogation capabilities and QoS)
Security Level Lower (application-dependant) Higher security level to protect patient information
Traffic Application specific, Modest data rate, Cyclic/sporadic Application specific, Modest data rate, Cyclic/sporadic
Wireless Technology WLAN, GPRS, Zigbee , Bluetooth and RF 802.15.6, ZigBee, Bluetooth, UWB.
Context Awareness Insignificant with static sensors in a well defined environment Very significant due to sensitive context exchange of body physiology
Overall Design Goals Self-operability, Cost optimization, Energy Efficiency Energy Efficiency, Eliminate electromagnetic exposure
and have peak power consumption between 0.001-0.1mW in
stand-by mode and up to 30mW in fully active mode [9].
Interference is also known as one of the crucial drawbacks
of WBAN systems. On one hand, interference occurs in
cases where several people that are wearing WBAN devices
and step into each others range, which makes coordination
impossible (off-body interference). Also, importantly, by 2014
there will be 420 million WBAN devices sold, and this
figure will continue to rise. The coexistence issues becomes
more prominent with higher WBAN density. Furthermore,
the value of employing transmit power control, in terms
of minimizing interference and saving power consumption
as well as increasing WBAN node battery lifetime should
be given more thought. Additionally, since postural body
movements are unpredictable, networks can easily move in
and move out of each others range [42]. This issue becomes
more dominant in the use of wireless technologies with higher
coverage area. On-body devices deployed in one WBAN may
also have interference with one another (on-body interference).
Off-body interference may also occur due to collision from
external sensors [53].
3. MAC Layer Challenges – The mechanisms given in
IEEE 802.15.6 do not build up a complete MAC protocol. In
fact, only the basic requirements for ensuring interoperability
amongst IEEE 802.15.6 devices have been outlined in terms
of message exchange protocols and packet formats whilst
further research questions have not been stated. For one thing,
density and topology changes relative to body movements
resulting in nodes moving into or out of coverage should be
considered in the MAC protocol design. The MAC protocol
should be robust enough to support multiple WBANs in
parallel applications. Thus, reliability is of major importance
in such networks. In this regard, the IEEE 802.15.6 standard
has allowed the deployment of dynamic channel hopping,
which assists the network to minimize interference from other
narrowband transmitters. Additionally, the proposed standard
aims to eliminate interference by shifting beacon transmission
via a known offset at each beacon period. Also, in cases where
the required levels of reliability cannot be achieved through
a one-hop star topology, the use of relays is allowed [42]. In
addition, MAC protocols must support the energy efficiency
requirement of WBAN applications, prolong sensor lifetime,
allow flexible duty cycling and save energy by periodically
switching the radio on/off. Channel polling must be used to
check if the nodes are awake to transmit/receive instead of
idle listening. Nodes with low duty cycle should not receive
frequent control packets and synchronization if they do not
intend to send or receive data. However, the MAC protocols
proposed thus far for WBANs do not provide efficient network
throughput and delay performance at varying traffic and the
synchronization of duty cycles of their sensors with variant
traffic characteristics and power requirements remains a chal-
lenge [5].
WBANs also have specific QoS requirements which need
to be met by the MAC proposal. For this purpose, the high
sampling rate from sensors in WBANs needs to be handled
by allowing data to be sent out as soon as possible or
to transmit data packets with the earliest deadline [5]. For
instance, in emergency applications the MAC protocol must
allow quick access of its nodes to the channel to transmit life-
critical data to the coordinator. MAC proposals for WBANs
are either contention-based such as Carrier Sense Multiple
Access/Collision Avoidance (CSMA/CA) or scheduled-based
such as TDMA. The contention-based protocols do not have
the constraint of strict time synchronization. However, they
incur heavy collision for high traffic nodes. On the other
hand, the TDMA MAC proposals are energy efficient, reduce
the duty cycle and do not incur overhearing, contention or
idle listening. However, their periodic time synchronization
requires extra energy [16]. Movassaghi et al. [116] have
proposed a practical energy efficient network coding approach
for WBANs using decode and forward, namely decode and
forward network coding (DF-NC) relays. This approach com-
bines messages from various sources at the relays to create one
message which is then transmitted to the destination. Thus,
only one transmission slot is required for transmission and
near optimal outage probability is achieved while minimizing
the number of transmissions per node, maximizing the energy
efficiency of WBANs, and minimizing the delay. In [117], the
use of a novel cooperative transmission scheme via network
24 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
TAB L E X
EXISTING PROJECTS ON WIRELESS BODY AREA NETWORKS
Project Target Application Intra-BAN Comm. Inter-BAN Comm. Beyond BAN Comm. Sensors
Mobi-
Health[118]
Ambulatory Patient
Monitoring Manually ZigBee/Bluetooth GPRS/UMTS ECG, Heart rate, Blood Pressure
AID-N[119] Mass Casualty Incident Wired Mesh/ZigBee WiFi/Internet/ Cellular
Networks Blood, Pulse, ECG, Temperature
MAHS[120] Health Care Bluetooth Wireless Network Internet Spirometer, Pulse, Temperature, Pressure
CodeBlue[121] Medical Care Wire d ZigBee/Mesh N/A Motion, EKG, Pulse Oximeter
LifeMinder[122] Real time daily self-
care Bluetooth Bluetooth Internet Galvanic Skin Reflex (GSR) Electrodes,
Pulse Meter, Thermometer, Accelerometer
SMART[123] Health Monitoring in
Waiting Room Wir ed 802.11.b N/A SpO2sensor, ECG
Tele-medicare17 Home-based Care and
Medical Treatment Bluetooth Internet Internet Temperature,ECG, Oximeter, Blood pres-
sure
CareNet[124] Remote Health Care N/A ZigBee Internet/Multi-hop 802.11 Gyroscope, Tri-axial accelerometer
ASNET[125] Remote Health Moni-
toring
Wired or Wireless In-
terface (WiFi) WiFi/Ethernet Internet/GSM Temperature, Blood Pressure
IBBT IM318 Telecare and
Telemedicine Services N/A N/A Internet Respiration, ECG, Heart rate
MITHril19 Health Care Wired WiFi N/A EKG, ECG
BASUMA [126] Health Monitoring UWB N/A N/A ECG, Reactive Oxygen Sensor (ROS),
SpO2Sensor, Spirometer
WHMS [1] Health Care Wire d WiFi N/A EKG, ECG
HUMAN++ [127]
Sport, Entertainment,
Medical, Assisted
Living, Lifestyle
UWB N/A N/A ECG, EMG, EEG
WiMoCA [128] Sport/Gesture
Detection
Star Topology and
Time table-based
MAC protocol
Bluetooth WiFi/Internet/ Cellular
Networks/Bluetooth Tri-axial Accelerometer
AYUSHMAN
[129] Health Monitoring ZigBee 802.11 Internet
EKG, Blood pressure , Oximeter, Gyro-
scopic sensors, Accelerometer, Gait mon-
itoring sensors
MIMOSA [130] Ambient Intelligence RFID/Bluetooth/
Wibre e UMTS/GPRS Internet RFID sensors, Any sensors
UbiMon20 Health Care ZigBee WiFi/GPRS WiFi/GPRS 3Leads ECG, 2Leads ECG strip, SpO2
LifeGUARD21
Ambulatory
physiologic monitoring
for space and
terrestrial applications
Wired Bluetooth/Internet Bluetooth/Internet
ECG, Respiration Electrodes, Pulse Oxime-
ter, Blood Temperature, Built-in Ac-
celerometer
HealthService2422 Mobile Health Care Wir ed UMTS/GPRS UMTS/GPRS/ Internet
ECG, EMG, SpO2,Pulserate,Respira-
tion, Skin temperature, Activity, Plethys-
mogram
coding, namely Random XOR Network Coding (RXNC), has
been proposed for Wireless Body Area Networks (WBANs)
to enhance reliability and throughput. In this approach, each
relay demodulates the received signal from each sensor node
and then selects a number different coded symbols amongst
them and XORs them to generate a network coded symbol.
