![Some of the proposed wireless sensor nodes: (a) Samson et al. [95]; (b) Pook et al. [35]; (c) Demo et al. [34]; (d) Kiepert et al. [36, 37]; (e) Liu et al. [38]; (f) Wu et al. [39, 40]; (g) Becker et al. [41]; (h) Arms et al. [42–44]; (i) Gao et al. [45, 46].](https://www.researchgate.net/profile/Shang-Gao-7/publication/324965893/figure/fig4/AS:1086451722780672@1636041684022/Some-of-the-proposed-wireless-sensor-nodes-a-Samson-et-al-95-b-Pook-et-al-35_Q320.jpg)
![Some of the proposed wireless sensor nodes: (a) Samson et al. [95]; (b) Pook et al. [35]; (c) Demo et al. [34]; (d) Kiepert et al. [36, 37]; (e) Liu et al. [38]; (f) Wu et al. [39, 40]; (g) Becker et al. [41]; (h) Arms et al. [42–44]; (i) Gao et al. [45, 46].](https://www.researchgate.net/profile/Shang-Gao-7/publication/324965893/figure/fig5/AS:1086451722780692@1636041684135/Some-of-the-proposed-wireless-sensor-nodes-a-Samson-et-al-95-b-Pook-et-al-35_Q320.jpg)
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Review Article
Airborne Wireless Sensor Networks for Airplane
Monitoring System
Shang Gao ,1Xuewu Dai,2Yu Hang,1Yuyan Guo,1and Qian Ji1
1School of Mechanical Engineering, Nanjing University of Science and Technology, XiaoLingWei Street No. 200, Nanjing 210094, China
2Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Correspondence should be addressed to Shang Gao; shang.gao@njust.edu.cn
Received 11 January 2018; Revised 23 March 2018; Accepted 11 April 2018; Published 17 May 2018
Academic Editor: Pavlos I. Lazaridis
Copyright © Shang Gao et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In traditional airplane monitoring system (AMS), data sensed from strain, vibration, ultrasound of structures or temperature, and
humidity in cabin environment are transmitted to central data repository via wires. However, drawbacks still exist in wired AMS
such as expensive installation and maintenance, and complicated wired connections. In recent years, accumulating interest has been
drawn to performing AMS via airborne wireless sensor network (AWSN) system with the advantages of exibility, low cost, and
easy deployment. In this review, we present an overview of AMS and AWSN and demonstrate the requirements of AWSN for AMS
particularly. Furthermore, existing wireless hardware prototypes and network communication schemes of AWSN are investigated
according to these requirements. is paper will improve the understanding of how the AWSN design under AMS acquires sensor
data accurately and carries out network communication eciently, providing insights into prognostics and health management
(PHM) for AMS in future.
1. Introduction
In a typical commercial/military aircra, the AMS includes
safety-critical system (e.g., engine control system, ight
control system) and nonsafety critical systems (e.g., structural
and engine health monitoring system, cabin environmen-
tal control system, and inight entertainment system) [].
Traditionally, a large number of real-time sensors based
on wired connections have served for current AMS. For
instance, in the airbus A, over miles of cables consist
of around , sensor connectors and , wires [].
Wired system in AMS has distinct features as follows: (a)
cableroutingisquiteastrictandcomplicatedtask.For
example, the power routing and electrical signal routing
should be physically separated to prevent routings from
electromagnetic interference, impeding airline customization
in the course of manufacturing. (b) e utilization of wire
harness is limited in accessible sensor locations and harsh
environmental condition. (c) Installation of longer wires in
large-size structure is time-consuming and labor-intensive.
(d) Degradation of wiring might contribute to ight mission
unnished or termination, even severe catastrophic failures.
According to a US Navy report, about aircra are made
ight mission incapable due to wiring faults, resulting in over
mission aborts each year []. Traditional wired AMS []
suers from many shortcomings mainly due to long wires
which connect each sensor to a central unit.
Till now, the progress achieved in embedded sensors
technologies and wireless data transmission has extended
the monitoring capability of aeronautical structures, space-
cra and ground testing equipment, and cabin environment
[]. One major potential advantage of using AWSN is the
reduction of weight and installation time of aircra system,
fuel consumption, maintenance, and overhaul. e AWSN
provides a new approach to resolve many issues intrinsic to
wire-based instrumentation, such as fuel eciency, carbon
emission, and ight mass [, ]. It has been proved that the
AWSN can result in lbs. weight reduction of Cessna R
control systems, lbs. weight saving for an SH military
helicopter control system [] and pounds reduction of
wires for Blackhawk helicopters []. Generally, the cabling
planning tasks for one aircra have a cost of , dollars per
kg []. e adopted AWSN can reach the savings of –
millions of dollars per aircra [].
Hindawi
Wireless Communications and Mobile Computing
Volume 2018, Article ID 6025825, 18 pages
https://doi.org/10.1155/2018/6025825
Wireless Communications and Mobile Computing
An attractive use of AWSN in AMS is sensing. In aircra,
due to high angles of attack in takeo/landing, sudden pilot
manoeuvres, turbulence, wind gusts, and normal shock waves
on the wing at transonic speed, boundary layer separation
on the wings the occurs, resulting in the phenomenon
of parasitic drag and stall. For this reason, deployment
of wireless airow control actuators on strategic locations
especially wing for providing real-time airow information
anddecisionmetricstothelocalsystemshedslightonthe
construction of ecient closed-loop airow control opera-
tion []. e AWSN can implement self-conguration, RF
tolerance, and maintenance troubleshooting []. In criti-
cal applications, although wireless links cannot completely
replace cables in light of the high reliability requirement, they
canalsofunctionasredundantlinks,enhancingthereliability
and exibility of AMS. Also, the AWSN will increase the
safety and system exibility due to less complicated fault
allocations processing and re hazards than wiring system.
isideaofoperatingAWSNforAMSwasinitially
introduced by researchers in the early century and several
researchers remarked the potential benets of this technology
over traditional AMS systems. Nevertheless, issues should
be still underscored when these systems kept in a long-term
operation. As a matter of fact, the rst AWSN prototypes
employed low-processing MCU coupled with low-resolution
analog-to-digitalconverter(ADC)andlowsamplingrate,but
the technique makes sophisticated sensing and conditioning
elements available. Recently, a number of scholars have
been dedicated to investigating key issues (e.g., powerful
hardware prototype, network protocol, time synchronization,
and passive sensing) in this disciplinary eld, indicating that
AWSN becomes increasingly practical in AMS [–].
Several well-known research institutes have invested
adequate funds for AWSN based AMS. For example, the
Wireless Interconnectivity and Control of Active Systems
(WICAS) project funded by the Engineering and Physical
Sciences Research Council (EPSRC) applies AWSN to aircra
wing active ow control []. e Flite Instrumentation
Test Wireless Sensor (FLITE-WISE) project backed by the
European Commission has developed AWSN to facilitate
the continuous monitoring of European aircra. e aim
of the project is to move away from the unnecessary bur-
den presented by wires, making aircra maintenance more
ecient []. e SAHARA project supported by ASTECH
company is a French R&D project which targets wireless
sensors applied on aircra, helicopters, and space vehicles
[]. e United States Air Force has started the Advanced
Subminiature Telemetry (ASMT) program in Florida at Eglin
Air Force Base aimed at developing a AWSN system for
aircra ground and ight test monitoring [, ]. e Fly-
by-Wireless (FBW) Alliance led by NASA Langley Research
Center announced to largely fund four research projects to
apply AWSN for AMS [, ].
