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SPECIAL ISSUE ARTICLE
IoT‐based wireless body area networks for disaster cases
Murtaza Cicioğlu
1
| Ali Çalhan
2
1
School of Electrical‐Electronic and
Computer Engineering, Duzce University,
Duzce, Turkey
2
Computer Engineering Department,
Duzce University, Duzce, Turkey
Correspondence
Murtaza Cicioğlu, School of Electrical‐
Electronic and Computer Engineering,
Duzce University, Duzce, Turkey.
Email: murtazacicioglu@gmail.com
Summary
Wireless networks have many advantages for emergency situations especially
disaster cases. A disaster might happen because of various reasons such as
earthquakes, hurricanes, floods, and tornadoes. In these emergency situations,
wireless communication technologies are very important for the rescue of
human life. Wireless technologies, especially body area networks, might mon-
itor, collect, and send data about human in trouble to rescue unit. In this
paper, Internet of things‐based wireless body area network is proposed for
disaster cases. The proposed architecture guarantees lifesavings by using wire-
less technologies for location determination, vital signs transmission, and SOS
calls. Also, a gateway selection algorithm based on fuzzy logic is developed for
selecting more appropriate wireless technology.
KEYWORDS
disaster case, IEEE 802.15.6, IoT, WBAN
1|INTRODUCTION
Wireless communication technologies have become indispensable for human life nowadays. There are various types of
wireless networks for different applications.
1
One of them is named as wireless body area network (WBAN) that consists
of the various sensor or actuator nodes placed in the clothes, on or in the body of a person. A WBAN suggests many
promising new applications in the health care monitoring, sports, military, and many others. All aforementioned appli-
cations take advantage of the unconstrained freedom of movement a WBAN offers. For example, a patient can be
equipped with a WBAN consisting of sensor nodes that constantly measure specific biological and vital functions, such
as blood pressure, temperature, respiration, heart rate, electrocardiogram (ECG), and electroencephalography (EEG).
So, the patient can move freely across the hospital or home and does not have to stay in bed or hospital.
Internet of things (IoT) interconnects various IP addressable physical devices like vehicles, sensors, and home appli-
ances over the Internet in order for them to communicate and cooperate for specific applications.
2
IoT is a very rapidly
growing hot topic of technology and connects with a number of other emerging technologies. In this context, WBAN
technology with IoT technology is more powerful for new applications. Different types of sensors and actuators in
WBANs might cooperate with various wireless technologies as an IoT architecture. For this purpose, a gateway node
selection process for interaction with different wireless technologies is proposed in the study.
A lot of people suffer in disasters because necessary help cannot reach on time or never. WBAN‐equipped rescue
teams and people in danger will be in touch with other units for real‐time communication. In this study, the IoT‐based
WBAN structure provides the real‐time communication for disaster cases for saving lives.
The remainder of the paper is as follows: In section 2, the related works are given for the necessity of doing this
study. Then, the proposed architecture and used technologies; IoT‐based WBAN with IEEE 802.15.6, and gateway selec-
tion in disaster cases are presented respectively in section 4. The simulation results are given in section 5.
Received: 27 July 2018 Revised: 5 October 2018 Accepted: 1 November 2018
DOI: 10.1002/dac.3864
Int J Commun Syst. 2018;e3864.
https://doi.org/10.1002/dac.3864
© 2018 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/dac 1of12
2|RELATED WORKS
There are many studies about IoT and WBAN in the literature. A wearable sensor node with solar energy harvesting
is proposed in a previous study.
3
It enables the implementation of an autonomous WBAN for IoT‐connected applica-
tions. Various sensor nodes are used for sensing fall notification, heartbeat, and temperature. Also, a web‐based
smartphone application is developed for monitoring the aforementioned data. Design of physical layer algorithms
for IEEE 802.15.6 narrowband receiver is presented in a previous study.
4
It uses multicarrier signal processing algo-
rithms, such as used in the 802.11a/b/g/n standard. Various scenarios are realized for performance analysis of the
proposed system.