The optimum number of coded symbols is calculated through
an analytical approach by minimizing the probability that an
XOR network coded symbol is incorrectly generated. The
proposed RXNC scheme has shown to outperform the no-
cooperation and conventional bitwise network coding schemes
in all channel signal to noise ratios (SNRs) from 0 dB to 18
dB.
4. Security Challenges – Due to limitation of resources
in terms of energy, memory, processing power and lack
of user interface existing security mechanisms proposed for
other communication networks are not applicable to WBANs
and more resource-efficient and lightweight security protocols
need to be developed. As an example, an adversary could
be capable of inducing heart failure by the detection and
17http://www.ist-world.org/ ProjectDetails.aspx/ProjectId=07a46fe732b64
e07a622e8e5 ecde2874
18http://projects.ibbt.be/im3
19http://www.media.mit.edu/wearables/mithril
20http://www.ubimon.net
21http://actuators.stanford.edu/Archive/CPOD.htm
22http://www.healthservice24.com
execution of vulnerabilities in an Implantable Cardioverter
Defibrillator (ICD). Moreover, numerous non-technical factors
are trivial to mass marketing in WBANs such as acceptance,
comfort, user friendliness, regulatory, affordability, regulatory,
ethical and legal issues [5]. Further detail about the security
challenges of WBANs can be found in Section VIII.
5. Transport (QoS) Challenges – The QoS requirements of
applications in WBANs must be met without degrading perfor-
mance and improving complexity. In addition, real time life-
critical WBAN applications are both loss-sensitive and delay-
sensitive. Therefore, the limited memory in WBANs requires
efficient acknowledgement, retransmission, secure correction
and error detection strategies. Also, a WBAN deployed on
one human body may be used to provide different applications
with different requirements in data rates, frequency, reliability
and power usage. Hence, WBANs have to ensure consistent
data transfer amongst the different wireless technologies being
used to be scalable, promote information exchange, interact
plug and play devices, provide uninterrupted connectivity and
ensure efficient migration across networks. Additionally, de-
vices in a WBAN may have different frequency, data rate and
power requirements. Hence, the chosen wireless technology
must be capable of handling a mixture of these requirements.
In terms of QoS, episodic data, real time wave form data,
periodic parametric data and emergency alarms need to be
supported with a BER of 10−10 to 10−3and peer to peer
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 25
latency from 10ms-250ms [9]. Hence, QoS features such
as bandwidth, reliability, delay, etc., require comprehensive
study. The desired QoS could also affect energy consumption,
which is one of the prominent requirements in WBANs. For
instance, to achieve a lower packet loss, the transmit power
should be increased, which also increases the relative power
consumption.
In this section we have summarized the main pitfalls that
can be experienced in WBANs. For one thing, Rayleigh
distribution is a very poor fit for small-scale fading compared
to the other standard distributions. Generally, Rayleigh fading
provides a good fit when multipath in the radio channel is
additive in the linear domain. Therefore, other radio networks
avoid the combination of multipaths that occur in WBANs;
which is normally additive in the log domain or multiplicative
and are mainly dominated by the shadowing. Thus, in fact
the best fit for a small-scale fading is gamma distribution.
Additionally, a distance-based path loss model provides poor
measurements in terms of the received signal strength for a
link. In fact, the environment and location are more dominant
factors than distance. More specifically, the position of a
transmitter with respect to a receiver is important in terms of
path loss but the position in relevance to body posture, body
movements, shadowing as well as the surrounding environ-
ment are more important. One other pitfall that most WBAN
researchers need to be aware of is that the characterization
of an on-body link as LOS or NLOS is not practical or
meaningful as the signal states vary as much from NLOS
to LOS because of dynamics, changes in posture and body
movements. Whilst the rate of movement needs to be captured
to statistically characterize the path loss of the link [24].
Another major pitfall is the danger of relying on a one-
hop star topology as such a network cannot be sufficiently
reliable for WBAN communications specifically in health
care applications that require the use of relays and cooper-
ative communication for reliable communication. The one-
hop star topology was only desired in the initial stages of
IEEE 802.15.6, however since it did not meet the reasonable
reliability requirement of WBANs, multi-hop and coopera-
tive communication were later added as an option to IEEE
802.15.6. Based on the standard, only one relay can be added
to a single WBAN. Relays can be more complex and powerful
devices compared to a typical WBAN as relays have greater
energy consumption. The necessity of using relays can be
further realized in a scenario where a patient is sleeping as
the WBAN channel will be quasi-stationary [131].
Table X provides a brief comparison of current and on-
going WBAN projects. Important aspects considered in these
projects are listed in Table X in terms of their actual appli-
cation, intra-WBAN communication, inter-WBAN communi-
cation, beyond-WBAN communication and the sensors being
used.
In summary, even though WBANs will provide major en-
hancements in human life style through the use of ubiquitous
networking, various challenges remain in this area that need
to be taken into account before being widely deployed such
as interoperability of WBANs and other wireless technologies,
energy efficient and high bandwidth communication protocols,
privacy and security, biosensor design, QoS, power scavenging
issues, mobility and scalability, standardization of interfaces
and design of successful applications [1, 5, 11, 110].
