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No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System Design


Abstract and Figures

There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-time actuation of IoT equipment, and activation of emergency alarms through the inference of a user’s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information.
Content may be subject to copyright.
Received September 30, 2020, accepted October 25, 2020, date of publication November 4, 2020,
date of current version November 17, 2020.
Digital Object Identifier 10.1109/ACCESS.2020.3035812
No Soldiers Left Behind: An IoT-Based
Low-Power Military Mobile
Health System Design
1School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
2School of Industrial Engineering and Systems, Amirkabir University of Technology, Tehran 1591639675, Iran
3School of Nursing and Midwifery, Edith Cowan University, Joondalup, WA 6027, Australia
4School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
Corresponding author: James Jin Kang (
ABSTRACT There has been an increasing prevalence of ad-hoc networks for various purposes and
applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Net-
works (WBAN) which have emerging applications in health monitoring as well as user location tracking
in emergency settings. Further applications can include real-time actuation of IoT equipment, and activation
of emergency alarms through the inference of a user’s situation using sensors and personal devices through
a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers
and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to
conserve battery power for sensors and equipment which transmit data to a central server. An inference sys-
tem can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption,
however this could result in compromising accuracy. This paper presents a framework for secure automated
messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst
maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the
battery power of devices such as wearables and sensor devices. The results for this system showed a data
reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods.
Authentication accuracy can be further enhanced with additional biometrics and health data information.
INDEX TERMS Multilayer inference algorithm (MIA), multilayer inference system (MIS), mobile health
(mHealth), wireless body area network (WBAN), low power wide area network (LPWAN), emergency alarm
notification, military mobile (health) network.
Significant research in the area of mobile health (mHealth)
has been conducted including its applications and infor-
matics. Internet of Things (IoT) and Low Power Wide
Area Networks (LPWAN) networks are now emerging to
replace previous sensor networks and technologies. These
two networks together provide a suitable solution for military
applications as they can satisfy the unique requirements for
military mobile networks which needs to be adaptable to
changing and often unpredictable environmental conditions
The associate editor coordinating the review of this manuscript and
approving it for publication was Francesco Piccialli.
and needs. Computational and battery constraints remain a
challenge for military mobile network sensors and devices as
many use portable batteries which have power constraints.
To mitigate these problems, this paper proposes a Multi-
layer Inference System (MIS) to build a framework for mil-
itary mobile networks. The following sections describe an
overview of the motivation, problem statement and the chosen
approach with methodologies used to develop the solution.
The Australian Department of Defence established the
Human Performance Research network (HPRnet) focus-
ing on the enhancement of military personnel, including
201498 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see VOLUME 8, 2020
J. J. Kang et al.: No Soldiers Left Behind: An IoT-Based Low-Power Military mHealth System Design
outcomes of research in soldier performance management:
Modelling of load, adaptation and performance. This is
inclusive of the ‘assessment and description of the phys-
ical demands and physiological responses of soldiers dur-
ing military training and prediction of soldier outcomes
and responses using wearable sensor data and psychometric
inventories’ [1]. Additionally, a separate research program
titled The Fight Recorder studies ‘the use of a small, light and
robust emergency beacon unobtrusively worn by the combat
soldiers to capture the data required for incident investiga-
tion, insight into the demands of military service in a deployed
environment.’ In this application, there is an emergency bea-
con that is activated by the wearer or a medical professional.
When this occurs, the device connects to satellites and the
geographic location is transmitted which allows for location
of personnel in an emergency e.g. an evacuation [2].
The ability to monitor the health and well-being of sol-
diers during training operations and in real life missions
can provide crucial enhancement to mission performance
and success. Recent developments have occurred in the
field of Internet of Things (IoT) networks, where there
has been an explosion of wireless sensors and devices that
can connect to ad-hoc networks including Wireless Body
Area Networks (WBAN) and Low Power Wide Area Net-
works (LPWAN). A more specific term of Mobile Health
(mHealth) can be used when these networks and sen-
sors/devices are used in healthcare applications, such as
monitoring soldiers’ health status, though the aforemen-
tioned network types are not necessarily limited to such
WBAN and LPWAN networks feature several compo-
nents including: availability of network without reliance on
infrastructure-based networks e.g. Internet cables, cellular
towers; both short and long data transmission distances; sen-
sor devices; long-lasting batteries; and, smaller data size.
In an mHealth system, sensors (implanted or attached to
the body) collect data about the user and transfers this to an
intermediary device, such as a smart phone or other smart
device. This is then forwarded to a cloud server where this
data can be processed or further analyzed or viewed e.g. by a
healthcare provider [3]. Two challenges exist when applying
an mHealth system to military applications and for use in
emergency situations. The first is that sensor data that is col-
lected may not be sufficient to accurately provide information
about the true health status of a soldier, and secondly, the
environment may not offer access to public networks such
as the internet or cellular networks for data transfer. Previ-
ously, activity recognition has been proposed as an additional
variable to help determine the validity of an emergency alarm
activation [4], [5]. The issue of reliance on public networks
can be overcome by existing network technologies, such as
Long Range (LoRa), NB-IoT and Sigfox [6]. An inference
system proposed in [7] which includes algorithms to assist
in situation determination can also be used in an mHealth
system for a military mobile network to improve the alarm
notification system accuracy.
In summary, it is crucial to observe the activity status
and health conditions of soldiers in the field. mHealth can
support the monitoring of soldiers’ health conditions and their
activity status in military networks using sensor (IoT) devices
that are available and affordable with low-cost networks
e.g. LPWAN. Smaller data packets can be used to transfer
health data information such as vital signs. An enhanced
Multilayer Inference Algorithm (MIA) can improve battery
life by further reducing data volume while maintaining high
accuracy through smarter energy consumption allocations.
This is achieved by reducing the frequency of transmission
data from sensors to a smart device which collects sensed data
and transmit to a server in the cloud. When data is transferred
by sensors, the most significant battery consumption is in
relation to radio communications, significant savings can be
made by reducing the frequency of transmissions.
This paper proposes a framework of military mHealth net-
works using a Multilayer Inference System (MIS) along with
various military applications using mHealth in conjunction
with LPWAN.