A distributed protocol to enable WBAN operation and interaction within an existing IoT is developed in another
study.
5
The authors control the emerging Bluetooth Low Energy (BLE) technology and promote the integration of a
BLE transceiver and a cognitive radio (CR) module within the WBAN coordinator. The authors discuss various
health monitoring systems, taking the smart phone as a tool in a previous study.
6
And also, they discuss some secu-
rity techniques that are used in data security for health care applications that can be applied in IoT environment.
The authors propose a time synchronization algorithm for the WBAN frame structure specified by the IEEE
802.15.6 standard in a previous study.
7
The proposed algorithm is based on a noncoherent timing error detector
under a Rayleigh fading channel. The system complexity is minimized by getting rid of the phase recovery block
in the study.
A study on integrated IoT and WBAN is proposed in another study.
8
The authors explain wireless sensor net-
works, IoT, and WBAN issues comprehensively. The authors discuss practical issues for implementation of WBAN
to health care service and a multihop WBAN construction scheme is proposed in a previous study.
9
The proposed
structure achieves an energy‐efficient feature by reducing the number of total control messages. They show that
the proposed system enhances the WBAN performance. A study is proposed in a previous work
10
for disaster relief
and emergency communication. An Internet of humans‐based platform is presented in the study. The developed solu-
tion consists of the critical and rescue operation using wearable wireless sensor networks (CROW2) and internet of
humans‐based platform.
Earlier, the authors present a new enhancement for an emergency and disaster relief system called Critical and Res-
cue Operations using Wearable Wireless sensors networks in a previous study.
11
CROW2 is executed based on the ORA-
CLE Net routing protocol. The authors deploy the routing protocol to evaluate the performance of the developed system
and the payload applications on Raspberry Pi and Android smartphone.
To the best knowledge of the authors of this paper, the differences from the literature in this paper are as follows:
i. IEEE 802.15.6‐based WBAN architecture is simulated with Riverbed Modeler
12
for intra‐WBANs and inter‐
WBANs.
ii. A gateway selection algorithm based on fuzzy logic is developed.
iii. The proposed system is a part of IoT technologies.
iv. The developed system is performed for disaster cases.
v. Consequently, there is no study in the literature about WBAN with IEEE 802.15.6, fuzzy logic‐based gateway selec-
tion, and disaster cases together.
3|THE PROPOSED ARCHITECTURE
The proposed architecture is shown in Figure 1. Each person with sensor nodes and a coordinator (or called HUB) con-
structs a WBAN. All sensor nodes send collected data to the coordinator as intra‐WBAN communication. WBANs can
communicate with the other WBANs as inter‐WBAN communication. In this situation, the coordinator nodes send rel-
evant data to the others.
The coordinator nodes have to transmit collected data from sensor nodes in WBANs to the destination. For trans-
mitting data to the destination, the coordinator nodes have to send data to a gateway such as Wi‐Fi access point (AP)
or other wireless technologies. One of the contributions of this study is the process of gateway selection. The parts of
the studies are explained in the next sections with details.
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3.1 |IEEE 802.15.6‐based WBANs
WBAN technologies use IEEE 802.15.6 standard as physical and data link layer specifications.
13
This standard utilizes
industrial scientific medical (ISM) bands, as well as frequency bands, agreed by national medical and regulatory author-
ities. It also provides an international standard for short range, low power, and wireless communication within the sur-
rounding area of the human body, supporting various data rates for different applications.
This standard can use carrier sense multiple access (CSMA) and slotted Aloha techniques. However, in order to
obtain more realistic results, CSMA technique has been preferred in this study. IEEE 802.15.6 standard uses carrier
sense multiple access with collision avoidance (CSMA/CA)–based medium access control (MAC) protocol and eight pri-
ority levels as can be seen from Table 1. The detailed information about IEEE 802.15.6 can be found in another study.