XIV. CONCLUSION
In this survey, a review of the on-going research in WBANs
in terms of system architecture, address allocation, routing,
channel modeling, PHY layer, MAC layer, security and appli-
cations is provided. A comparison of WBANs with respect to
WSNs and other wireless technologies is given. Additionally,
a list of existing and applicable sensors, radio technologies
and current research projects, open issues, and future work in
WBANs is also presented. WBANs will allow for continuous
monitoring of patients in medical applications, capable of
early detection of abnormal conditions resulting in major
improvements in the quality of life. Importantly, even basic
vital signs monitoring (e.g. heart rate) can enable patients
to engage in normal activities as opposed to being home
bound or nearby specialized medical services. In summary, the
procedural research on this valuable technology has significant
importance in better usage of available resources that will no
doubt truly affect our future well being. We truly believe
this research to be a source of inspiration towards future
developments in WBANs.
ACKNOWLEDGMENT
The authors would like to thank Mahyar Shirvanimoghad-
dam from The University of Sydney for his assistance.
REFERENCES
[1] A. Milenkovic, C. Otto, and E. Jovanov, “Wireless sensor networks for
personal health monitoring: Issues and an implementation,” Computer
Communications (Special issue: Wireless Sensor Networks: Perfor-
mance, Reliability, Security, and Beyond, vol. 29, pp. 2521–2533, 2006.
[2] C. Otto, A. Milenkovic’, C. Sanders, and E. Jovanov, “System archi-
tecture of a wireless body area sensor network for ubiquitous health
monitoring,” J. Mob. Multimed., vol. 1, pp. 307–326, Jan. 2005.
[3] “IEEE standard for local and metropolitan area networks: Part 15.6:
Wireless body area networks,” IEEE submission,Feb.2012.
[4] D. Smith and L. Hanlen, “Wireless body area networks : Towards a
wearable intranet,” ISCIT Tutorial, Sept. 2012.
[5] M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, and V. Leung, “Body
area networks: A survey,” Mobile Networks and Applications,vol.16,
pp. 171–193, 2011.
[6] S.Ullah,B.Shen,S.M.R.Islam,P.Khan,S.Saleem,andK.S.Kwak,
“A study of medium access control protocols for wireless body area
networks,” arXiv preprint arXiv:1004.3890, 2010.
[7] H. Kwon and S. Lee, “Energy-efficient multi-hop transmission in body
area networks,” in 20th IEEE Int. Symp. on Personal, Indoor and
Mobile Radio Commun. (PIMRC), pp. 2142 –2146, Sept. 2009.
[8] B. Latr´e, B. Braem, I. Moerman, C. Blondia, and P. Demeester, “A
survey on wireless body area networks,” Wirless Network, vol. 17,
pp. 1–18, Jan. 2011.
[9] M. Hanson, H. Powell, A. Barth, K. Ringgenberg, B. Calhoun, J. Aylor,
and J. Lach, “Body area sensor networks: Challenges and opportuni-
ties,” Computer, vol. 42, pp. 58 –65, Jan. 2009.
[10] K. Kwak, S. Ullah, and N. Ullah, “An overview of IEEE 802.15.6
standard,” in 3rd Int. Symp. on Applied Sciences in Biomedical and
Communication Technologies (ISABEL), pp. 1 –6, Nov. 2010.
[11] S. Ullah, H. Higgin, M. A. Siddiqui, and K. S. Kwak, “A study of
implanted and wearable body sensor networks,” in Proc. 2nd KES Int.
Conf. on Agent and multi-agent systems: technologies and applications,
(Berlin, Heidelberg), pp. 464–473, Springer-Verlag, 2008.
[12] E. Dishman, “Inventing wellness systems for aging in place,” Com-
puter, vol. 37, pp. 34 – 41, May. 2004.
[13] J. Xing and Y. Zhu, “A survey on body area network,” in 5th Int.
Conf.on Wireless Communications, Networking and Mobile Computing
(WiCom ’09), pp. 1 –4, Sept. 2009.
26 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
[14] B. Wang and Y. Pei, “Body area networks,” Encyclopedia of Wire-
less and Mobile Communications,Edited by Borko Furht, Taylor and
Francis, vol. 98, 2007.
[15] S. Ullah, P. Khan, N. Ullah, S. Saleem, H. Higgins, and K. Kwak, “A
review of wireless body area networks for medical applications,” arXiv
preprint arXiv:1001.0831, vol. abs/1001.0831, 2010.
[16] S. Ullah, H. Higgins, B. Braem, B. Latre, C. Blondia, I. Moerman,
S. Saleem, Z. Rahman, and K. Kwak, “A comprehensive survey of
wireless body area networks,” J. of Medical Systems, pp. 1–30, 2010.
[17] D. Lewis, “802.15.6 call for applications in body area networks-
response summary,” in 15-08-0407-05-0006, Nov. 2008.
[18] N. de Vicq, F. Robert, J. Penders, B. Gyselinckx, and T. Torfs,
“Wireless body area network for sleep staging,” in IEEE Biomedical
Circuits and Systems Conf. (BIOCAS 2007), pp. 163–166, 2007.
[19] M. Lipprandt, M. Eichelberg, W. Thronicke, J. Kruger, I. Druke,
D. Willemsen, C. Busch, C. Fiehe, E. Zeeb, and A. Hein, “Osami-
d: An open service platform for healthcare monitoring applications,”
in 2nd Conf. on Human System Interactions (HSI’09), pp. 139–145,
IEEE, 2009.
[20] J. Nehmer, M. Becker, A. Karshmer, and R. Lamm, “Living assistance
systems: an ambient intelligence approach,” in Proc. 28th Int. Conf.
on Software engineering, pp. 43–50, ACM, 2006.
[21] C. Tachtatzis, F. Franco, D. Tracey, N. Timmons, and J. Morrison, “An
energy analysis of IEEE 802.15.6 scheduled access modes,” in IEEE
GLOBECOM Workshops (GC Wkshps), pp. 1270 –1275, Dec. 2010.
[22] A. W. Astrin, H.-B. Li, and R. Kohno, “Standardization for body area
networks,” IEICE Trans. Commun., vol. no. 2, pp. 366–72, 2009.
[23] D. Lewis, “802.15.6 call for applications-response summary,” in 15-
08-0407-00-0006-tg6-applications-summary.doc.
[24] D. Smith, D. Miniutti, T. A. Lamahewa, and L. Hanlen, “Propagation
models for body area networks: A survey and new outlook,” to appear
in IEEE Antennas and Propagation Mag., Dec. 2013.
[25] J. Y. Khan, M. R. Yuce, G. Bulger, and B. Harding, “Wireless body
area network (wban) design techniques and performance evaluation,”
J. of medical systems, vol. 36, no. 3, pp. 1441–1457, 2012.
[26] B. Zhen, M. Patel, S. Lee, E. Won, and A. Astrin, “Tg6 technical
requirements document (TRD) IEEE p802.15-08-0644-09-0006.,” Sept.