Automatic alarm notification abilities can be vital when mil-
itary personnel are in an emergency, such as being injured
or experiencing loss of consciousness. Despite the advance-
ment in technology and equipment being carried during mod-
ern field operations (illustrated in Figure 1), there are no
automated functions dedicated to raising alarms or sending
distress signals during an emergency. Furthermore, the load
carried by soldiers are weight-limited and devices requiring
battery packs can result in significant additional weight.
FIGURE 1. Devices and batteries used by a US soldier during an
operation – carrying a total of 70 batteries weighing at 7kg [9].
IoT networks and military mobile health networks using
inference systems that control data transmissions to the com-
mand office can be used to conserve battery power. Improving
the accuracy of data processing and pinpointing data selection
can extend battery life. In addition, this approach can reduce
logistical cost and reduce the overall weight of equipment
carried by soldiers.
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A centralized power supply for equipment can be one
option for improvement [8] such as consolidating compat-
ibility of battery types. However, another approach is to
reduce battery consumption of devices such as reducing the
frequency of radio transmissions using a smart algorithm that
can infer data. The following approach is considered:
Structured infrastructure networks such as cellular and
wireless networks are not reliably available in the com-
bat setting
In an emergency situation e.g. a soldier being rendered
unconscious, identifying the health status and location
of personnel is crucial
Monitoring personnel devices is important to check
availability, battery level, remaining equipment supplies,
and require conserving battery power using an algorithm
Remote control (actuation) of the equipment is not cur-
rently viable
An mHealth network converged with IoT networks collects
health data from users, and obtains device management data
such as sensor battery levels. This information is transferred
to a smart device within a WBAN, which will connect to
a gateway in an LPWAN as an ad-hoc node. The LPWAN
gateway transfers data to a regional headquarter server, which
further connects to central servers via internal (proprietary)
networks. Battery consumption is reduced by performing data
inference at the sensor nodes prior to transmission within the
The standard for efficiency and accuracy expectations are
predetermined according to application requirements. The
presented work is based on a framework that was proposed
in a previous publication [10], which proposed emerging
mHealth and LPWAN networks for military mobile networks.
This paper further extends this research with additional
insights and experiments by applying a novel multiplayer
inferencing algorithm. Five areas require integration in devel-
oping a solution to this problem:
mHealth network captures health data for monitoring of
personnel and equipment. It connects with IoT networks
through a smart device as an aggregation node, which will
communicate with a server in the command centre.
Alarm notification is used during an emergency using
Activity Recognition (AR). This function requires learning
the posture and activity of personnel to avoid false alarms.
Remote actuation of devices enables the ability to locate
personnel in an emergency. Search and rescue teams can
access and actuate devices to pin-point locations.
Security of the network includes identification of person-
nel using health data combined with biometrics for enhanced
multi-factor authentication.
Prediction of health conditions is critical to optimize
placement of personnel during mission planning.
Due to the nature of military operations, reliable infrastruc-
ture networks are not available for establishing an LPWAN.
Hence, ad-hoc networks are used consisting of devices from
other personnel in the local area with a dedicated gateway
device, which is then connected to a server in a regional
headquarter. Appropriate security measures are applied at the
gateway to protect the network. A WBAN gateway connects
to a gateway of a neighbouring WBAN through LPWAN,
which provides security functions with more computational
power and higher battery consumption. Devices operating in
a WBAN can include personal health devices that collect data
and transfer them to a smart device, which functions as a node
within an LPWAN. Military-grade wearables would also con-
nect to the same smart device for management and actuation
purposes. The following aspects need to be considered for a
military network setup:
Ad-hoc networks can be established with devices from
other personnel
A gateway in LPWAN collects data and connects to other
autonomous systems
WBAN has multiple routes to connect gateway devices
for redundancy
IoT/LPWAN network can be established to connect
mHealth sensors, IoT devices and gateways
A smart device plays a key role as a gateway for both
networks, i.e. WBAN and IoT
This paper contributes the design of an IoT-based low
power military mHealth system by emerging mHealth and
IoT/LPWAN with resolving the security issues for identifi-
cation of personnel and weaponry equipment via applying a
novel algorithm of a multiplayer inference system.
This section describes the related works, including the areas
of tracking location using health data and IoT devices and
network, wireless and mobile networks for military, inference
system, alarm notification for health status (using AR activity
recognition) and identification of personnel using health data
(biological, physiological and biometrics).
While the emphasis has been on tracking assets and direct-
ing potential changes during missions including changes in
route, or a change in destination, the rapid advancement of
technologies in this space has given rise to the concept of
the ‘‘connected soldier,’’ where IoT is used to integrate bio-
metrics, biomedical devices, environmental sensors and other
equipment (e.g. weapons) to monitor and enhance soldier per-
formance. In the field, the convergence of IoT and the military
battlefield is also known as the Internet of Military Things
(IoMT) or Internet of Battlefield Things (IoBT). The primary
goals of IoMT and IoBT are to: 1) improve the performance of
soldiers in the battlefield; 2) allow for rapid identification of
the enemy using Edge computing, and 3) identify and detect
real-time changes to health conditions. As Figure 1 shows,
soldiers have weapons systems and combat gear which are
embedded with a variety of sensors and computing devices.
These sensors/devices can capture real-time biometric data
including information from the user’s face (e.g. iris, peri-
ocular space, facial expressions), fingerprints, gestures, gait
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J. J. Kang et al.: No Soldiers Left Behind: An IoT-Based Low-Power Military mHealth System Design
and positioning as well as physiological data such as vital
signs [11], [12].
Technological advances in IoT and big-data analytics have
the potential for early detection, diagnosis, and treatment [13]
of a soldier’s health status while they are in the field. In emer-
gency situations, early detection of casualties and the ability
to predict potential outcomes from a combat-related injury
can assist triage and prioritisation for medical support. Aggre-
gate location tracking of soldiers could provide valuable
information about the live casualty status during a particular
mission in a geographical region. Unit commanders could use
this data to rapidly revise mission strategies and to deploy
more troops or medical staff to required areas [14]. Wear-
ables are frequently used in healthcare applications, and are
becoming used more frequently to remotely monitor a variety
of health-related physiological signs [15] including vitals
(blood pressure, heart rate, respiratory rate, temperature),
blood glucose monitoring, cardiac function monitoring, phys-
ical activity and exertion, sleep, and calories burned. IoT has
the potential for high-speed processing of big data, however
several issues exist including integration and communication
of IoT devices, real-time transmission, network challenges,
and issues facing remote geographical locations [16].