Rapid advances on composite materials and piezoelectric
sensors have presented new opportunities to AMS, essential
to make more comprehensive analysis for damage, impact,
and crack monitoring []. Typically, the integration of piezo-
electric sensors and AWSN has opened a new door for active
AMS [–]. e simplicity, robustness, and potentially low
cost of piezoelectric sensors determine the suitability of their
embedment into aircra composite structures, contributing
to excite and sense Lamb waves as an online aircra health
monitoring method [, ]. Depending on the capability
of local signal conditioning, data processing and wireless
communication and wireless piezoelectric node can locally
carry out the interpretation and processing of Lamb wave,
promising a real-time large-scale AMS [].
is paper is organized as follows: Section describes
a brief context of AMS, AWSN, and the characteristics of
AWSN from dierent aspects (accuracy, real-time, reliability,
timesynchronization,throughput,longevity,safety,andsecu-
rity). Section presents the current platforms and network
communication schemes of AWSN. Finally, potential paths
for future development and main challenges are briey out-
linedinSectionfollowedbytheconclusionsinSection.
2. Context of AWSN
In this section, the context of AWSN is discussed, from
both a general and a specic perspective. Firstly, a brief
description of AMS is presented to illustrate the role of
AWSN in this novel concept and framework. en, an AWSN
diagram is introduced to show the structure of general
AWSN in which the architecture of airborne wireless sensor
nodes, communication networking, and node deployment
are demonstrated. Finally, we summarize the characteristics
required for AWSN to accommodate new requirements of
AMS.
2.1. e Overview of AM. In this review, the AMS mainly
includes airplane structural health monitoring (SHM) and
airplane cabin environmental monitoring. e AMS collects
data from various sensors deployed on airplane structures
and installed inside airplane cabin, implementing structural
health monitoring and cabin environmental monitoring,
respectively. With the rapid development of new materials
andadvancedtechnologyinairplane,themodemstructures
are becoming complicated increasingly. e airplane SHM,
including Lamb wave damage detection technology, optical
ber based global condition perception, multisensor fusion
detection technology, and structural health assessment, pro-
vides approaches for assessing the health condition and
ensures the safety of the complicated structures. Especially,
structural response is obtained through strain, vibration,
ultrasound, and piezoelectric sensor online structural health
monitoringonairplanestructures.eresponseisusedto
evaluate structural health status and assess the residual life
oftheairplanestructure,furthertodevelopPHM.Overall,
theSHMisaimtosavemoneyspentonmaintenanceor
replacement and ensure the structure to operate eciently
during its whole intended life.
e airplane cabin full of a mixture of outside and recir-
culated air is a semienclosed structure. Generally, the airplane
cabinisinalowhumidity,lowpressuredynamiccondition.
Additionally, various concentrations of ozone (O3), carbon
monoxide (CO), carbon dioxide (CO2), and other chemicals
are generated and spread over the airplane cabin. Dierent
locations (e.g., on the ground, in ascent, at cruise, or in
Wireless Communications and Mobile Computing
Smart sensors
Temperature and
humidity sensors
Vibration sensors
Strain sensors
Piezoelectric sensors
RFID sensors
Inter-AWSN Beyond AWSN
Portable devices
Remote Servers
Ground station
Air-traffic-control center
Access Point
Access Point
Gateway
Cockpit displayers
Management system
Control system Satellite system
Access Point
F : e airborne wireless network schematic diagram.
Sensing Interface
Include analog-to-digital
converter (ADC) to
convert analog snsor
signals to digital formats
Wireles s Radio
Wireless radio to transmit
and receive data with
other wireless sensors and
data serves
Computing Core
Two main components of
wireless sensor cores are
microcontrollers and
memory for sensor data
Actuation Interface
Includes digital-to-analog
converter (DAC) to
Command active sensors and
actuators with analog signals
F : e schematic diagram of airborne wireless sensor board.
decent) of the airplane determine the level of contaminants
invaded from outside sources. If the level of contaminants is
notreal-timemonitoredandadjustedtimely,itisharmfuland
dangerous for passengers and crews.
2.2. e Overview of AWSN. To further illustrate the frame-
work of AWSN, we will concentrate on wireless commu-
nication from the perspective of AWSN deployed in AMS.
AsshowninFigure,eAWSNcommunicationsys-
tem includes four components: smart sensors, inter-AWSN,
beyond AWSN, and remote servers. Within AWSN, various
smart sensors deployed on airplane connect to airborne
wireless sensor nodes. It is clear that the AWSN is formed
among all sensor nodes depending on wireless transceivers.
Beyond AWSN, access point nodes and the gateway create a
bridge to other networks in airplane such as portable devices,
cockpit displayers, and control system. Finally, higher-level
data applications, including satellite network, ground station,
air-trac-control center, and management system, are based
on these specic networks. For communication and networks
within an airplane, the AWSN has become a good com-
plimentary network to an airborne wired communication
network.
2.2.1. Airborne Wireless Sensor Nodes. e basic block of
any wireless sensor network is the airborne wireless sensor
board. e appropriate selection of board is favorable for the
performance of wireless monitoring. As shown in Figure ,
typical airborne wireless sensor board consists of three or four
functional subsystems: sensing section, computational core,
wireless transceiver, and, for some, an additional actuation
interface.
Wireless Communications and Mobile Computing
T : Comparisons of dierent Wireless Avionics Intra-Communications.
Standards Standard Max.
throughput Frequency Free-space
range Spectrum
WiFi IEEE . Mbps . GHz m Unlicensed
Zigbee IEEE .. Kbps / MHz/. GHz m Licensed
Bluetooth IEEE .. Mbps ./ GHz m Unlicensed
RFID ISO/IEC Kbps kHz/. MHz cm– m Unlicensed
LoRaWAN IEEE .. Kbps /// MHz Km Unlicensed
SigFox IEEE .. Kbps / MHz Km Unlicensed
NB-IOT IEEE .. Kbps MHz Km Licensed
WirelessHART IEEE .. Kbps . GHz m Unlicensed
ISA.a IEEE .. Kbps . GHz m Unlicensed
WiMAX IEEE . Mbps GHz m Unlicensed
GHz GHz Mbps – GHz m Unlicensed
UWB UWB Mbps .–. GHz m Unlicensed
WA I C C-Band
IEEE .. Kbps–Mbps
.–. GHz,
.–. GHz,
.–. GHz,
.–. GHz
m Unlicensed
e computation core is the primary dierence between
a airborne wireless sensor board and its wire-based counter-
part. e presence of a microcontroller unit (MCU) allows
for onboard data processing, data storing, and preparing for
communication. To fulll these tasks, the measured data and
executable program (such as damage detection routines) are
embedded in random access memory (RAM) and read only
memory (ROM), respectively. e size (in bits) of internal
data bus for microcontroller is classied as -, -, or -
bits, determining processing speed and power consumption.
Many dierent memory sizes and employed algorithms are
commercially available, which are tailored in conformity with
the particular monitoring activity to be performed.
e sensing section is dedicated for converting ana-
log output into a digital representation that can be han-
dled by digital electronics. Some typically used sensors for
AMS application include strain gauges, temperature sensors,
accelerometer and piezoelectric sensors. Many sensing sec-
tions integrate more than one type of sensing elements, while
others incorporate one sensor concentrating on one kind
of physical quantity for accuracy and power-saving reasons.