2008.
[27] S. Wang and J.-T. Park, “Modeling and analysis of multi-type failures
in wireless body area networks with semi-markov model,” Comm.
Letters., vol. 14, pp. 6–8, Jan. 2010.
[28] K. Y. Yazdandoost and K. Sayrafian-Pour, “Channel model for body
area network (BAN),” Networks, p. 91, 2009.
[29] “IEEE p802.15-07–0867-04-0ban,” in 15-10-0245-06-0006, Oct. 2007.
[30] “IEEE p802.15.6/d0 draft standard for body area network,” IEEE Draft,
2010.
[31] D. Lewis, “IEEE p802.15.6/d0 draft standard for body area network,”
in 15-10-0245-06-0006, May. 2010.
[32] “IEEE p802. 15-10 wireless personal area networks,,” July, 2011.
[33] A. Zhang, D. Smith, D. Miniutti, L. Hanlen, D. Rodda, and B. Gilbert,
“Performance of piconet co-existence schemes in wireless body area
networks,” in IEEE Wireless Commun. and Netw. Conf. (WCNC), pp. 1
–6, April. 2010.
[34] L. Hanlen, D. Miniutti, D. B. Smith, D. Rodda, and B. Gilbert, “Co-
channel interference in body area networks with indoor measurements
at 2.4 GHz: Distance-to-interferer is a poor estimate of received
interference power.,” IJWIN, vol. 17, no. 3-4, pp. 113–125, 2010.
[35] M. Patel and J. Wang, “Applications, challenges, and prospective
in emerging body area networking technologies,” Wireless Commun.,
vol. 17, pp. 80–88, Feb. 2010.
[36] T. Zasowski, F. Althaus, M. Stager, A. Wittneben, and G. Troster,
“Uwb for noninvasive wireless body area networks: channel measure-
ments and results,” in IEEE Conf. on Ultra Wideband Systems and
Technologies, pp. 285 – 289, Nov. 2003.
[37] R. Shah and M. Yarvis, “Characteristics of on-body 802.15.4 net-
works,” in 2nd IEEE Workshop on Wireless Mesh Networks (WiMesh),
pp. 138 –139, Sept. 2006.
[38] M. Sukor, S. Ariffin, N. Fisal, S. S. Yusof, and A. Abdallah, “Perfor-
mance study of wireless body area network in medical environment,”
Asia Int. Conf. on Modelling & Simulation, pp. 202–206, 2008.
[39] “IEEE standard for information technology- telecommunications and
information exchange between systems- local and metropolitan area
networks- specific requirements part 15.4: Wireless medium access
control (MAC) and physical layer (PHY) specifications for low-rate
wireless personal area networks (WPANs),” IEEE Std 802.15.4-2006
(Revision of IEEE Std 802.15.4-2003), pp. 0 1 –305, 2006.
[40] A. Natarajan, M. Motani, B. Silva, K. Yap, and K. Chua, “Investigating
network architectures for body sensor networks,” in Proc. 1st ACM
SIGMOBILE Int. Workshop on Systems and networking support for
healthcare and assisted living environments, (New York, NY, USA),
pp. 19–24, ACM, 2007.
[41] D. Domenicali and M.-G. Di Benedetto, “Performance analysis for
a body area network composed of IEEE 802.15.4a devices,” in 4th
Workshop on Positioning, Navigation and Communication (WPNC
’07), pp. 273 –276, Mar. 2007.
[42] A. Boulis, D. Smith, D. Miniutti, L. Libman, and Y. Tselishchev,
“Challenges in body area networks for healthcare: The mac,” IEEE
Commun. Mag., May. 2012.
[43] M. R. Yuce, S. W. Ng, N. L. Myo, C. K. Lee, J. Y. Khan, and W. Liu,
“A mics band wireless body sensor network,” in Wireless Commun.
and Netw. Conf. (WCNC), pp. 2473–2478, IEEE, 2007.
[44] I. Khan, Y. Nechayev, and P. Hall, “On-body diversity channel char-
acterization,” IEEE Trans. Antennas Propag., vol. 58, pp. 573 –580,
Feb. 2010.
[45] A. Guraliuc, A. Serra, P. Nepa, and G. Manara, “Channel model for on
body communication along and around the human torso at 2.4GHz and
5.8GHz,” in Int. Workshop on Antenna Technology (iWAT), pp. 1–4,
Mar. 2010.
[46] Y. Hao, “Antennas and propagation for body centric wireless com-
munications,” in 2011 IEEE Int. Conf. on Microwave Technology
Computational Electromagnetics (ICMTCE), May 2011.
[47] E. Reusens, W. Joseph, B. Latre, B. Braem, G. Vermeeren, E. Tanghe,
L. Martens, I. Moerman, and C. Blondia, “Characterization of on-
body communication channel and energy efficient topology design
for wireless body area networks,” IEEE Trans. Inf. Technol. Biomed.,
vol. 13, pp. 933 –945, Nov. 2009.
[48] S. L. Cotton, W. G. Scanlon, and G. Jim, “The κ-μdistribution applied
to the analysis of fading in body to body communication channels for
fire and rescue personnel,”
[49] D. B. Smith, L. W. Hanlen, J. A. Zhang, D. Miniutti, D. Rodda, and
B. Gilbert, “Characterization of the dynamic narrowband on-body to
off-body area channel,” in IEEE Int. Conf. Commun. ICC, pp. 1–6,
2009.
[50] W. G. Scanlon and S. L. Cotton, “Understanding on-body fading
channel at 2.45 GHz using measurements based on user state and en-
vironment,” in Loughborough Antennas and Propagation Conference,
Loughborough, UK, pp. 10–13, 2008.
[51] P. W. A. Fort, C. Desset and L. Biesen, “Indoor body-area channel
model for narrowband communications,,” IET Microwaves, Antennas
and Propagation, pp. 1197–1203, December 2007.
[52] D. B. Smith, L. W. Hanlen, D. Miniutti, J. A. Zhang, D. Rodda, and
B. Gilbert, “Statistical characterization of the dynamic narrowband
body area channel,,” in Int. Symp. on Applied Sciences in Bio-Medical
and Communication Technologies,, pp. 1–5, Oct. 2008.
[53] M. Yuce and J. Khan, Wireless Body Area Networks: Technology,
Implementation, and Applications. Pan Stanford Publishing, 2011.
[54] W.-B. Yang and K. Sayrafian-Pour, “Interference mitigation for body
area networks,” in 22nd Int. Symp. on Personal Indoor and Mobile
Radio Communications (PIMRC), pp. 2193–2197, IEEE, 2011.