In addition to the logistical benefits of detecting casual-
ties and remote triaging, physiological monitoring of sol-
diers provides numerous possibilities in general performance
enhancement, and precision forecasting of soldier failure in
response to physical, psychological and environmental stres-
sors. Other benefits may include fitness and nutrition opti-
mization and the prediction of long-term health risks, which
allows for proactive medical management [14]. Presently
available commercial systems such as smart watches and their
proprietary algorithms limit their applicability for military
grade purposes. Machine learning algorithms need to be
developed based on the physiological measures which are sig-
nificant predictors for soldiers. The use of clinically relevant
information including soldier feedback of the context and
events that may have resulted in acute changes in health can
enhance activity recognition (e.g. injury). Dynamic Activity
Recognition (AR) uses sensor data to predict the movement
and positioning of a person, and has been primarily used to
monitor movement in indoor or enclosed environments such
as in smart homes. To integrate soldiers’ measures of health
data into military mobile networks, activity classification
and decision support systems [17] are required in order to
classify streams of health-related sensor data. These would
include measures of physiological, psychological (e.g. abnor-
mal behavioural responses) and environmental data (e.g. tem-
perature and location). For complex event reasoning, these
measures will need to be classified and contextualized based
on desired classifications, including soldier performance,
acute health events, forecasting soldier failure and long-term
health outcomes as well as situational context [18]. A rules
knowledge base can be used for complex event reason-
ing [17] where an X change in measure Y results in a health
event Z. After complex event reasoning, the classified data
can be integrated into the surveillance system along with
other potential classifications such as weapons recognition
and geospatial situation. Should the system detect an acute
anomaly, the notification can be sent using a surveillance sys-
tem interface and two-way decision-support system, allowing
the soldier to respond to the notification where possible.
A battlefield surveillance system is used to integrate mul-
tiple sensors and mobile devices. Several surveillance sys-
tems can then together form a surveillance network [19],
which is critical for communication, teamwork and opera-
tions planning. Wireless and mobile networks for military
use includes Integrated Tactical Networks (ITN) [20], [21].
Terahertz communication may have application to military
use of wireless and mobile networks. Terahertz (THz) fre-
quency band (0.1 - 10 THz) is a wireless radio communication
with potential for wide bandwidth and high-speed sensor
data transfer [22]. The host of sensors embedded in soldiers’
gear and weapons will result in significant volume of sensor
data requiring big data wireless cloud storage, making rapid
retrieval of analytics imperative. It could potentially optimize
telecommunications amongst soldiers, between soldiers and
coalition members, and between soldiers and command cen-
tres. In summary, the ability to track soldiers on the modern
battlefield is integral for soldier safety and mission success.
While there may be feasible options for networks and big
data storage that could already exist, the unique integration
of biomedical, biometric, and environmental monitoring in
an integrated surveillance system could optimize military
mobile networks significantly. However, to apply this to the
battlefield where soldiers’ health status can be actively mon-
itored and responded to for acute health situations, further
activity classification and decision support systems need to be
developed and complex event reasoning is required for accu-
rate notifications. Ideally, the activity classification system
should be a part of the integrated surveillance system.
Mobile ad-hoc networks have been used in many military
and non-military applications, including healthcare settings,
environmental monitoring, location tracking and activity
recognition. The application of mobile ad-hoc systems in
the healthcare industry is increasing with research focusing
on improving and optimizing these systems. Mobile ad-hoc
networks are useful for military applications due to their
characteristics of autonomy, flexibility, adaptability and self-
configurability [23]. Physical-layer performance of ad-hoc
networks can be improved by increasing the number of relay
nodes which increases spatial diversity [24]. Bands reserved
for military applications, such as radar are relatively underuti-
lized whereas spectrum bands for public cellular networks are
used much more heavily [25]. Therefore spectrum-sharing
opportunities and improvements are applied mainly in spec-
trum bands used for military applications [26].
A critical issue to address for military mobile networks
is optimization of Wireless Sensor Network (WSN) energy
usage. Mobile sinks are used to avoid the problem of WSNs
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J. J. Kang et al.: No Soldiers Left Behind: An IoT-Based Low-Power Military mHealth System Design
with static sinks. Various mobility patterns in the field of
sensor networks are used to increase energy efficiency and
apply improved data gathering strategies [27]. Naghibi and
Barati recently presented a solution to gather and deliver data
to the sink node with minimal energy consumption using a
wireless sensor network. To achieve this, mobile sinks were
applied to divide the network into geographical cells [28].
A user-feedback system has been used for activity recognition
to minimize the occurrence of false alarm notifications. The
use of such a system aims to decrease the frequency at which
data transmission occurs at sensors in WBANs [4], [29].
A novel method of activity recognition integration was devel-
oped to combine mHealth data of a soldier with vehicular data
to modify a vehicle safety system using a cloud information
system. This system is implemented by integrating the new
mHealth technologies with military vehicular applications
through WBAN sensors and devices [30]. Kang et al. also
developed an inference system based on short and long-term
health status prediction, inferred from health information.
This system, which is built using big data in the cloud, is use-
ful for preventing life-threatening situations [31]. This novel
solution utilized an inference system that reduced data trans-
fer to other networks from sensors, without the additional
burden from IoT on sensor devices. The goal in this method is
to infer data at the sensors to reduce unnecessary or redundant
data transmission at the first instance, as well as reducing
overall battery consumption that occurs with additional data
transfer [32]. The inference system was modified and fur-
ther improved with the use of beacon data points that are
transferred at set intervals, regardless of the initial inference.
This is to maintain a regular transmission of data at set time
intervals to improve data accuracy where the inference system
may not transfer data for a period, and the frequency can be
adjusted using variance rates [3].