Usually, the section includes amplier, linear, compensator,
and lter. e sensing resolution relies on the ADC eective
number of bits and measurement range in Volts, coupled with
sensitivity of sensors. For most AMS applications, ADC reso-
lution of bits or higher is preferred for detecting signals.
For example, generally, low sampling rates (e.g., less than
HZ) are adequate for aircra structural health monitor-
ing. However, wireless sensors are increasingly investigated
for applying in acoustic and ultrasonic NDE; therefore, there
has been a growing desire for higher sampling rates in excess
of Hz.
e presence of radio frequency (RF) communication
allows each board to interact with other nodes and to
forward sensing data. For this reason, more stress on eective
communicationneedstobelaidforthesakeofAMS’sreliabil-
ity and high-performance transmission. is is particularly
true as high data sampling rate, high delity sensing, high
transmission rate, and large transmission range are oen
involved in AMS. RF communication is real challenge on
aircra structures made of composite or steel components.
e last subsystem would be the actuation interface in
which the core element is the digital-to-analog converter
(DAC). It allows converting digital data generated by MCU
into a continuous analog voltage output for exciting active
sensors (e.g., piezoelectric elements) interplayed with the
physical structures. Actuators and active sensors installed
onphysicalsystemcanbothbehandledbyanactuation
interface.
2.2.2. Communication Networking. In Table , thirteen
WSNs standards including WiFi, Zigbee, Bluetooth, RFID,
LoRaWAN, SigFox, NB-IOT, WirelessHART, ISA.a,
WiMax, GHz, UWB, and Wireless Avionics Intra-
Communications (WAIC) have been listed. e comparisons
of parameters such as the standards, working frequency,
maximum range, and maximum throughput are also listed.
It is evident that most standards share the same framework
(IEEE ...) and radio frequency. Nowadays, as a
promising standard based on IEEE ., WiFi has become
another option in WSN. However, WiFi is not designed
for AMS applications. Recently, the civil aviation industry
is developing new standards such as WAIC for airplane.
e WAIC is to provide highly reliable short-range radio
communications between avionic systems and onboard
subsystemsinthesameaircra.In,theWorldRadio
Conference [] proposed a new spectrum band of GHz
[]or.to.GHz[]fortheWAIC.eWAIC
systems can also use parts of IEEE .. standard [].
Compared to GHz, the frequency band of .GHz to
Wireless Communications and Mobile Computing
. GHz is more suitable for the WAIC as the WAIC in
GHz and radio altimeter share the same frequency [].
Till date, the frequency of . to . GHz has emerged as
a promising band for WAIC communications. In addition,
the electromagnetic waves from wireless airborne devices
inside internal airplane suer from high attenuation as
they penetrate complex metallic and composite spaces. In
Table , the values of free space range in dierent wireless
standards do not take propagation path loss into account. e
AWSN has inherited many features from WSNs, especially
communication protocol. us, all protocols can be applied
in AWSN theoretically. However, due to the particular
features of airborne environment, some new constraints and
requirements in Section . will be demonstrated for AWSN.
2.2.3. e Deployment of AWSN in AMS. General aircra
body is consisted of le and right wing, cockpit or cabin,
engine, vertical tail, le and right horizontal stabilizer, land-
ing gear, front, middle, and rear sections which are installed
in subsystems of aircra. Due to the dispersing deploy-
ment characteristic of the subsystems, cluster-star network
topology is more suitable for AWSN in AMS []. Figure
illustrates how the WSN is deployed inside cabin, fuel tank,
on the wings and other sections of the airplane with cluster-
star topology. To meet the requirements of AMS, one or
more clusters are constructed in each subsystem or respective
region of aircra body, and cluster head and sensor nodes
in each cluster formed cluster topology. e deployment of
sensor nodes at certain optimum locations inside airplane
enumerates as follows:
(1) Fuel Tank. e sensor nodes are deployed inside fuel tanks
whicharelocatedinthewingsandtailoftheairplaneto
measure the level of fuel.
(2) Exhaust. Sensors placed inside the exhaust would monitor
whether any obstructions exist in it.
(3) Wheels. e routine examination for health and condition
of the wheels should be implemented before takeo and
aer landing of aircra. Moreover, the wheels might be also
damaged while the aircra is on the runway or in the air.
(4) Engine.eengineisthe“heart”ofanairplane,which
should be monitored in real-time. Overheating or physical
damageoftheengineisharmfulforairplanes.Mostsevere
catastrophic failures even airplane crashes are associated with
the safety of engine. us, sensor nodes installed in and
around the engine would monitor temperature and state of
the engine surrounds and all components.
(5) Wings. Wings in the aircra are always exposed to
corrosion, impact, and crack damage due to external various
complicated climatic environments. Sensor nodes installed
in the wings would monitor vibration or strain arising from
them to diagnose or forecast the localization, severity of
them.
(6) Fire and Safety. Certain areas inside the airplane such
as the aircra cabin and the passenger area carry items
Application
Transportation
Routing
MAC layer
Physical layer
Accuracy
Real-time
Reliability
Time synchronization
roughput
Longevity
Layer-AWSNs Demands for AWSNs
Safety and Security
F : e characteristics for AWSNs.
like luggage, cockpit, the kitchen, passenger section, and the
cargo, where smoke sensor nodes might detect re indication
andsendalertsthroughtheAMS.
2.3. e Requirements of AWSN for AMS. e AWSN has
some requirements particularly for the environment of AMS.
Generally,thearchitectureofordinaryWSNisconstituted
with dierent layers including application, transporting,
routing, medium access control (MAC), and physical layer.
However, this traditional layer architecture cannot satisfy the
requirements for AWSN applied in AMS. e requirements
cover accuracy, real-time, reliability, time synchronization,
throughput, longevity and safety and security in terms of
their constraints, challenges, and design goals for realizing
AWSN, as shown in Figure . To meet these requirements, the
designofplatformsandMACschemeforAWSNneedstobe
considered throughout all layers in Section .
2.3.1. Accuracy. e accuracy of acquired data is an important
data quality aspect. In the AWSN, data accuracy is directly
related to the accuracy of the airplane prognostics and health
management, such as fault detection, fault isolation, fault
prognosis, and prognosis of the remaining life. Additionally,
the accuracy of acquired data aects the safety of airplane, the
economic prots, and ight eciency. In AWSN, the context
is also associated with time synchronization, node number,
hop number, and sampling rate.
2.3.2. Real-Time. WSNs have been applied in smart plants,
industrial environment monitoring, and automation facto-
ries for low latency wireless communication, which sets
an example for the AWSN. Real-time performance is an
important issue for the AWSN. Communications between
wireless nodes require latency to improve productivity.
However, dierent applications may have dierent real-time
requirements. Table shows the latency requirements for
dierent AMS subsystems. Flight motion control systems
have the fastest real-time requirement (< ms), while mon-
itoring systems tolerate the largest latency value (<s).Many
researchers have presented methods to achieve good real-
time performance from dierent views and layers of wireless
networks. Most of these studies have achieved low latency by
improving the routing, MAC, or transport layers. Section .
Wireless Communications and Mobile Computing
T : Real-time requirements in dierent AMS.