[55] B. de Silva, A. Natarajan, and M. Motani, “Inter-user interference in
body sensor networks: Preliminary investigation and an infrastructure-
based solution,” in 6th Int. Workshop on Wearable and Implantable
Body Sensor Networks (BSN), pp. 35–40, IEEE, 2009.
[56] J. Dong and D. Smith, “Cooperative body-area-communications: En-
hancing coexistence without coordination between networks,” in IEEE
23rd Int. Symp. Personal Indoor and Mobile Radio Commun. (PIMRC),
pp. 2269–2274, IEEE, 2012.
[57] S. Marinkovic, E. Popovici, C. Spagnol, S. Faul, and W. Marnane,
“Energy-efficient low duty cycle MAC protocol for wireless body area
networks,” IEEE Trans. Inf. Technol. Biomed., vol. 13, pp. 915 –925,
Nov. 2009.
[58] A. Arriola, J. Sancho, S. Brebels, M. Gonzalez, and W. De Raedt,
“Stretchable dipole antenna for body area networks at 2.45 ghz,”
Microwaves, Antennas Propagation, IET, vol. 5, 13 2011.
[59] J. Kim and Y. Rahmat-Samii, “Implanted antennas inside a human
body: simulations, designs, and characterizations,” IEEE Trans. Mi-
crow. Theory Tech., vol. 52, pp. 1934 – 1943, Aug. 2004.
[60] L. Hanlen, D. Smith, A. Boulis, B. Gilbert, V. Chaganti, L. Craven,
D. Fang, T. Lamahewa, D. Lewis, D. Miniutti, O. Nagy, D. Rodda,
K. Sithamparanathan, Y. Tselishchev, and A. Zhang, “Wireless body-
area-networks : toward a wearable intranet,” in National ICT Australia,
2011.
[61] M. Kamarudin, Y. Nechayev, and P. Hall, “Performance of antennas in
the on-body environment,” in IEEE Antennas and Propagation Society
Int. Symp., vol. 3A, pp. 475 – 478, July. 2005.
[62] Z. Hu, M. Gallo, Q. Bai, Y. Nechayev, P. Hall, and M. Bozzettit,
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 27
“Measurements and simulations for on-body antenna design and prop-
agation studies,” in 2nd European Conf. on Antennas and Propagation
(EuCAP), pp. 1–7, Nov. 2007.
[63] S. Saleem, S. Ullah, and H. S. Yoo, “On the security issues in wireless
body area networks,” JDCTA, vol. 3, no. 3, pp. 178–184, 2009.
[64] “IEEE p802.15 working group for wireless personal area net-
works (WPANs):medwin MAC and security proposal documentation,”
IEEE802.15.6 technical contribution, 2009.
[65] M. Mana, M. Feham, and B. A. Bensaber, “Trust key management
scheme for wireless body area networks.,” Int. J. Netw. Security, 2011.
[66] C. Poon, Y.-T. Zhang, and S.-D. Bao, “A novel biometrics method to
secure wireless body area sensor networks for telemedicine and m-
health,” IEEE Commun. Mag., vol. 44, pp. 73 – 81, April. 2006.
[67] S. Cherukuri, K. Venkatasubramanian, and S. Gupta, “Biosec: a
biometric based approach for securing communication in wireless
networks of biosensors implanted in the human body,” in Proc. Int.
Conf. on Parallel Processing Workshops, pp. 432 – 439, Oct. 2003.
[68] H. Wang, H. Fang, L. Xing, and M. Chen, “An integrated biometric-
based security framework using wavelet-domain hmm in wireless body
area networks (wban),” in IEEE Int. Conf. on Commun. (ICC), pp. 1
–5, June. 2011.
[69] A. Banerjee, K. Venkatasubramanian, and S. Gupta, “Challenges of
implementing cyber-physical security solutions in body area networks,”
in Proc. of 4th Int. Conf. on Body Area Networks, 2009.
[70] S.-D. Bao and Y.-T. Zhang, “A design proposal of security architecture
for medical body sensor networks,” in Int. Workshop on Wearable and
Implantable Body Sensor Networks (BSN), April. 2006.
[71] L. W. Hanlen, D. Smith, J. A. Zhang, and D. Lewis, “Key-sharing via
channel randomness in narrowband body area networks: Is everyday
movement sufficient?,” in Proc. 4th Int. Conf. on Body Area Networks,
BodyNets ’09, pp. 17:1–17:6, ICST (Institute for Computer Sciences,
Social-Informatics and Telecommunications Engineering), 2009.
[72] M. Li, S. Yu, W. Lou, and K. Ren, “Group device pairing based secure
sensor association and key management for body area networks,” in
IEEE INFOCOM Proc., pp. 1 –9, Mar. 2010.
[73] S. L. Keoh, E. C. Lupu, and M. Sloman, “Securing body sensor
networks: Sensor association and key management,” in Per Com , IEEE
Computer Society, 2009.
[74] M. Abolhasan, T. Wysocki, and E. Dutkiewicz, “A review of routing
protocols for mobile ad hoc networks,” Ad Hoc Networks, vol. 2, pp. 1–
22, Jan. 2004.
[75] K. Akkaya and M. Younis, “A survey on routing protocols for wireless
sensor networks,” Ad Hoc Networks, vol. 3, pp. 325–349, 2005.
[76] S. Cheng and C. Huang, “Coloring-based inter-wban scheduling for
mobile wireless body area network,” IEEE Trans. Parallel Distrib.
Syst..
[77] A. Maskooki, C. B. Soh, E. Gunawan, and K. S. Low, “Opportunistic
routing for body area network,” in IEEE Consumer Commun. and Netw.
Conf. (CCNC), pp. 237 –241, Jan. 2011.
[78] M. Quwaider and S. Biswas, “DTN routing in body sensor networks
with dynamic postural partitioning,” Ad Hoc Networks, vol. 8, no. 8,
pp. 824–841, 2010.
[79] D. Takahashi, Y. Xiao, F. Hu, J. Chen, and Y. Sun, “Temperature-aware
routing for telemedicine applications in embedded biomedical sensor
networks,” EURASIP J. of Wireless Commun. Netw., Jan. 2008.
[80] B. Braem, B. Latr ´e, I. Moerman, C. Blondia, and P. Demeester, “The
wireless autonomous spanning tree protocol for multihop wireless body
area networks,” In proc. 3rd Annu. Int. Conf. on Mobile and Ubiquitous
Systems: Networking Services, pp. 1 –8, July 2006.
[81] T. Watteyne, I. Auge-Blum, M. Dohler, and D. Barthel, “Anybody: a
self-organization protocol for body area networks,” in 2nd Int. Conf.
on Body Area Networks (BodyNets), vol. 5, (Florence, Italy), pp. 8020–
8024, June. 2007.