An advantage of military mobile network converged with
IoT and LPWAN is to allow for tracking of users in the
field. For the purpose of location tracking using health data
and IoT devices and network, it is feasible to implement
a functional, low cost, low data rate tracking and locating
system of personnel and objects with low power consumption
based on the Zigbee/XBee technology [33]. They decreased
battery consumption and extended the battery life to be able
to work for 126 days with a battery capacity of 1000mAh.
Santos et al. [34] presented an IoT-based mobile gateway to
extract information about location, heart rate, and possible
fall detection of users or patients for mHealth scenarios
when a network of body sensor tracks a person and their
environment. Furthermore, this IoT-based mobile gateway
can instantaneously transmit the gathered information to a
caretaker Intelligent Personal Assistant (IPA) which allows
them to manage alarms and a group of actions in a timely and
effective manner. Further, Santos et al. proposed algorithms
for mobile gateway services as a communication channel.
They found three indices for accessing the algorithm, which
includes power consumption of devices, the accuracy of each
monitoring service available at the mobile gateway, and the
interoperability with other objects available on the environ-
ment. Patii and Iyer [35] proposed a reliable, low priced
wireless IoT based system or mechanism to monitor and
track soldiers on the battlefield as a solution to high noise,
installation cost and signal loss. This system features instan-
taneous data transmission, location tracking of soldiers, and
monitoring of physiological data such as heart rate, body tem-
perature and environmental oxygen levels. Lagkas et al. [36]
developed a new algorithm named Hot-Cold to locate mobile
monitored targets or individuals concerning the IoT system
in dynamic environments with ill-defined or ill-designed
infrastructure. This technique can guarantee the proximity
maintenance formed from the power of the output RF signal
sent by the individuals or targets to transmit its extracted data
from sensors indicating their location.
Due to the development of new mobile networks, smart-
phones have the capacity of running complex algorithms for
tactical support for low-ranking commanders and individual
soldier support. Tactical data (unit location, composition,
tasks) in dynamic mobile military networks can be utilized
to access information anywhere during the mission and for
providing practical support for decision superiority. Combat
decision support has been one of the required fields in the
military application using mobile ad-hoc networks [37]. They
focussed on designing quantitative methods and algorithms
for combat decision support using the mobile application.
Their analytical tools evaluate the usefulness of implement-
ing mobile applications for combat support, situation aware-
ness development, and in delivering augmented reality.
Another important field of using the mobile network is
health status alarm notification and activity recognition which
can be implemented using sensor nodes. This feature can
raise an alarm notification using sensed physiological data
and activity recognition sensors [4]. AR has become a crit-
ical research subject, along with personal real-time sensors
and mobile monitoring devices based on health information
and predicting health status [30]. These systems and sensors
require significant work to increase the accuracy of activ-
ity determination based on the situation. Some studies have
focused on increasing accuracy by increasing the number of
sensors in different locations of the body [38]–[40]. Some
studies in health informatics have developed alarm systems
using smartphones for simpler and smaller applications in
pill dispensers [41], intelligent pillboxes [42] and wound
assessment systems [43].
Leier et al. developed a human activity recognition and
fall detection algorithm, which can be used to increase the
safety of people in challenging working conditions. This
system uses real-time information of workers which can
produce alarms in the case of abnormal conditions [44].
Santamaria et al. developed a wearable device which can
collect data from different sensors and send them to a cloud
platform. The data are utilised for AR and health status
notification. They also added a fuzzy-based Human Activity
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Recognition (HAR) and tested different classes of data filters
to reduce the volume of data sent to the cloud [45].
Chmielewski et al. present an application of non-invasive
sensors for hosting data acquisition, filtering and analy-
sis [46]. Selected data sources and signals are utilized to
recognize and detect seizure symptoms in children. They
constructed a system based on electromyography and iner-
tial data which can be used for seizure detection. Hu et al.
recently developed an IoT and blockchain-based healthcare
system [47]. This system is mainly used for human activ-
ity recognition via monitoring vital/nonvital signals using
wearable-sensors. This method is used to recognize an activ-
ity done by a patient which can be utilized as programmable
alarms during a treatment period.
As well as location tracking of targets through a mobile net-
work, identification or authentication of personnel is another
essential and possible outcome of the mobile network con-
verged with IoT and LPWAN in terms of finding the intended
and right target (soldiers or any other form of targets) amongst
a group of individuals. Han [48] proposed a verification tech-
nology for hand-based personal authentication including two
hand-based features, hand geometry and a user’s palmprint
where Positive Boolean Function (PBF) and the bootstrap-
ping method adoption improved the performance. Beritelli
and Serrano [49] decided to use Phonocardiogram (PCG)
data to develop a system for identifying human as PCG
is specific and unique to each person. They also proposed
an individual identification study using analysis of cardiac
sound frequency of 20 people. The two-loudest heart sounds
detection algorithm proposed in this study played an active
role in frequency analysis and signal matching phases and
introduced a PCG sequence as a reliable physiological sign.
Girish Rao Salanke et al. [50] proposed a novel approach to
identify individuals using different and unique Photoplethys-
mography (PPG) signal of people in different states (stressed
and relaxed) based on Mahalanobis distance between wave-
forms. They showed that PPG is a useful metric as it is
impossible to be replicated easily compared to other biomet-
rics such as the face, voice and fingerprint. Gui et al. [51]
proposed an Electroencephalography (EEG) based frame-
work to prove human identification and authentication. They
not only decreased the noise level but also extracted some
frequency features using ensemble averaging and low-pass
filter, and wavelet packet decomposition respectively. Their
classification method was derived from an artificial neural
network which gave them 90 per cent accuracy of identify-
ing authorised individuals. Spanakis et al. [52] analysed the
voice acoustics and verified audio-visual identity for user
authentication in the form of a face and voice recognition
platform called SpeechXRays. They showed that a voice-and-
face-recognition-based biometrics platform is sufficient for
use as a valid and reliable entrance gate to sensitive data
accessibility in the eHealth industry. Kacer et al. [53] used
the FlexiGuar system as a base to develop a physiological
data measurement system for air force staff in order to predict
their physical and psychological situation. This system can
measure body temperature, heart rate and acceleration simul-
taneously to contribute to the prediction of dangerous state
identification for military staff. Su et al. [54] proposed an effi-
cient and functional multimodal (multi-biometric) personal
identification system exploiting finger vein and ECG signals
in an integrative manner. Their system successfully achieved
recognition accuracy and security evaluated by Receiver
Operating Characteristic (ROC) and Equal Error Rate (EER)
as two evaluation metrics. Aziz et al. [55] introduced a reli-
able, accurate and comparatively less expensive ECG based
biometric authentication system through denoising raw ECG
data and extracting interest regions from data using Empirical
Mode Decomposition (EMD). Furthermore, they extracted
some features including variance, skew, Shannon energy,
occupied bandwidth and median frequency which were then
classified using Support Vector Machines (SVM).