AMS Latency
Automation system <ms
Environmental monitoring system <s
Flight motion control system < ms
Wing control < ms
gives a summary of dierent communication schemes for
real-time requirements.
2.3.3. Reliability
Path Loss. e wireless airborne devices employed inside
airplane forward structural or environmental data to data
aggregation center. Unfortunately, the electromagnetic waves
from devices inside internal airplane should penetrate com-
plex metallic and composite spaces. For instance, wireless
propagation in airplane wing suers from high attenuation
as it goes through wing relief holes and spaces between wing
structures including skin, spar, stringers, and ribs. However,
path loss models for airplane wing environment link have
been seldom investigated. It is challenging to get complete
wireless link budget due to the high metallic feature of
airplane wing. Due to the particularity of airplane wing
environment, traditional path loss models for building or
outdoor environments (e.g., alsh-Ikegami [], Keenan-
Motley [], and Turkmani []) are unsuitable for empirical
path loss of airplane wing.
Signal Interference. e wireless airborne devices inevitably
face interference due to elements of the harsh environment
such as heavy dust, vibration, heat, freeze, uncertain tem-
perature and humidity, bad weather in upper air, and other
RF signals [], which contravene wireless communication
principles. Because personal wireless mobile devices such as
mobilephonesandlaptopsarenotallowedtooperatein
airplane cabin, they will not produce signal interference. In
addition, as Zigbee, Wi-Fi, UWB, and Bluetooth protocols
can be adopted in AMS, they also have to coexist with
each other. If the AWSN has only one working channel,
it usually suers from frequency interference and packet
errors. In external aircra, the signal interference sources are
generally from air-ground (A/G) communication system, air-
air (A/A) communication system, satellite-based communi-
cation systems, aeronautical Mobile Airport Communication
System (AeroMACS), and other communication systems
[]. e A/G communication systems use HF, VHF, and
L bands for data transmission. e A/A communication
systems adopt HF band. Also, the satellite-based aeronautical
communication systems usually use VHF, X, Ku, and Ka
bands. As shown in Table , most frequencies of S-Band and
C-Band in AWSN and WAIC coexist with most bands in
A/G communication system, A/A communication system,
satellite-based communication systems and AeroMACS [].
However, the overlapping bands between AWSN, WAIC, and
other systems should also be underlined. For instance, the
onboard GNSS inside aircra conveys the aircra position
and speed to Extended Squitter (ES) signals and sends
them by A/G communication system. e ground stations
receive the ES signals, report to air trac controller, and
then respond to aircra A/G communication system. e ES
operating in the MHz band is vulnerable to cochannel
interference from the AWSN. Moreover, the WAIC and
aeronautical radio altimeters (RAs) share the band of . to
. GHz, as well as the WAIC, and airport surface applications
share the band of .–. GHz, which has the risk of
harmful mutual interference []. e RAs are utilized to
provide accurate and reliable measurements of the minimum
distance to the earth surface. ey operate in the process
ofightorinthesituationwheretheaircraislocatedon
ground or taking o. Table summarizes all aeronautical
spectrum bands and their services.
Packet Loss and Bit Error.ereliabilityoftheAWSNfor
AMS is an important evaluation index. However, the harsh
aircra environment of AMS can introduce more interference
and increase the packet loss rate (PLR) and the bit error
rate (BER) of the AWSN. Retransmission mechanism is an
approachgenerallytohandlewiththeseproblems,whichalso
increasesthelatency.isisanissueforAWSNandthere-
fore some researchers have proposed ecient algorithms to
increase network reliability in Section ..
2.3.4. Time Synchronization. Each airborne wireless sensor
node has its own local clock, which is not initially synchro-
nized with other nodes. Two jitters, namely, temporal jitter
and spatial jitter, occur inside node and between dierent
nodes, respectively, due to variation in oscillator crystals.
Time synchronization errors between dierent devices mean
that obtaining the proper mode shapes without deviating
from reality or theoretical calculations of structure is impos-
sible []. How synchronization errors aect the process of
obtaining mode shapes and why the synchronization error
should be below ms in order to get valid data are studied
in []. Synchronization errors contribute to the inaccu-
racy of synchronized data acquisition and unstable long-
time operation of the AWSN. Furthermore, the precision
of time synchronization aects the validity of structural
analysis and the accuracy of structural damage diagnosis
[].InAWSN,thetimesynchronizationerrorshouldbe
controlled within microsecond. At present, many researches
on clock synchronization of WSNs, such as TPSN [], RBS
[], and FTSP [], have the synchronization accuracy
of us approximately. However, data sampling rate is not
considered in these methods. If the data sampling rate
(e.g., vibration) increases in AWSN, airborne wireless sensor
nodes spend more time for data acquisition, contributing
to the decrease of synchronization accuracy []. To tackle
this limitation and ensure the synchronization accuracy,
systems have to reduce the number of nodes at the expense
of network throughput performance. erefore, in airplane
SHM,thegoalistodevelopacompletewirelessmeasurement
system with appropriate time synchronization method in
which the accuracy is always less than us,,thenumber
of nodes is not limited, and reliability is no longer an
issue.
Wireless Communications and Mobile Computing
T : e overview of all aeronautical spectrum bands and their services.
Band Frequency Service
HF – MHz A/G communication system, A/A communication system
VHF .– MHz A/G communication system, satellite-based aeronautical
communication system
L-Band – MHz
– MHz
A/G communication system, Extended Squitter (ES) signals, and
AWS N
X-Band GHz
Satellite-based aeronautical communication system
Ku-Band .–. GHz
.–. GHz
Ka-Band – GHz
S-Band .–. GHz AWSN, WAIC
C-Band .–. GHz
.–. GHz
WAIC, Radio Altimeters
WA I C
C-Band .–. GHz Airport surface applications, WAIC.
2.3.5. roughput. For AWSN application, it is required to
improve the network throughput for collecting large amount
of acquired data in AMS. eoretically, the baseline through-
put between two nodes with single-radio at IEEE ..
band is Kbps. Furthermore, Osterlind and Dunkels
proved that the maximum data throughput at IEEE ..
band is Kbps []. If the packet copying is considered
between the transceiver and the microcontroller during real
data sampling and sending procedure, the maximum data
throughput is Kbps approximately. Furthermore, if the
network communication protocol is also considered when
multiple nodes are accessed into network, further decrease of
data throughput will result in the time delay of transmission,
thus weakening the real-time performance of AWSN. For
instance,MBdataacquiredinAWSNwilltakehours
approximately to be transferred to data aggregation center
in light of communication protocol. e multichannel com-
munication can improve the data throughput by transferring
data in dierent communication channels [, ]. However,
most multichannel communications in the single-radio sink
node depend on one radio module. Some researchers investi-
gated multichannel communication by employing multiradio
sink node. In the multiradio sink node, switching among
dierent channels is avoided and data owing in all radios can
be received parallel from senders, which can greatly improve
data throughput.