[82] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-
efficient communication protocol for wireless microsensor networks,”
in Proc. 33rd Annu. Hawaii Int. Conf. on System Sciences,vol.5,
pp. 8020–8024, Jan. 2000.
[83] M. Moh, B. J. Culpepper, L. Dung, T.-S. Moh, T. Hamada, and C.-
F. Su, “On data gathering protocols for in-body biomedical sensor
networks,” in Proc. of Global Telecommunications Conf..
[84] S. Movassaghi, M. Abolhasan, and J. Lipman, “Energy efficient
thermal and power aware (ETPA) routing in body area networks,” in
23rd IEEE Int. Symp. on Personal Indoor and Mobile Radio Commun.
(PIMRC), Sept. 2012.
[85] X. Liang, X. Li, Q. Shen, R. Lu, X. Lin, X. Shen, and W. Zhang,
“Exploiting Prediction to Enable Secure and Reliable Routing in
Wireless Body Area Networks,” in 31st IEEE Int. Conf. on Computer
Commun. (INFOCOM), (Orlando, United States), 2012.
[86] M. Quwaider and S. Biswas, “Probabilistic routing in on-body sensor
networks with postural disconnections,” Proc. 7th ACM Int. Symp. on
Mobility management and wireless access (MobiWAC), pp. 149–158,
2009.
[87] M. Quwaider and S. Biswas, “On-body packet routing algorithms for
body sensor networks,” 1st Int. Conf. on Networks and Commun.
(NETCOM ’09), pp. 171 –177, Dec. 2009.
[88] B. Braem, B. Latr ´e, C. Blondia, I. Moerman, and P. Demeester,
“Improving reliability in multi-hop body sensor networks,” Proc. 2008
2nd Int. Conf. on Sensor Technologies and Applications, pp. 342–347,
2008.
[89] A. G. Ruzzelli, R. Jurdak, G. M. O’Hare, and P. Van Der Stok,
“Energy-efficient multi-hop medical sensor networking,” Proc. 1st
ACM SIGMOBILE Int. Workshop on Systems and networking support
for healthcare and assisted living environments (HealthNet), pp. 37–42,
2007.
[90] A. Bag and M. A. Bassiouni, “Biocomm - a cross-layer medium
access control (mac) and routing protocol co-design for biomedical
sensor networks,” Int. J, of Parallel, Emergent and Distributed Systems,
vol. 24, Feb. 2009.
[91] A. Ortiz, N. Ababneh, N. Timmons, and J. Morrison, “Adaptive routing
for multihop IEEE 802.15.6 wireless body area networks,” in 20th
Int. Conf. on Software, Telecommunications and Computer Networks
(SoftCOM),, pp. 1–5, 2012.
[92] Q. Tang, N. Tummala, S. K. S. Gupta, and L. Schwiebert, “Communi-
cation scheduling to minimize thermal effects of implanted biosensor
networks in homogeneous tissue,” in IEEE Trans. Biomed. Eng.,
vol. 52, pp. 1285–1294, July. 2005.
[93] A. Bag and M. Bassiouni, “Hotspot preventing routing algorithm
for delay-sensitive biomedical sensor networks,” IEEE Int. Conf. on
Portable Information Devices (PORTABLE07), pp. 1 –5, May. 2007.
[94] A. Bag and M. A. Bassiouni, “Energy efficient thermal aware routing
algorithms for embedded biomedical sensor networks,” in IEEE Trans.
Mobile Adhoc and Sensor Systems (MASS), (Vancouver), pp. 604–609,
2006.
[95] A. Bag and M. Bassiouni, “Routing algorithm for network of homo-
geneous and id-less biomedical sensor nodes (RAIN),” IEEE Sensors
Applications Symp. (SAS), pp. 68 –73, Feb. 2008.
[96] S. Movassaghi, M. Abolhasan, and J. Lipman, “A review of routing
protocols in wireless body area networks,” J. of Networks, March 2013.
[97] D. Djenouri and I. Balasingham, “New QoS and geographical routing
in wireless biomedical sensor networks,” in 6th Int. Conf. on Broad-
band Communications, Networks, and Systems (BROADNETS),pp.1
–8, Sept. 2009.
[98] M. A. Razzaque, C. S. Hong, and S. Lee, “Data-centric multiobjective
QoS-aware routing protocol for body sensor networks,” Sensors,Jan.
2011.
[99] X. Liang, I. Balasingham, and S.-S. Byun, “A reinforcement learn-
ing based routing protocol with QoS support for biomedical sensor
networks,” in 1st Int. Symp. on Applied Sciences on Biomedical and
Communication Technologies (ISABEL ’08), pp. 1 – 5, Oct. 2008.
[100] X. Liang and I. Balasingham, “A QoS-aware routing service frame-
work for biomedical sensor networks,” in 4th Int. Symp. on Wireless
Commun. Systems (ISWCS), pp. 342 –345, Oct. 2007.
[101] S. Movassaghi, M. Abolhasan, and J. Lipman, “Optimized prophet
address allocation (OPAA) for body area networks,” in Proc. 7th
Int. Wireless Communications and Mobile Computing Conf. (IWCMC
2011), pp. 2098–2102, July. 2011.
[102] C. Lauradoux and M. Minier, “A mathematical analysis of prophet
dynamic address allocation,” in Rapport de recherche,INRIA, no. RR-
7085.
[103] S. Movassaghi, M. Abolhasan, and J. Lipman, “Hierarchical collision-
free addressing protocol(HCAP) for body area networks,” in Proc.
3rd Int. Workshop on Wireless Sensor, Actuator and Robot Networks
(WiSARN), pp. 549 –554, April. 2011.
[104] S. Movassaghi, M. Abolhasan, and J. Lipman, “Addressing schemes for
body area networks,” IEEE Commun. Lett., vol. 15, pp. 1310 –1313,
Dec. 2011.
[105] H. Cao, V. Leung, C. Chow, and H. Chan, “Enabling technologies for
wireless body area networks: A survey and outlook,” IEEE Commun.
Mag., vol. 47, pp. 84–93, Dec. 2009.
[106] X.Yu, X.Xia, and X.Chen, “Design and application of rubee-based
telemedicine data acquisition system,” in IEEE/ACIS 10th Int. Conf.
on Computer and Information Science (ICIS),, pp. 365 –370, May.
2011.
[107] S. Movassaghi, P. Arab, and M. Abolhasan, “Wireless technologies for
body area networks: Characteristics and challenges,” in Int. Symp. on
Communications and Information Technologies (ISCIT), pp. 42 –47,
28 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION
Oct. 2012.