The human body possesses several traits and features
which are almost universally present yet unique. Some of
these traits, such as fingerprints, have been used historically
for identification/authentication purposes in various applica-
tions [56]. Since each biometric trait has its own advantages
and disadvantages, no singular biometric trait can meet the
requirements of all applications. Some common biometric
traits are briefly introduced below. The pattern of valleys
and ridges on fingertips are defined as fingerprints. Fin-
gerprints are one of the most reliable biometric traits for
human identification/authentication, hence the prevalence of
research papers and various applications which utilize this
feature [57]. The convenient and non-intrusive nature of facial
recognition methods has resulted in its popularity in many
applications. The features used for recognition can include
the eyes, eyebrows, mouth and nose shape [58]. The iris
patterns of any two eyes are independent due to the epigenetic
nature of iris patterns, and studies have found that even
identical twins have iris patterns which are unrelated [59].
In conclusion, we found that an MIA can improve the
longevity of battery power. Due to the computational limita-
tions of devices however, such a system needs to be designed
with efficiency in mind. Health data can be combined with
biometrics to improve authentication and user identification.
There is feasibility for ad-hoc networks to be used for military
mobile health network purposes with various applications.
This section describes the proposed solution, which includes
the mHealth network to be emerged with IoT and LPWAN
The mHealth network refers to a collection of inter-
networking devices and systems architecture used for optimal
collection, classification, and delivery of health information.
The journey typically starts from the users/patients (cap-
turing vital physiological signs), traversing across multiple
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FIGURE 2. Activity Recognition and Detection Support.
platforms, hops and nodes across the internet as shown
in Figure 3.
FIGURE 3. mHealth Network.
There are several technologies and protocols being used in
the mHealth space, which are optimized for various segments
of this end-to-end link, such as data capture, information
management, user-centric device/sensor functionality, and
protocol and data exchange. In this section, the focus is
placed on the Body Area Network (BAN) segment shown
in Figure 3. Other variants of BAN (xBANs), include Wire-
less Body Area Networks (WBAN), Body Sensor Networks
(BSN), and Medical Body Area Networks (MBAN). Inter-
net of Things (IoT) is the internet-aware feature of the
xBAN devices, which requires these devices to have certain
capabilities to not only route the health-driven data across
the network efficiently but also provide bidirectional feed-
back to the user/patients from the cloud/internet stakeholders
(e.g. remote doctors). The provisioning of mHealth devices
with IoT capabilities has brought healthcare services closer
to patients and users [60].
According to Nazir et al. [60], applications of IoT in
healthcare can be categorised into two main applications: Sin-
gle health condition and cluster health (greater than one) con-
ditions. The single main conditions are comprised of glucose
level, blood pressure, blood oxygen level, body temperature
and electrocardiography signal capturing.
The cluster condition applications are those dealing with
the wheelchair, rehabilitation, and smartphone intervention
healthcare solutions. The investigated IoT-based healthcare
services included Ambient Assisted Living (AAL), Internet
of mHealth Things (mIoT), mHealth Community Healthcare
(mCH), and mHealth Paediatric Healthcare (mPH).
The interconnection of IoT devices, as well as systems
and architectures belonging to the city infrastructure is often
categorised as a Smart City. The Smart City Paradigm aims
to manage devices and associated data to monitor and anal-
yse the interactions of urban stakeholders efficiently. These
include [61] people (smart citizens), smart energy, smart
buildings (including homes), smart mobility (traffic and
transportation), smart healthcare, smart infrastructure, smart
governance, smart education, and smart security.
The communication protocols used to interconnect
IoT-based devices and gadgets into the smart city com-
munication grids include Radio-Frequency Identification
(RFID), Near Field Communication (NFC), Low Rate Wire-
less Personal Area Network (LWPAN), ZigBee (and its
longer-range/higher throughput variant; ZBee), and IPv6 over
Low-Power Wireless Personal Area Network (6LoWPAN).
Military and defence applications fall within the domain of
the smart city paradigm, and they belong to a specific class
of high resilience devices and protocols that are expected to
operate under harsh natural conditions (e.g. low visibility,
high mobility, low bitrate etc.). Requirements for this class
of applications are discussed further throughout this paper.
MIA is used to improve the accuracy and efficiency of the
existing inferencing algorithm [7].
A two tier MIA is developed according to the following
1) Apply first inference algorithm (e.g. reduce 10,000 data
points (DP) ->1000 DPs)
2) Optimize (find more samples to reduce the gap between
the original and sample data points) (e.g. 1000 DPs ->
1100 DPs)
3) Apply second inference algorithm (e.g. 1100DPs ->
300 DPs)
4) Optimize (find more samples to reduce the gap between
the original and sample data points) (e.g. 300 DPs ->
320 DPs)
5) Finalise the sample DPs to calculate accuracy
rates (AR) and savings rates (SR)
The real-time monitoring of personnel and equipment in
the field is critical to ensure a successful military operation.