2.3.6. Longevity. Many studies have pointed that WSNs are
still constrained by energy limits because most of airborne
wireless sensor nodes are usually battery-powered. In this
case, it is impractical to replace and recharge a large number
of batteries. Furthermore, in AWSN, batteries not only pro-
vide energy for wireless communication but also for mechan-
ical systems, so the energy limitation is a critical challenge for
prolonging network life. Typical WSN deployments employ
battery-powered nodes. ese onbattery nodes are usually
deployed randomly and widely. It is impossible for them
to be recharged once deployed. We should try our best
to provide staple networking functionality within limited
energy budgets and reduce their energy consumption by
using innovative routing algorithm. In this section, we will
concentrateontwoaspectstoprolongtheoperationofAWSN
and discuss the longevity.
e rst approach to ensure longevity of AWSN is
ecient energy management and conservation. Energy e-
cient strategies have been implemented in dierent layers.
For instance, in the physical layer, unnecessary actions
can be reduced, and the physical parameters can be opti-
mized to achieve strong power-saving performance. In the
application layer, several useful methods such as event-
driven techniques, application-driven techniques, and e-
cient data/messaging can be used to decrease energy con-
sumption. Additionally, as for networking and communica-
tion, the protocols of MAC routing can be designed to reduce
the energy consumption. For instance, sleeping and working
mechanism is adopted to achieve good energy conservation.
Energy harvesting and transferring power wirelessly are
other approaches to improve the longevity of AWSN, which
redene the traditional design of battery-operated AWSN.
e WSNs coupling with ambient energy harvesting can
prolong the system’s lifetime or possibly enable perpetual
operation. Meanwhile, the self-powered nodes have longer
life-time for routing and path selections for data transmission
than on-battery nodes. Many researchers have studied energy
harvesting by exploring environmental energies such as
solar, vibration, wind, and microwave [–] to increase
energy eciency and save power. However, the self-powered
nodes cannot be rearranged as their locations are presented
by AWSN requirements. e transferring power wirelessly
hastheabilitytomoveenergyacrossspace,allowingfor
exploiting abundant energy sources available at places other
than the locations of sensing. Moreover, the transferring
power wirelessly balances energy across the AWSN by dis-
tributing the available energy from energy-rich locations to
energy-poor locations. Table summarizes sources of energy
harvesting for AMS in more detail.
2.3.7. Safety and Security. Safety and security are an impor-
tant consideration in safety-critical avionics applications. In
both wired network and AWSN, they should be considered
Wireless Communications and Mobile Computing
D.Samson
(a)
POOK M
(b)
Demo J
(c)
Kiepert J
(d)
Liu
(e)
Wu J
(f)
Becker
(g)
Arms
(h)
Shang Gao
(i)
F : Some of the proposed wireless sensor nodes: (a) Samson et al. []; (b) Pook et al. []; (c) Demo et al. []; (d) Kiepert et al.
[, ]; (e) Liu et al. []; (f) Wu et al. [, ]; (g) Becker et al. []; (h) Arms et al. [–]; (i) Gao et al. [, ].
T : Some sources of energy harvesting for AWSN.
Authors Harvesting energy
Samson et al. [] ermoelectric
Lu et al. [], Hart et al. [], Hadas et al. [],
Arms et al. [] Vibration
Siu et al. [], Klesh and Kabamba [] Solar
Azevedo and Santos [] Wind
Zhao et al. [] Microwave
in the OSI protocol stack including physical, MAC, rout-
ing, transportation, and application layer. e AWSN is
vulnerable to malicious attacks in all layers, and security
vulnerabilities with these layers are separately protected at
each layer.
Jamming [] is the main attack in physical layer in WSN.
A common mechanism against physical layer jamming attack
in WSNs is spread spectrum communication such as FHSS,
DSSS, and THSS. In MAC layer, the attack mainly includes
MAC spoong [] and man-in-the-middle (MITM) []
attack. Mac spoong attack can alter the MAC address and
code it into NIC card, making nodes take illicit activities.
Another attack type, MITM attack, intercepts MAC address
of the legitimate nodes by sning network trac. It functions
as relay between two victim nodes. In routing layer, IP
protocol is in charge of data or packet delivery from the
source to the destination through routers using IP addresses.
eattacksinthislayerincludehijacking[]andsmurf
attack []. IP hijacker controls the IP address of legal
users, contributing to disconnection between legal users and
network and nally establishing an illegal network. In smurf
attack,alargenumberofICMPpacketsaresenttothe
victim nodes which respond to ICMP requests. is attack
overwhelms the victim network in this way. In transport
layer, the TCP ooding attack [] and UDP ooding attack
[] are implemented by sending large number of ICMP
ping requests and UDP packets to victim nodes, respectively.
e two ooding attacks contribute to the delay connection
of victim to the target network. In application layer, several
p r o t o c o l s s u c h a s H T T P, F T P, a n d S M T P a r e v u l n e r a b l e t o
security attacks. Malware attack [] includes the ways of
Trojan horse, worms, key-loggers, and viruses. SQL injection
[] attacks network by acquiring unauthorized access to
websites. Firewalls and antiviruses are required to counter
theseattacks.AsshowninTable,wesummarizedvarious
security attacks, their characteristic features, and counter-
measures at dierent OSI layers.
3. Current Platforms and Network
Communication Schemes of AWSN
In Section ., seven requirements described above, accu-
racy, real-time, reliability, time synchronization, throughput,
longevity, and safety and security, are closely related to each
other. In AWSN, the main factors including hardware plat-
forms and MAC schemes determine the performance of the
seven indexes in AWSN. erefore, in this section, we survey
current hardware platform design for AWSN applications.
Also, we provide a brief comparative study of emerging and
existing network communication schemes development for
AWSN in terms of seven requirements. e purpose of these
surveys is to investigate how to design a feasible and high-
performance AWSN for satisfying the requirements of AMS.
Table presents the key components like computing speci-
cations, data acquisition, and wireless section of airborne
wireless sensor node prototypes. Furthermore, in Table ,
analytical comparisons of characteristics features such as
throughput, max sampling rate, time synchronization error,
and target application are condensed. A few examples of the
developed sensor boards are shown in Figure .
Wireless Communications and Mobile Computing
T : Characteristics and countermeasures of attacks in various OSI layers.
Attacks Characteristics OSI layer
aected Countermeasures
Malware attack Trojan horse, worms, key-loggers, and viruses Application Firewalls and antiviruses
SQL injection Acquiring unauthorized access to websites Application Firewalls and antiviruses
TCP ood Sending massive ping requests Transport Reducing packets response
UDP ood Sending massive UDP packets Transport Reducing packets response
IP hijacking Legal users IP address impersonation Routing Firewalls
Smurf attack Sending massive ICMP requests Routing Reducing packets response
Mac spoong MAC addresses falsication MAC ARP packets
MITM attack Communicating nodes impersonation MAC Virtual privat e networks
(VPNs)
Jamming attack Interrupting legal data transmission Physical Spread spectrum techniques
Denial of service Sending abundant packets Physical Temper-proof packaging
3.1. Current Existing Platforms. In , Zhao et al. [, ]
investigated a wireless piezoelectric sensor/actuator platform
for real aluminum aircra wing health monitoring. e
platform’s computational core consisted of a CF MCU
and ADC chip ADC having bit resolution and sampling
rate of MSPS, while it used LINX ES Series transceiver for
RF transmission. A customized conditioning board generated
V peak-to-peak tone-burst signals ranging from KHz
toKHzwhichwasfedtoPZTtransducersforproducing
ultrasonic guided waves. e total power consumption of
board was close to mW (i.e., mW for the RF transmit-
ter, mW required by the ADC).