[108] H.-B. Li, K. i. Takizawa, B. Zhen, and R. Kohno, “Body area network
and its standardization at IEEE 802.15 MBAN,” in 16th 1ST Mobile
and Wireless Communications Summit, pp. 1 –5, July. 2007.
[109] C. Liolios, C. Doukas, G. Fourlas, and I. Maglogiannis, “An overview
of body sensor networks in enabling pervasive healthcare and assistive
environments,” Proc. 3rd Int. Conf. on Pervasive Technologies Related
to Assistive Environments (PETRA ’10), pp. 43:1–43:10, 2010.
[110] K. Bilstrup, “A preliminary study of wireless body area networks,” in
Technical Report,IDE0854, Halmstad University, Sweden, Aug. 2008.
[111] Z. Chen, C. Hu, J. Liao, and S. Liu, “Protocol architecture for
wireless body area network based on nrf24l01,” in IEEE Int. Conf.
on Automation and Logistics (ICAL), pp. 3050 –3054, Sept. 2008.
[112] W. Scanlon, G. Conway, and S. Cotton, “Antennas and propagation
considerations for robust wireless communications in medical body
area networks,” in IET Seminar on Antennas and Propagation for Body-
Centric Wireless Communications, p. 37, IET, 2007.
[113] R. Serrano, S. Blanch, and L. Jofre, “Small antenna fundamentals and
technologies: Future trends,” in 1st European Conf. on Antennas and
Propagation (EuCAP), pp. 1–7, IEEE, 2006.
[114] A. Kiourti and K. S. Nikita, “A review of implantable patch antennas
for biomedical telemetry: Challenges and solutions [wireless corner],”
IEEE Antennas Propagat. Mag., vol. 54, no. 3, pp. 210–228, 2012.
[115] H. Higgins, “Wireless communication,” in Body Sensor Networks,
pp. 117–143, Springer, 2006.
[116] S. Movassaghi, M. Shirvanimoghaddam, M. Abolhasan, and D. Smith,
“An energy efficient network coding approach for wireless body area
networks,” in The 38th IEEE Conf. on Local Computer Networks
(LCN), 2013.
[117] S. Movassaghi, M. Shirvanimoghaddam, and M. Abolhasan, “A co-
operative network coding approach to reliable wireless body area
networks with demodulate-and-forward,” in 9th Int. Wireless Commun.
and Mobile Computing Conf. (IWCMC), 2013.
[118] K. Wac, R. Bults, B. van Beijnum, I. Widya, V. Jones, D. Konstantas,
M. Vollenbroek-Hutten, and H. Hermens, “Mobile patient monitoring:
The mobihealth system,” in Annu. Int. Conf. on Engineering in
Medicine and Biology Society (EMBS), pp. 1238 –1241, Sept. 2009.
[119] T. Gao, T. Massey, L. Selavo, D. Crawford, B. Chen, K. Lorincz,
V. Shnayder, L. Hauenstein, F. Dabiri, J. Jeng, A. Chanmugam,
D. White, M. Sarrafzadeh, and M. Welsh, “The advanced health
and disaster aid network A light-weight wireless medical system for
triage,” IEEE Trans. Biomed. Circuits Syst., vol. 1, pp. 203 –216, Sept.
2007.
[120] E. Kang, Y. Im, and U. Kim, “Remote control multi-agent system for
u-healthcare service,” in Proc. 1st KES Int. Symp. on Agent and Multi-
Agent Systems: Technologies and Applications (KES-AMSTA), (Berlin,
Heidelberg), pp. 636–644, Springer-Verlag, 2007.
[121] V. Shnayder, B. Chen, K. Lorincz, T. Fulford-Jones, and M. Welsh,
“Sensor networks for medical care,” in Harvard University Technical
Report TR-08-05, 2005.
[122] K. Ouchi, T. Suzuki, and M. Doi, “Lifeminder: a wearable healthcare
support system using user’s context,” in Proc. 22nd Int. Conf. on
Distributed Computing Systems Workshops, pp. 791 – 792, Nov. 2002.
[123] D. Curtis, E. Shih, J. Waterman, J. Guttag, J. Bailey, T. Stair,
R. Greenes, and L. Ohno-Machado, “Physiological signal monitoring
in the waiting areas of an emergency room,” in Proc. 3rd ICST Int.
Conf. on Body Area Networks.
[124] S. Jiang, Y. Cao, S. Iyengar, P. Kuryloski, R. Jafari, Y. Xue, R. Bajcsy,
and S. Wicker, “Carenet: an integrated wireless sensor networking
environment for remote healthcare (bodynets),” in Proc. 3rd ICST Int.
Conf. on Body Area Networks, ICST (Institute for Computer Sciences,
Social-Informatics and Telecommunications Engineering), 2008.
[125] T. Sheltami, A. Mahmoud, and M. Abu-amara, “Warning and monitor-
ing medical system using sensor networks,” in the Saudi 18th National
Computer Conf. (NCC18), (Riyadh, Saudi Arabia), pp. 63–68, 2006.
[126] T. Falck, J. Espina, J.-P. Ebert, and D. Dietterle, “Basuma - the sixth
sense for chronically ill patients,” in Int. Workshop on Wearable and
Implantable Body Sensor Networks (BSN), pp. 4–6, Apr. 2006.
[127] B. Gyselinckx, R. Vullers, C. Hoof, J. Ryckaert, R. Yazicioglu,
P. Fiorini, and V. Leonov, “Human++: Emerging technology for body
area networks,” in Int. Conf. on Very Large Scale Integration (IFIP),
pp. 175 –180, Oct. 2006.
[128] E. Farella, A. Pieracci, L. Benini, L. Rocchi, and A. Acquaviva,
“Interfacing human and computer with wireless body area sensor
networks: the wimoca solution,” Multimedia Tools and Applications,
vol. 38, pp. 337–363, 2008.
[129] K. Venkatasubramanian, G. Deng, T. Mukherjee, J. Quintero, V. Anna-
malai, and S. S. Gupta, “Ayushman A wireless sensor network based
health monitoring infrastructure and testbed.,” Springer Lecture Notes
in Computer Science, vol. 3560, pp. 406–407, 2005.
[130] I. Jantunen, H. Laine, P. Huuskonen, D. Trossen, and V. Ermolov,
“Smart sensor architecture for mobile-terminal-centric ambient intelli-
gence,” Sensors and Actuators A Physical, vol. 142, no. 1, pp. 352–
360, 2008.
[131] D. B. Smith, D. Miniutti, and L. W. Hanlen, “Characterization of
the body-area propagation channel for monitoring a subject sleeping,”
IEEE Trans. Antennas Propag., vol. 59, no. 11, pp. 4388–4392, 2011.