Command centres are concerned with conducting efficient,
precise and cost-effective warfare [14]. High value assets
such as soldiers, military equipment and vehicles need to be
monitored in real-time to enable precise manoeuvres in the
field. GPS positioning systems have well-known, relatively
simple applications to track the location of personnel and
equipment. However, complexities are inherent in monitoring
the physiological health of a soldier in the field. Real-Time
Physiological Monitoring (RT-PSM) can be useful for medics
and commanders to assess the physical and psychological
health and wellness of a soldier [62], [63]. For RT-PSM to
monitor and potentially forecast changes in health status,
sensors will need to be worn or attached to the body of the sol-
dier. Non-invasive, wearable, and tuneable electromagnetic
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multi-sensing systems have the potential to be used in sol-
diers. Electromagnetism (EM) is considered a leading tech-
nology in multi-sensor applications in health care and has
been used for glucose monitoring [64]. EM sensors can be
stretchy, adhering metallic devices that are able to radiate or
receive EM waves. The types of sensors and number of sen-
sors or monitoring devices will need to be determined based
on the number of physiological measures that need to be
collected [15] to allow the activity monitoring and decision-
making system to make precise and clinically-relevant infer-
ences about a soldier’s health condition. Precision health is a
concept used in medicine ‘‘to enable a new era of medicine
through research, technology, and policies that empower
patients, researchers, and providers to work together toward
development of individualized care’ [65]. Although techno-
logical advancements have enabled the collection of copi-
ous amounts of sensor data, the achievement of precision
health for the public continues to be elusive [65]. Predict-
ing or forecasting health-related outcomes requires unique
algorithms determined by the desired outcome measures
e.g. soldier’s performance during training, during combat
and long-term health outcomes such as chronic illnesses or
suicide risk. The physiological data of a soldier may be vastly
different on the battleground when compared to peace-time
training [14], [66].
Based on the health and device data processed by the infer-
ence algorithm, an alarm can be raised by the smart device
which sends an alarm to a server at a regional headquarter.
To minimize false alarms, the smart device prompts for a
confirmation prior to raising the alarm through a message
display on the device. A timeout is applied such that if no
response is detected within the time threshold, the alarm
is raised automatically. A situation determination process is
conducted by the pre-defined threshold table to determine
whether the condition of the user would necessitate an alarm.
Activity recognition is used to determine the posture or
motion movement of the user to assist in determining the
status of injury, level of consciousness or normal condition
of the user. Management alarms are raised for sensors and
device monitoring tasks such as low battery levels.
When a user is unresponsive to the confirmation message
sent on the smart device, hardware devices may also be acti-
vated to aid rescue personnel to locate the user. The user may
be equipped with some medical or emergency devices that
can be operated remotely. Pre-programmed devices such as
wearables may actuate automatically following the situation
determination process without remote operation. Devices can
include mHealth sensors for vital signs (heart rate, body
temperature, respiration rate and blood pressure), AR sensors
(such as accelerometers and gyroscopes) and smart devices
with GPS. Personal equipment is connected to WBAN for
management purposes to provide the status of battery level
and availability. For a rescue operation, remote actuation may
be fulfilled by triggering personal devices such as beacons to
aid the physical rescue team in locating the injured personnel
on the actual field.
It is feasible to use biometric data of a soldier for identifica-
tion purposes, which can be used to activate and deactivate
weaponry equipment to prevent an adversary from acquir-
ing and using them. As shown in Figure 4, biometric data
can be extracted from two types of biometric traits, namely
physiological traits such as the iris, fingerprints, and facial
patterns and behavioural traits such as keystroke recognition
and voice. There are seven properties of a biometric trait that
determine whether it can be used for a biometric application
with specific requirements [56]. These seven properties are:
FIGURE 4. Examples of some physiological and behavioural biometric
1) Uniqueness: this is the most important requirement for
a biometric trait. The biometric traits that can be used
for verifying individual identity must differ from one
individual to another so that they can serve as that
individual’s unique identification (ID) [67].
2) Universality: biometric features should be universally
present across most people. Very few people may not
have certain traits in rare circumstances.
3) Permanence: the trait should be constant over a long
period of time.
4) Measurability: a biometric trait should be easy to cap-
ture without demanding significant time and cost.
5) Performance: the recognition accuracy requirement
imposed by an application should be met. To achieve
this, a biometric trait should exhibit small intra-user
variability and large inter-user variations. This means
that features extracted from the same individual by
multiple acquisitions should be similar, while features
extracted from different individuals should be different.
6) Acceptability: individuals should be willing to use the
trait in biometric applications.
7) Circumvention: it should be difficult for a biometric
trait to be replicated.
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TABLE 1. Comparison of some biometric traits in terms of seven
properties (adapted from [56]) ‘H’ =high, ‘M’ =medium, ‘L=low.
FIGURE 5. Extraction of the iris features from an iris image (adapted
from [70]).
Several commonly proposed and used biometric traits have
been outlined in Table 1 in relation to the seven properties.
The iris has low acceptability, but can offer high universality,
uniqueness and performance. In recent years, iris recogni-
tion has become an active scheme for personal recognition
because of the high reliability and uniqueness that iris pat-
terns can provide, especially when high recognition accuracy
is required [68]. The human iris is the annular part between
the pupil and the sclera, as shown in Figure 5, and is consid-
ered one of the most reliable biometric traits [69]. Military
applications have strict security requirements and therefore
require high recognition accuracy. Iris-based recognition sys-
tems usually outperform systems using fingerprints or face,
thus in this paper, we present an iris recognition system as
an example for showing how to perform personal recognition
with the iris – this approach is based on [70].
Unique iris patterns can be extracted from the digitized
image of the eye using image processing techniques and
encoded into a feature vector, which can be stored in a
database as the template. If a person wants to be authenticated
by the iris authentication system, their eye image is first cap-
tured. Then a feature vector is extracted and compared with
its claimed template in the database. If the similarity between
them is larger than a predefined threshold, then a matching
report is given and the authentication is successful [59].
To achieve highly accurate recognition of individuals, dis-
criminative features in an iris pattern should be extracted.
Generally, there are a few stages to extract the iris features,
including segmentation, normalization, and feature encod-
ing [59]. Specifically, in the segmentation stage, the actual iris
region is isolated in a given eye image as shown in Figure 5.
In the meantime, artefacts such as specular reflections within
the iris region should be excluded with proper technique.