From to , Wu et al. [, ] worked on
developing a low cost wireless sensor node for aircra
structural health monitoring. e system incorporated bit
ATMEGA MCU and TI CC RF transmitter. e
conditioning board equipped a bit, -channel, and low
pass-lter AD chip providing input oset dri as low
as 𝜇V/∘C and maximum sampling rate of KSPS. e
sensing element selected was P strain indicator having
aresolutionof.V/𝜇𝜀. In , to further validate the
reliability of the star-topology WSN, recorded strain data
from aircra fore-undercarriage strength testing system were
implementedinAircraStrengthandResearchInstituteof
China [].
Inand,Armsetal.[,]adoptedcommer-
cially available Agile-Linkfamily nodes from well-known
Microstrain Inc. [] including SG-Link5(Wireless Strain
Node) which is used for the strain measurement of a Bell
M helicopter in USA Army. In , Arms et al. []
further explored time synchronized algorithm for helicopter
ight test and addressed tradeo between wireless link
quality, and power consumption, varying sampling frequency
from Hz to Hz, acquisition channels and network
size.
Generally, the WSNs for AMS mostly relied on active RF
transmission. Some researchers described specic wireless
passive devices under harsh conditions in aerospace vehicles.
In , Elmazria and Aubert [] proposed small, simple
androbust,andpassivesurfaceacousticwave(SAW)device
which is capable of enduring extreme and harsh conditions
such as high level of radiation and electromagnetic interfer-
ence, with temperatures up to ∘C. In , a series of
NASA’s robust passive chemical wireless sensors (e.g., MEMS,
SAW, RFID) [] presented for aerospace vehicles from
Space Shuttle, HyperX, and Helios would withstand high
temperature above ∘C.
In , as for online aircra impact damage monitoring,
Delebarre et al. [] sought to study a piezoelectric WSN with
energy harvesting ability, integrating a low-power consump-
tion microcontroller for detecting whether voltage value
arisingfromimpactreachesthresholdandWIFImodule
for Lamb wave transmission. Liu et al. [] and Yuan et al.
[] further explored a light-weight ( g) and low-power
( mW) piezoelectric-based wireless digital impact node
(WDIM) for damage subregion location. In this study, the
wingboxspecimenforWDIMperformancetestinghadthe
dimension of × × mm3,withPZTsensors
connected to WDIM covering monitoring range of ×
mm3.
In , starting from previous work proposed by Yuan et
al. [], Gao et al. [] rstly developed a new double-radio
relay node (D-RRN), extending the monitoring distance
without degradation of wireless link quality. e M-RSN []
and D-RRN used in an multihop WSN allowed for achieving
maximumdatathroughputrateof.Kbpsforaircra
health monitoring system. For proving the performance,
reliability, and synchronization precision of the WSN,
sensor nodes deployed on aircra wing box and an UAV
composite wing was capable of implementing strain sensor
data acquisition on simultaneous communication channels.
A comparison of the strain results from evaluation with
counterparts obtained using wired optic strain in respective
location showed a maximum dierence of % in the two sets
of data.
Due to minor damage detection ability of Lamb wave
produced by piezoelectric sensors for AMS, piezoelectric
sensors showed great potential promises for online aircra
structural health monitoring. For this reason, Gao et al. []
proposed new wireless piezoelectric sensor node made of
Wireless Communications and Mobile Computing
T : Summary of airborne wireless hardware prototypes.
Wu et al. [ , ]
Becker et al. [], Gao et al. [],
Arms et al. [–],
Demo et al. [], Samson et al.
[]
Gao et al. []
Pook et al. []
Kiepert et al. [, ], Hall et al.
[, ]
Liu et al. [],
Yuan et al. [ ] Lu et al. []
Computing specications
Processor Atmel AVR
ATMeg a L
Texas Instru m e nts
MSPF
Texas Instru m e nts
TMSF Atmel ATUCA Altera Cyclone II
EPCQc
Texas Instru m e nts
CCF
Clock speed MHz MHz MHz MHz MHz MHz
Bus size -bit -bit -bit -bit -bit -bit
Program memory KB KB KB KB KB KB
Data memory KB MB KB KB KB
Data acquisition specications
Sensing type Strain gauge Strain gauge Piezoelectric
CO, CO, pressure,
temperature,
and humidity
Piezoelectric MEMS
A/D channels
A/D resolution -bit -bit -bit -bit -bit
Wireless specications
Radio ChipCon CC ChipCon CC Atmel
RF ChipCon CC ChipCon CC iMA
Frequency band MHZ . GHz . GHz . GHz . GHz . GHz
Outdoorrange m m m m m m
Data rate . Kbps Kbps Mbps Kbps Kbps Kbps
Wireless Communications and Mobile Computing
T : Comparison of the characteristics features for airborne wireless prototypes.
Study (-) roughput
(Kbps)
Max. sample
rate (Hz)
Synchronization
accuracy (us) Target application (-)
Wu et al. [] - Strain monitoring for aircra carbon ber reinforced plastic wing
box
Wu et al. [ , ] -
Liu et al. [],
Yuan et al. [ ] - - Impact monitoring for aircra wing box
Delebarre et al.
[] - - - Impact monitoring for aircra wing box
Gao et al. [] Strain monitoring for aircra wing box and an UAV composite
wing
Gao et al. [] - Aircra aluminum plate
Loo et al. [] - - Aircra corrosion monitoring
Pook et al. []
Kiepert et al.
[, ]
- -
Becker et al. [] - - Laboratory environment
Zhao et al. [, ] - Ultrasound monitoring for aircra wing inspection
Demo et al. []
Samson et al. [] - Aircra corrosion monitoring
Hall et al. [, ] - Aircra corrosion monitoring
Arms et al. [–] Strain and vibration monitoring Bell Model helicopter
Blanckenstein et al.
[] - - RSSI and BER for Airbus A-
Lu et al. [] - - Lab environment
base board, radio board, and conditioning board. e base
boardinstalledadigitalprocessorof-bitTMSF
and -bit ADC with conversion rate of up to MHz. e
radio board integrated the commercially available Atmel
SMART SAM R board with data rate of Mbps. e condi-
tioning board embedded an operational amplier (Op AMPs)
AD with low noise ( nV/maximum), low bias current
( pA/maximum), and low oset voltage ( uV/maximum)
forthesakeofmaximizingthefeaturesofthe-bitADC.
e tests were implemented on an aircra aluminum plate of
mmthickness.
In , Lu et al. [] developed a WSN system powered
by piezoelectric energy harvester, integrated CCF
System-on-Chip (SOC) having enhanced core MCU
architecture and low-power iMA radio RF module. e
SOC included -bit resolution multiplexed ADC with con-
versionrateofuptoKHz,whilethesensorboardinstalled
three-axis LISDH accelerometer having a resolution equal
to 9.65 ⋅ 10−3 m⋅s−2, ultracompact digital pressure sensor
(LPSH), and digital humidity sensor (HDC). e
accelerometer output signal fed -bit ADC having a resolu-
tion equal to 1.22 ⋅ 10−3 m⋅s−2.epresenceofsensorswas
used for correlating the eect of the aircra environmental
condition to the eciency of energy harvester.
In , Becker et al. [] presented a kind of WSN
node for aircra strain measurement. e prototype, relied
on TI MSP MCU having sampling rate of Hz and
ChipCon for RF transmission. e sensing element,
P strain indicator, provided a resolution of . V/𝜇𝜀.