Samaneh Movassaghi received a B.Sc. from Uni-
versity of Tehran in 2009 and a Master by Re-
search in Telecommunication Engineering from the
University of Technology, Sydney in 2012. She
is currently a PhD student at the University of
Technology, Sydney and is conducting research in
the field of Wireless Body Area Networks. She is
currently an IEEE Student member. She has been
the reviewer of a number of conference papers and
journals. Her research interests are in Wireless Body
Area Networks, Energy Efficient Communication,
Interference Mitigation, Address Allocation, Routing Schemes, Energy ef-
ficient Scheduling and QoS provisioning in wireless sensor networks. She
is a recipient of the UTS International Research Scholarship (IRS), UTS
President’s scholarship (UTSP) and UTS Teaching Fellowship Award.
Mehran Abolhasan completed his B.E in Computer
Engineering and PhD in Telecommunications on
1999 and 2003 respectively at the University of Wol-
longong. From July 2003, he joined the Smart Inter-
net Technology CRC and Office of Information and
Communication Technology within the Department
of Commerce in NSW, where he proposed a major
status report outlining strategies and new projects to
improve the communications infrastructure between
the NSW Emergency Services Organisations. In July
2004, he joined the Desert knowledge CRC and
Telecommunication and IT Research Institute to work on a joint project called
the Sparse Ad hoc network for Deserts project (Also known as the SAND
project). During 2004 to 2007 A/Prof. Abolhasan led a team of researchers
at TITR to develop prototype networking devices for rural and remote
communication scenarios. Furthermore, he led the deployment of a number
of test-beds and field studies in that period. In 2008, A/Prof. Abolhasan
accepted the position of Director of Emerging Networks and Applications
Lab (ENAL) at the ICTR institute. During this time as the Director of
ENAL, A/Prof. Abolhasan won a number of major research project grants
including an ARC DP project and a number of CRC and other government
and industry-based grants. He also worked closely with the Director of ICTR
in developing future research directions in the area wireless communications.
In March 2010, he accepted the position of Senior Lecturer at the School
of Computing and Communications within the faculty of Engineering and IT
(FEIT) at the University of Technology Sydney, where he is now an Associate
Professor. A/Prof. Abolhasan has authored over 90 international publications
and has won over one million dollars in research funding. His Current research
Interests are in Wireless Mesh, Wireless Body Area Networks, 4th Generation
Cooperative Networks and Sensor networks. He is currently a Senior Member
of IEEE.
MOVASSAGHI et al.: WIRELESS BODY AREA NETWORKS: A SURVEY 29
Justin Lipman received a B.E. Computer Engi-
neering and Ph.D. Telecommunications Engineering
from the University of Wollongong, Australia in
1999 and 2003 respectively. He joined Intel in 2006
as a Sr. Research Scientist and is currently a member
of Intels IT Labs based in Shanghai, China. Justin
has led research and architecture efforts for numer-
ous Intel products such as the Intel classmate PC for
1:1 Education where he innovated, architected and
filed patents for optimized data transfer in densely
populated Wi-Fi networks and wireless device to
device proximity detection. His research and architecture contributions to
productize Intels long range Wi-Fi based Rural Connectivity Platform led to
the technology being selected by the Clinton Global Initiative in 2008. Prior to
Intel, he was Program Manager for Research and Innovation at Alcatel where
he managed a technically diverse team of talented research engineers charged
with determining the next generation of product concepts and internal ventures
for Alcatel. These included WiMAX Mesh and Backhaul, Wireless over
Copper Distribution, Distributed Antennae Systems, Reliable IPv6 Multicast
efficiency and signaling, 2G/3G Enhancements, Single Frequency Reuse, and
Mobile Gaming. Prior to Alcatel, Justin was a visiting fellow at the University
of New South Wales for the Smart Internet CRC where he prototyped research
on the patented Swarm Phone concept and supervised research students in
mesh and sensor networks. He has published over 40 international peer
reviewed publications, has one US patent and a further 25 patent applications
under review with the USPTO. He has a strong interest in distributed and
ubiquitous computing with an emphasis on Mesh Networks, Body Area
Networks, Internet of Things, Wireless Localization and Location Based
Services.
David Smith is a Senior Researcher at National
ICT Australia (NICTA) and is an adjunct Fellow
with the Australian National University (ANU), and
has been with NICTA and the ANU since 2004. He
received the B.E. degree in Electrical Engineering
from the University of N.S.W. Australia in 1997,
and while studying toward this degree he was on a
CO-OP scholarship. He obtained an M.E. (research)
degree in 2001 and a Ph.D. in 2004 both from the
University of Technology, Sydney (UTS), and both
in Telecommunications Engineering. His research
interests are in technology and systems for wireless body area networks; game
theory for distributed networks; mesh networks; disaster tolerant networks;
radio propagation and electromagnetic modeling; MIMO wireless systems;
coherent and non-coherent space-time coding; and antenna design, including
the design of smart antennas. He also has research interest in distributed
optimization for smart grid. He has also had a variety of industry experience
in electrical engineering; telecommunications planning; radio frequency, op-
toelectronic and electronic communications design and integration. He has
published over 70 technical refereed papers and made various contributions
to IEEE standardization activity; and has received four conference best paper
awards.
Abbas Jamalipour is the Professor of Ubiquitous
Mobile Networking at the University of Sydney,
Australia, and holds a PhD in Electrical Engineering
from Nagoya University, Japan. He is a Fellow of the
Institute of Electrical, Information, and Communica-
tion Engineers (IEICE) and the Institution of Engi-
neers Australia, an ACM Professional Member, and
an IEEE Distinguished Lecturer. He is the author of
six technical books, nine book chapters, over 300
technical papers, and three patents, all in the area
of wireless communications. He was the Editor-in-
Chief IEEE Wireless Communications and has been an editor for several
journals. He is the Vice President-Conferences (2012-13) and a member of
Board of Governors of the IEEE Communications Society. Previously he has
held positions of the Chair of the Communication Switching and Routing
and the Satellite and Space Communications Technical Committees and Vice
Director of the Asia Pacific Board, in ComSoc. He was a General Chair or
Technical Program Chair for several IEEE ICC, GLOBECOM, WCNC and
PIMRC conferences. Dr. Jamalipour is also an elected member of Board of
Governors (2014-16), IEEE Vehicular Technology Society. He is the recipient
of a number of prestigious awards such as the 2010 IEEE ComSoc Harold
Sobol Award, the 2006 IEEE ComSoc Distinguished Contribution to Satellite
Communications Award, the 2006 IEEE ComSoc Best Tutorial Paper Award.