In the normalization stage, the iris region is converted into
fixed dimensions, such that even if two iris images of the
same eye are captured under different conditions (e.g. vari-
ous imaging distance, different eye positioning or rotation),
features at the same spatial location can be extracted. In the
feature encoding stage, from the normalized iris region,
a number of feature encoding algorithms, e.g., wavelet encod-
ing, Gabor filter, 1D wavelet, Haar Wavelet, Laplacian of
Gaussian filter, can be used to extract iris features, which are
used to perform intra-class matching (comparing iris images
from the same eye) and inter-class matching (comparing iris
images from different eyes).
In this study, the VeriEye SDK [71] is used to extract
the iris features from the iris images. The feature vector
extracted from each iris by the software includes 2348 integer
values I=[I(1),...,I(i),...,I(2348)], where iranges from
1 to 2348 and each integer value I(i) is in the ranging from
0 to 255. As in the method used in [70], each of these integer
values is quantized with the following equation,
BI(i)=floor(I(i)/S1) (1)
where S1is the quantization step size and is set to be 64 in
this application; floor() is a function that rounds a value to
the nearest integer towards minus infinity. By applying equa-
tion (1), the value of BI(i), can be 0, 1, 2, or 3, of which its cor-
responding binary representation is ‘00’, ‘01’, ‘10’, or ‘11’,
respectively. By concatenating all the binary representations,
e.g., BI(i), a binary feature vector Fwith a feature length of
L=4696 bits can be produced from each iris image and can
be used for matching. In the matching process, the similarity
between the template feature vector FTand the query feature
vector FQis calculated by equation (2) as:
where F(k) represents the kth element of Fand Fis the mean
value of F. Moreover, Trepresents template and Qrepresents
the query. The authentication is considered successful if the
value of Sis greater than a predefined threshold.
Multi-factor authentication uses biometrics in combination
with health data information to increase accuracy and secu-
rity. Single-factor authentication is less secure and not ideal
for a military application. For example, compromised bio-
metrics for an individual could compromise access to poten-
tially dangerous equipment and personal health information.
The smart device holds the algorithm to determine person-
nel identification using a data set that includes predefined
threshold data. In addition, it is necessary to have a safeguard
that would override normal authentication procedures in an
emergency, such as accessing restricted information by using
a pre-recorded higher-level access password.
There are two types of health data for experimentation:
1) Physiological data (e.g. vitals) and 2) Biometrics data
(e.g. retina and fingerprint pattern.)
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The mHealth network provides a data inference system to
reduce the frequency of data transfer and conserve battery
power of sensor devices, which is critical in mHealth security.
This has been further enhanced using MIS, which applies
inference algorithms multiple times to increase the accuracy
and efficiency by adding a process of optimization.
Health data combined with biometrics increase the accu-
racy of identification with multi factor authentication.
This section describes the process of optimizing the data
set and reducing data size through the application of the
inference system. As previously stated, different inference
systems such as applying variance rate, removing duplicate
data and using beacons have been studied in various experi-
ments and applications [7], [10], [11]. A practical inference
system can extract a smaller volume of data, while ensuring
that the gathered data continues to represent the original data
set. In this study, the MIS could analyze the sensed data
variance and compare it with predefined threshold measures.
As health data will vary between individuals based on their
age, gender, health conditions and status, the threshold is
defined by a health practitioner who examines the user and
defines a personal threshold.
Primary physiological data includes heart rate (HR), body
temperature (BT), respiration rate (RR) and blood pressure
(BP). HR is more amenable for experimentation and in pro-
viding a rich data set, as it can change rapidly and is sensi-
tive to changes in the body and psychological state. Large
quantities of HR values can be produced within a specific
time; hence it has been chosen for use in the study’s inference
The main objective of the inference system is to decrease
the frequency of transmission in sensor nodes as well as
to conserve battery consumption which is vital in military
applications. Such an inference system can be implemented
in military systems by not transferring unchanged or almost
unchanged data and the data that does not affect the data
analysis step. In this study, we use a MIA, including maxi-
mum and minimum points (relative extrema) of heart rate data
points in each trend of heart rate data set, and optimize the
sensed data set in two layers. The MIA can optimize the data
set through different layers and steps to decrease the number
of data samples from the original data set.
The MIA also uses beacons which are samples taken at set
intervals to modify the distortion of inferred data as much
as possible [7]. These intervals can be classified to the short
and long interval for exercise mode based on every second,
and for normal or non-exercise mode based on every minute,
The MIS developed in this study includes two inference
layers consisting of four different steps to achieve efficiency
with a high savings rate:
1) The first layer of the inference system extracts data
from the original data set based on the maximum and
FIGURE 6. Graphical concept of SU and SL.
minimum points and the 60-seconds beacons period.
Figure 6 shows the real value in comparison to the
inferred value graph, which is derived from applying
the inference system.
2) The first layer optimizes the extracted data set from
Step 1 by adding additional valuable data to the inferred
data set to minimize the gap between the original and
inferred data points. This process will increase the
accuracy, however, will reduce the savings rate as a
3) The second layer of the inference system extracts data
from the previous inferred data set in Step 2 using a
predefined variance rate. This step improves the sav-
ings rate; however, the accuracy rate will decrease.
4) The second layer optimizes the extracted data set
from Step 3 by adding additional valuable data to the
inferred data set to increase algorithm accuracy. This
step improves the accuracy rate. As a result, the accu-
racy rate improves significantly whilst the savings rate
is slightly reduced (as shown in Figure 7). The sav-
ings rate increased by 10.3% whilst the accuracy rate
decreased by 0.91% against the baseline results as
shown in Figure 7(a) and inferred results in Figure 7(d)
using 1000 data points.
FIGURE 7. Progress trend of inference system after each layer of
extracting data in a heart rate data set. (a) applying Max. and Min. points
(b) extracting points with the specific gap between original data and
inferred data graphs (c) applying variance rate between inferred data
(d) extracting points with the specific gap between real data and inferred
data graphs.