A high-performance power management system was driven
by thermoelectric energy harvesting device. All tests for the
node were implemented in laboratory environment.
In , Loo et al. [] and Kiepert and Loo []
developed st and nd generation sensor nodes for aircra
cabin environment monitoring in more than commercial
ights. ey incorporated -bit Microchip PICF with
the million-instructions-per-second (MIPS) architecture and
maximum operational frequency of MHz. In , starting
from previous design, Kiepert et al. [] and Pook et al.
[] updated the existing version with -bit AVR micro-
controller having Dhrystone MIPS (DMIPS) architecture.
Compared to the previous version, new prototype lied
computational capabilities and reduced power consumption.
In laboratory test, two prototypes measure carbon dioxide
(CO), relative humidity, temperature, atmospheric pressure,
and sound intensity. e system was tested over a period of
hours, close to the longest time during active commercial
ight.
In , Demo et al. [] and Samson et al. [] pro-
posed Luna’s wireless sensing platform for aircra cabin
environment monitoring. e based board embedded an
ultralow-power MSP MCU with -bit A/D converter.
e communication board installed Zigbee module based on
IEEE . protocol. Meanwhile the analog board provided
transducer excitation and signal conditioning for all sensors,
in stark contrast to previous versions used in aircra cabin
environment monitoring [–, , ].
Wireless Communications and Mobile Computing
In , Hall et al. [, ] developed an in-home air qual-
ity (IHAQ) node integrating dierent sensors in aircra cabin
environmental conditions related to air quality, health, and
comfort and utilizing a Zigbee radio modules. For proving the
capability of the IHAQ node, the prototype was tested during
hours in single-family home including three dierent sensor
nodes deployed, showing that IHAQ node detected CO,
carbon monoxide (CO), humidity, temperature, pressure,
and sound with resolution of ppm, . ppm, .%, .∘C,
Pa, and . dBA, respectively. ese parameters met the
requirements of the National Children’s Study Program.
In , Blanckenstein et al. [] proposed a wireless
sensor node consisting of -bit ARM CM EFMGF
MCU and ATRF . GHz IEEE .. transceiver
having data rate of Mbps which is sucient for transmitting
high volume of sensor data. e SHT chip, humidity
and temperature sensor, was chosen as the sensing element
having resolution of about .% RH and .∘Cforhumidity
and temperature, respectively. e prototype was evaluated
with -node WSN deployment on Airbus A- for
measuring Received Signal Strength Indication (RSSI) and
Packet Error Rate (PER).
3.2. Comparative Studies of Existing Network Communica-
tion Schemes in AWSN. Ecient medium access control
(MAC) scheme and routing protocol are highly required in
AWSN. Many transmission protocols have been proposed
for WSN. Demirkol et al. [] listed the advantages and
disadvantages of existing main MAC protocols (S-MAC, T-
MAC, DMAC, SCP-MAC, and LMAC), typically providing
network communication and routing algorithm for WSN.
Nevertheless, these protocols are proposed from a general
insight into WSNs, rather than consideration for aeronautics
specic application requirements. For instance, in S-MAC
protocol, a packet delivery ratio (PDR) is evaluated less
than . and time latency ranges from s to s under one
packet for one node per second, which cannot satisfy the
PDR and delay requirements of WSNs in AMS. Additionally,
other requirements, high-precision time synchronization,
and real-time data aggregation, demand further research
on developing more suitable MAC protocol for AMS [].
Typically, WSN MAC protocols served for AMS mainly fall
into two categories: collision-free protocol (e.g., carrier sense
multiple access (CSMA)) and contention-free protocol (e.g.,
time division multiple Access (TDMA) protocol).
e feasibility and reliability of collision-free protocol
on the basis of IEEE .. standard in wireless links
forAWSNarefullyconsidered[,].Asformost
collision-free protocols, Zigbee protocol is typically adopted
as communication technique [, ]. Two standards [],
wireless HART from the HART Communication Foundation
and ISA.a from the International Society of Automation
(ISA), are introduced into WSN in aircra application due
to root from the IEEE .. standard and its mature
application in industrial control eld. In , by simulating
Zigbee protocol with hierarchical WSNs topology on OPNET
modeler platform, Notay and Safdar [] found that data
dropped, time delay, and throughput parameters are cor-
related with number of nodes and distance among nodes.
Results demonstrated that most MAC protocols based on
CSMA still suer from high collision rate and transmission
unfairness between the nodes. To improve the performance
of original CSMA protocol, researchers tried to employ
intelligent algorithm for facilitating transmission eciency
operation of CSMA protocol. Barcelo et al. [] developed
channel and delay allocation (CDA) algorithm together with
slotted CSMA protocol for aircra conditioning monitoring,
the verication of which was implemented by employing
nodes uniformly deployed along the body and the wings in
columns. Akkarajitsakul et al. [] studied the feasibility
of game theory coupled with CSMA protocol, for mitigating
collision issues (e.g., packet loss, system throughput, as well
as fairness). e GTMA protocol, a new algorithm proposed
by Chowdhury et al. [], also linked game-theoretic strategy
to CSMA algorithm. Results from simulation indicated that
GTMA protocol outweighed original CSMA protocol in
terms of packet collisions, data throughput, and short-term
fairness between the contending nodes. To fully investigate
theimplementationofgame-basedCSMAprotocolindif-
ferent real aircra wing structures, Krichen et al. [, ]
put forward various sensor deployment simulation models
for specic types of aircra wing and conducted respective
network topology. Simulation framework constructed by
Matlab soware was performing in the deployment of
sensor nodes over aircra wing of m × m dimensions to
monitor vibration and detect utter phenomenon. ey also
remarked that game-based CSMA was superior to original
CSMA protocol in respect to transmission delay, packet loss
ratio, and data throughput.
AlthoughanumberofimprovedCSMAprotocolshave
been proposed for WSN in AMS, they still suer from
many issues due to persistent collisions. e contention-free
protocol such as TDMA algorithm provides better perfor-
manceonlatency,jitter,andspatialreusethanimproved
CSMA protocols. Meanwhile, to achieve high accuracy or
high resolution of damage localization in aircra structure,
high-precision data synchronized acquisition in AMS should
be underlined. To cope with these issues of star-cluster
WSNs, Zhou and Jing [] proposed MAC layer based
on improved TDMA technique for AMS wherein the time
slot length was exibly allocated and adjusted according to
sensor nodes trac. Sensor nodes evaluated relative dri
and oset and adjust their clock according to the clock
embedded in synchronous message from cluster head at
certain cycle. Simulations compared the performance of S-
MACprotocol,TDMAprotocol,andmodiedTDM,proving
that improved TDMA protocol has obvious advantages in
termsofpacketlossrate,timedelay,andenergyconsumption.
Furthermore, due to the linear multihop WSN topology
commonly adopted in active airow control, Omiyi et al.