13
TABLE 1 User priorities
UP Traffic
Packet
type
CSMA/CA
CW
min
CW
max
0 Background D 16 64
1 Best effort D 16 32
2 Excellent effort D 8 32
3 Video D 8 16
4 Voice D 4 16
5 Medical data D/M 4 8
6 High priority medical data D/M 2 8
7 Emergency D 1 4
Abbreviations: CSMA/CA, carrier sense multiple access with collision avoidance; CW, contention window; D, data; M, management; UP, user priorities.
FIGURE 1 The proposed architecture
CICIOĞLU AND ÇALHAN 3of12
3.2 |Disaster case management
Disaster cases are created with natural events frequently such as earthquakes, an avalanche, or with unnatural events
such as explosions and fire disasters. Disaster case management is a time‐critical process for affected people in order to
plan for and achieve realistic goals for recovery following a disaster.
14,15
In disaster cases, to identify survivors and rescue team's needs and to provide accurate and timely response are two
important topics. All interventions focus on urgent requirements like location determination. Therefore, communica-
tion technologies are very important in disaster cases. Wireless signals can pass into the buildings and reach destina-
tions. Nowadays, wireless technologies are used anytime and anywhere and densely found in any environment. So,
in disaster cases, some of wireless APs can be broken and some of them ready to serve. We ensure the wireless technol-
ogies help us in disaster cases.
3.3 |Fuzzy‐based gateway selection
The coordinator nodes have to send data from sensor nodes to the relevant destinations. It can be a mobile phone or
laptop as well as a sensor node. Also, it has to use some communication networks as gateway to transmit data to remote
locations. Therefore, a fuzzy logic‐based gateway selection is proposed for WBANs in disaster cases in this study.
Received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and bandwidth parameters that indicate the qual-
ity of a wireless link are the inputs of the proposed fuzzy system. The coordinator node decides on which AP to be con-
nected according to these parameters.
Fuzzy logic is a decision‐making and machine‐learning method and it is an approach to computing based on
“degrees of truth”rather than the “true or false”as Boolean logic. It is suitable for uncertain or approximate reasoning.
The idea of fuzzy logic was first created by Lotfi Zadeh of the University of California at Berkeley in the 1960s.
16
Fuzzy
logic consists of 0 and 1 as extreme cases of truth but also contains the several states of truth in between 0 and 1. Fuzzy
has an important role in our proposed scheme for selecting a gateway. The proposed fuzzy model is shown in Figure 2.
Figure 2 illustrates the fuzzy logic‐based model, which is responsible for the calculation of the candidacy value
according to the SNR, RSSI, and bandwidth information belonging to APs.
The crisp values as RSSI, SNR, and bandwidth are collected from APs. The second process named fuzzification trans-
forms these crisp values into linguistic values. The linguistic values as can be seen from membership functions in
Figure 3. They are low, medium, and high. Membership functions are applied to the measurements and the degree
of memberships are determined.
The other step of the fuzzy is the rule evaluation. The fuzzy rules are used for decision of output in the fuzzy systems
and they are a series of if‐then statements. The inputs are applied to a set of if/then control rules as can be seen from
Table 2. There are three inputs of the proposed system, so there are 27 fuzzy rules that can be defined. Some of them are
written in Table 2.
FIGURE 2 Block diagram of the proposed fuzzy model
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The results of various fuzzy rules are calculated together to generate a set of fuzzy outputs. The last step is the
defuzzification process. In this process, fuzzy outputs are combined into discrete values needed to make a decision in
the proposed mechanism.
In our case studies, the fuzzy steps of the proposed mechanism are illustrated in Figure 3. The inputs are taken to the
fuzzy system, and then candidacy values of APs are calculated between 0 and 1. The AP with the highest candidacy value
is selected as a gateway. In Figure 4, access point with the number of 3 is selected as a gateway. The sequence diagram of
the proposed system is outlined in Figure 5. The coordinator node scans the frequency band periodically for potential
APs considering the aforementioned input parameters (Bandwidth, SNR, and RSSI). When any AP is detected, its work-
ing parameters are stored in a database, for further consideration. As soon as the scan operation is completed, the coor-
dinator node compares the candidacy value of current AP with the ones added to the database, respectively.