[] developed TDMA-based hop-by-hop WSN converge-
cast scheduling strategies including serial line scheduling
(SLS) and parallel line scheduling (PLS) algorithm, wherein
sensor and actuator nodes were categorized into several linear
clusters with each cluster consisting of 𝑋nodes uniformly
space along a straight line. e PLS was to maximize the
number of linear clusters communicating in parallel, while
SLS attempted to raise speed of per cluster sensor data
Wireless Communications and Mobile Computing
delivery by increasing the number of nodes per cluster. To
further boost the network performance of convergecast delay
and tradeo between latency and energy consumption, Dai
et al. [] proposed TDMA-based hybrid line, CSMA, with
frequency division multiple access (FDMA) for large-scale
aircra health monitoring scheduling (HLS) protocol for air
ow control. Results showed that HLS contributed to %
reductionindelayandenergysavingatmoderatelyhigh
sensor densities. To access more nodes into network, in
and , Arms et al. [–] proposed a novel protocol
integrating TDMA. strain sensing nodes can access
network at Hz of sampling rate for each node on a single-
radio channel. If FDMA was enabled and channels in
. GHz were open, the system can support maximum of
strain sensing nodes at Hz sampling rate. Additionally,
in , Blanckenstein et al. [] constructed a WSN with
sensor nodes deployed in Airbus A-. A robust and
scalable TDMA protocol without multihop and ARQ-based
retransmissions was proposed for guaranteeing maximum
delay to maintain high channel utilization.
In Table , AWSN communication schemes are analyti-
cally compared based on types of platform, target application,
reliability, energy eciency, real-time transmission, topology,
framework, and data ow as attributes.
4. Research Challenges
In recent years, more practical WSNs including system design
and network protocol have been successfully proposed for
AMS, evaluating safety of aircra ight and engine control,
the quality of aircra cabin environmental condition, and
the exibility of aircra structural health. Nevertheless, there
is room for improvement of WSNs for AMS. More work is
needed to allow WSNs to fulll the requirements for large-
scaleAMS.Oneimportantchallengeresearchersarenow
facing is turning the sensor node from ordinary data acquired
device into online data processing intelligent “brain,” making
the WSN more powerful and ecient [, ]. Additionally,
emphasis should be laid on the improvement of power supply,
data transmission reliability, and network bandwidth for
WSN.
We should also identify how the achievement of high-
accuracy time synchronization for large-scale AMS (espe-
cially in aircra structural monitoring) is attained. e
accuracy of time synchronization is closely correlated with
network protocol and transmission bandwidth, sampling
rate, and other factors. A large amount of data transmis-
sion results in network congestion, lower synchronization
quality, and higher power consumption, whereas smaller
data amount might reduce the accuracy of data analysis and
increase network delay. It should be highlighted that tradeo
algorithm among the amount of data transmission, syn-
chronization and network quality, and power consumption
needs to be explored. More energy facilitates maximization
of operating time for WSN. However, the battery life of
WSN is too short for performing long-term AMS. Energy
harvester [] from ambient energy sources might be an
eective approach and strategy to resolve this issue for power-
constraint sensor node. For abovementioned reasons, many
researches will be committed to the construction of scalable
networks by developing more eective energy harvests and
control algorithms, reducing the amount of transmitted data
and raising accuracy of network synchronization [].
Nonsafety critical systems (e.g., wireless smoke and
re detection system, cabin emergency wireless-controlled
lighting system) have been certied by the Federal Aviation
Administration (FAA) which is typically operated in an
unlicensed spectrum [, ]. It has been simply proved
that noninterference might exist between the regular wireless
communication in aircra electronic equipment and the
wireless frequencies at . GHz or sub-GHz band in AMS
[]. e recent study over the last few years focuses on
the possible challenges and nds suitable solutions for Wire-
less Avionics Intra-Communications (WAIC) at . GHz
or GHz. In future, more strict regulations are desired
for executing to ensure that no interference existS among
portable wireless electronic devices, equipped airplane radio
transmitters, and WSN in AMS. Furthermore, future aviation
standard must ensure that the WAIC, AWSN, and RAs
operating cofrequency are able to coexist. With the rapid
development of the Internet-of-things (IOT), it is predicted
that billion devices will be connected to the cloud (mainly
via wireless links) by []. It is promising that new
IOT systems such as IoT [], the IoT ARM [], and the
ISO/IEC IOT architecture [] with special frequency band
will be possibly applied in the AWSN in future.
5. Conclusions
e AWSN is a promising technology which plays an increas-
ingly key role in the AMS applications. However, few AWSN
surveys consider its application background in AMS, so this
is motivation of this review. e AWSN creates a new set
of challenges in terms of accuracy, real-time, reliability, time
synchronization, throughput, longevity, and safety and secu-
rity.Inthispaper,abriefsurveyofAWSNandAMSincluding
a range of areas from the general to the specic. e survey
focuses on the prototype architecture and network com-
munication schemes of the developed AWSN in AMS. It is
outlined in this review that the node architecture and network
communication schemes proposed in the early st century
are being developed and updated continuously, providing
amountsofsolutionsforAMS.erstAWSNprototypes
employed low-processing MCU coupled with low-resolution
ADCs, but technology makes sophisticated sensing and
conditioning elements available. Recent modications and
improvements made on board’s MCU, RF transceiver, signal
conditioning unit, and time synchronization show how WSN
becomes mature gradually for applied in AMS. We further
demonstrate that wireless piezoelectric platform based on
Lamb wave algorithm shows great potential promises for
online AMS. Besides, most traditional network communi-
cation schemes cannot satisfy all requirements of real-time
AWSN in AMS. Intelligent algorithm (e.g., game theory) or
machine learning might act as a new approach to resolve
issues intrinsic to these protocols. ese ndings not only
provide theoretical evidence and appropriate solutions to
WSN design according to AMS requirements but also novel
Wireless Communications and Mobile Computing
T : Comparison of network communication schemes for AWSN.
Study
(-)
Platform
(-)
Target application
(-) Reliability Energy
eciency
Real-time
transmission Type of network Framework
(-)
Data ow
(-)
Krichen et al. [, ]
Chowdhury et al. [] Matlab Vibration monitoring
of aircra wing Medium Medium √Homogeneous CSMA-CA and
game theory Continuous
Barcelo et al. [] Experiment Aircra condition
monitoring Medium Medium √Homogeneous CDA and
CSMA-CA Event based
Ma et al. [], Pook et al. [],
Kiepert et al. [, ], Hall et
al. [, ]
Experiment Aircra cabin environment
monitoring Medium No √Homogeneous CSMA-CA Event based
Guanglin and Hongyu [] NS Aircra vibration
monitoring Medium No √Homogeneous Zigbee/CSMA-CA Event based
Notay and Safdar [] OPNET Aircra condition
monitoring Medium No √Homogeneous Zigbee/CSMA-CA Event based
Ma et al. [], Park et al. [] OPNET Air ow control for aircra
wing and body High High √Homogeneous TDMA Continuous
Zhou and Jing [] OPNET Aircra health monitoring High High √Homogeneous TDMA Continuous
Ma et al. [] OPNET Vibration monitoring of
aircra wing High High √Homogeneous TDMA Continuous
Arms et al. [–] Experiment Bell Model helicopter High Medium √Homogeneous CSMA, TDMA
and FDMA Continuous
Blanckenstein et al. [] OPNET Airbus A- High High √Homogeneous TDMA Continuous
Wireless Communications and Mobile Computing
insights into airplane prognostics and health management
(PHM).
Conflicts of Interest
e authors declare that there are no conicts of interest
regarding the publications of this paper.
Acknowledgments
is work was supported in part by Nanjing University of
Science & Technology under Research Start-Up grant (no.
AE/).
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