FIGURE 3 Fuzzy membership functions
TABLE 2 Some fuzzy rules
If RSSI is low, and SNR is low, and bandwidth is low, then candidacy value is low
If RSSI is medium, and SNR is medium, and bandwidth is medium, then candidacy value is medium
If RSSI is low, and SNR is high, and bandwidth is low, then candidacy value is low
If RSSI is high, and SNR is low, and bandwidth is high, then candidacy value is low
If RSSI is high, and SNR is medium, and bandwidth is high, then candidacy value is high
Abbreviations: RSSI, received signal strength indicator; SNR, signal‐to‐noise ratio.
FIGURE 4 Example fuzzy inputs/
outputs with a scenario
CICIOĞLU AND ÇALHAN 5of12
FIGURE 5 Sequence diagram of the proposed system
TABLE 3 Simulation parameters
Parameter Value
Simulation time 300 second
Slot length 100 ms
Frequency 2400 to 2483.5 GHz
Number of node 8
Number of coordinator 1
Bandwidth 1 MHz
Transmit power of node 0.1 W
Data rate 971.4 kbps
Packet length 1020 bits
MicaZ parameter 2 AA battery (3 V)
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4|SIMULATION RESULTS
The performance analyses of the WBAN architecture and gateway selection process are introduced in this section. The
simulation parameters are given in Table 3. The proposed architecture's delay, throughput, and power consumption
results are shown respectively. Riverbed Modeler is utilized for simulating WBAN architecture.
WBAN architecture consists of eight sensor nodes as UP0, UP1 …UP7, and one coordinator. These sensor nodes
have different priority as can be seen in Table 1. First, the delay results are taken from the simulation environment.
For example, delay results for different nodes with 0.5 packets per second are seen from Figure 6. The delay results
for different packet generation rates are given in Figure 6. The obtained results are compared with the literature, and
delay results are consistent.
17,18
FIGURE 6 A, Delay results for different nodes with 0.5 packets per second B, Delay results for different nodes with 1 packet per second C,
Delay results for different nodes with 2 packets per second D, Delay results for different nodes with 3 packets per second E,Delay results for
different nodes with 4 packets per second F, Delay results for different nodes with 5 packets per second
CICIOĞLU AND ÇALHAN 7of12
As throughput of sensor nodes with different priority increases, the delays vary according to the priority levels. The
sensor nodes with higher priority level (such as UP7) have little change in delay results, but the sensor nodes with lower
priority level (such as UP0) have much change in delay results. The most important reason for this situation arises from
the different contention windows (CWmin‐CWmax) given in section 3.1. The sensor nodes with higher priority use
wireless environment first because of having less contention window sizes. So, the delay results are low. In disaster
cases, these results are quite important for using WBANs and sending data to the destination quickly.
The other performance metric is chosen as throughput. The throughput results are given with different packet gen-
eration frequency in Figure 7. Throughput is the amount of data moved successfully from one node to another in a
FIGURE 7 A,Throughput results for different nodes with 0.5 packets per second B, Throughput results for different nodes with 1 packet
per second C, Throughput results for different nodes with 2 packets per second D, Throughput results for different nodes with 3 packets per
second E, Throughput results for different nodes with 4 packets per second F,Throughput results for different nodes with 5 packets per
second
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given time period and usually measured in bits or packets per second. The obtained throughput results satisfy the qual-
ity of service (QoS) requirements of WBANs.
13,19
The obtained results are compared with the literature, and throughput
results are consistent.
18,20
As can be seen in Figure 7, sensor nodes with different offered loads and different priorities
appear to have throughputs that match the priority levels. It is seen that the throughput of the nodes with higher pri-
ority levels are higher in the different offered loads of the nodes with the higher priority level and the throughput of the
nodes having the lower priority level are decreased with the different offered loads. The results are appropriate and con-
sistent for QoS requirements of WBANs in disaster cases.
The power consumption results are given in Figure 8. All results for nodes are shown in Figure 8, and only UP7 and
coordinator node power consumption results for different offered loads are given in Figure 8. As shown in Figure 8,
energy consumption levels of nodes with different priorities also vary. This is due to the different contention windows
that have different priorities, as described in section 4.1 and Table 1. The UP7 node, which has a high priority, always
has first access to the environment and therefore has a low possibility of collision. However, nodes with a higher con-
tention window, such as the lowest priority UP0, are less able to use the environment and are therefore more likely to
collide. As a result, packet losses and then the process of resending the same packet is processed and more energy is
consumed. The results obtained are consistent with some studies in the literature.
16,17,19
For analysis of the proposed fuzzy‐based gateway selection process in WBAN, a scenario is evaluated as in Figure 9.
A WBAN walks through with a trajectory shown as below. There are some possible gateways in the area. The proposed
fuzzy system gets the relevant parameters and decides which AP to connect to a gateway.
In disaster cases, the wireless environment is unstable; so, RSSI and SNR values are so changeable, and there may be
not enough AP for serving people. In such a case, a scenario is evaluated for simulating disaster case. The proposed gate-
way selection process is run and the candidacy values of gateways are shown in Figure 10.
FIGURE 8 A, Average power consumption (joule per second) for 5 packet per second B, Power consumption results for different offered
loads
FIGURE 9 A scenario for evaluating the proposed system
CICIOĞLU AND ÇALHAN 9of12
As can be seen in Figure 10, first, AP 3 is selected as a gateway with the maximum candidacy value, and then the
other APs that have maximum candidacy values can be selected. The results have shown that the proposed fuzzy‐based
selection process has the ability to select the most appropriate AP as a gateway.
Furthermore, a case study is presented for performance analysis of the proposed system. As can be seen in Figure 11,
a disaster case has happened and a WBAN is found with various APs in the environment. Some of these APs are active
and some of them are passive because of disaster case. WBAN‐equipped human moves along with trajectory and he/she
encounters other active and passive APs. So, the coordinator node of the WBAN scans the environment for possible APs,
and decides to connect the APs with the help of fuzzy logic‐based gateway selection algorithm. The results of this case
study are shown in Figure 12.
FIGURE 10 Candidacy values of
gateways
FIGURE 11 A case study: gateway selection for a disaster case
FIGURE 12 The candidacy values of
the APs for a case study
10 of 12 CICIOĞLU AND ÇALHAN
Firstly, AP 3 is selected as a gateway between AP 2 and AP 3, because APs 1, 4, and 5 are passive. Then, AP 6 is
selected between AP 3 and AP 6, because APs 5 and 7 are passive. And then, AP 9 is selected between AP 6 and AP
9, because APs 7 and 8 are passive. Finally, AP 10 is selected between AP 9 and AP 10, because AP 8 is passive.
5|CONCLUSIONS
WBANs are very important for human beings in many aspects. For remote health monitoring, military applications, and
sports activities, WBAN may be used. In our study, WBAN is used in disaster cases where the people in danger have to
send some information to the destination. So, IEEE 802.15.6‐based WBAN architecture is designed and all QoS require-
ments are satisfied. Delay, throughput, and power consumption results are given in the paper. Also, a fuzzy logic‐based
gateway selection process is proposed and the performance analysis is given with disaster case scenarios and a case
study. Consequently, the proposed system gives a good and correct performance in disaster cases. For future works,
some other IoT systems are included to the system like cell phone and new technologies are added such as cognitive
radio.
ORCID
Murtaza Cicioğlu https://orcid.org/0000-0002-5657-7402
Ali Çalhan https://orcid.org/0000-0002-5798-3103
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How to cite this article: Cicioğlu M, Çalhan A. IoT‐based wireless body area networks for disaster cases. Int J
Commun Syst. 2018;e3864. https://doi.org/10.1002/dac.3864
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