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In the first layer of the inference algorithm, it compares a
data point with a previous one; if there is a positive differ-
ence between those data points (i.e. increasing rate of HR),
the MIA will continue comparing a data point with a prior
one, until there is no sign of the increasing trend and a positive
difference. This stopping point (relative extrema) will be
transmitted as an inference value, and then the system con-
tinues with the same comparison. This system can transmit
all the maximum and minimum points and covers all the time
periods in this step. Then the first inference layer completes
by applying beacons. Optimizing the inferred data resulted
from the first inference layer is the next stage. To increase
the accuracy of the inferred data, the MIA extracts more
valuable and effective data points which were not extracted
in the first layer of the inference algorithm. This optimization
step includes a count of the maximum absolute differences
between the original and the sample data in each and every
period (Sland Su) and extracting those which are greater than
a defined threshold (e.g. more than 2 per cent of real data
points). The mentioned threshold should be the most opti-
mized. The higher the accuracy and savings rates, the better
and more optimized the threshold is. Furthermore, in this step,
the AR and SR for different thresholds are calculated, and the
most optimized one is extracted which lets the MIS achieve
the best accuracy and savings rates as shown in Figure 8(a).
Although this inference system can result in a reason-
able accuracy rate, it may not demonstrate satisfactory sav-
ings results in certain data sets, particularly where there are
expected to be large and frequent fluctuations in values.
In these volatile data sets, it is likely to generate a large
number of maximum and minimum points, and subsequently
a relatively large number of extracted data points resulting
from the first inference layer as shown in Table 2.
TABLE 2. Savings and Accuracy Rates in different layers and steps for low
and high fluctuation data set.
The first layer of optimization is applied to improve the
accuracy. In this stage, the gaps between original and inferred
values are monitored. Between each two inferred points,
a point with a maximum gap between two plots is selected
and if the gap is above a defined and optimized threshold,
this point will be extracted as a new inferred point, and added
FIGURE 8. Sensitivity analysis of savings rate and accuracy rate for low
fluctuation data set for the purpose of optimization, (a) finding the
optimized percentage of the gap in the first layer of optimization,
(b) finding the optimized VR in the second layer of inference system.
(c) finding the optimized percentage of the gap in the second layer of the
inference system.
to the inferred data set. This layer has a notable impact on the
accuracy of MIS, which is shown in Figure 7(b) and Table 2.
Although these two steps result in an excellent accuracy
of MIS, the total number of inferred points are unsatisfactory
(Table 2 ). Therefore, the third step is applied. In this step,
the second inference algorithm named variance rate (VR) can
be implemented. The VR algorithm compares the variance
between two consecutive data resulting from the two pre-
vious steps, including every minimum and maximum data
point against the data set from the first optimization step.
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In this layer, if a point passes the predefined threshold, it is
extracted as an inference value. By applying the VR algo-
rithm, the inference algorithm can avoid the unchanged or
nearly unchanged data point from the last stages (Figure 7(c)).
The mentioned predefined threshold of VR in this study
is optimized variance rate, which could result in the best
accuracy and savings rates. In this step, different AR and
SR based on the different VR are calculated, and the best
VR related to the highest AR and SR will be chosen. This
VR is shown in Figure 8(b) where two lines intersect each
other. In the last step, an optimization process like the second
step is applied to the inferred data. This is done to increase
the accuracy of the system (Figure 7(d)). The VR can be
adjusted based on the different requirements, situations and
applications. The complete inference system is shown in the
algorithm in Figure 9.
FIGURE 9. Algorithm of Inference system including detecting Max and
Min Points, detecting points with Max Gap and VR between extracted
data points.
The quality of output data transmission from the inference
system is analyzed using three indices, including Efficiency
Rate (ER), Savings Rate (SR) and Accuracy Rate (AR) of
which terminology are defined as below [7]. The ER equation
is shown in (3):
Efficiency Rate (ER)
=(No.of Sensed data No.of Transferred data)
No of Transferred data ×100
SR shows the data size reduction rate through the inference
system and is shown in (4):
Savings Rate (SR)
=(No.of Sensed data No.of Transferred data)
No of Sensed data ×100
The smaller the volume of data transmitted from the orig-
inal data set, the less battery consumption that can occur
resulting in greater energy efficiency.
The AR shows the amount of produced and transmitted
data accuracy using the inference system and is shown in (5):
Accuracy rate (AR)=S0S
×100 (5)
where S0=Area beneath original DPs and S =sum of
differences which refers to the sum of the gaps between the
transferred data and sensed data plots and:
Sushows the area of gaps where the inferred values are less
than the original ones, whilst Slshows areas of inferred values
that are higher than the original values.
where Snu =area differences between general values and
inferred value between inferred value kand k-1 in the cases
that the inferred values are less than the original.
DP =Data Point
i=Data point calculator
RV =Real Values
INF_V =Inferred Values
S0 =area beneath the real value graph
SL =area of differences between two set of data in each
pair of data in which inferred data is higher than original
SU =area of differences between two set of data in each
pair of data which inferred data is lower than original
S=Sum of all SU & SL
VR =Variance rate
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Gap_tresh =Specified threshold for gap between real
data graph and inferred data graph
where Snl =area differences between general values and
inferred value between inferred value kand k-1 in the cases
that the inferred values are less than the original. The graphics
of Suand Slare depicted in Figure 6. Higher values of total
area gaps refer to greater distortion in comparison to the
original data set, and lower values of Sin (6) imply a better
accuracy rate in the inference system.
The smaller the differences, the closer the extracted sensed
data set is to the original data set. Therefore, these figures can
determine how accurate each inference is with different
amounts of VR, while the decreased number of extracted data
points can be defined as efficiency. Zero gaps between plots
prior to and after applying a VR (S=0) would represent the
original data completely with no distortion [5].
Data set: Two data sets from the University of Queens-
land Vital Signs Dataset was used in the experiment, which
included vital signs such as heart rate, SpO2 (oxygen satura-
tion), NBP, ETCO2 [7].
Experiment: This study applied a novel MIA on two
different data sets used for our experiment. One of the data
sets had a lower fluctuation of values, whilst the other had
higher fluctuation over a specific time interval. The devel-
oped MIA was applied to the high fluctuation data set with
4292 valid HR data points, and the lower fluctuation data
set with 11288 valid HR data points. Changes of AR and
SR resulted from the different number of VRs are shown
in Figure 10. Matlab R2019b was used to develop the MIA
for coding and visualization with graphs.
The results of applying the multi-layer inference system are
shown in Figure 10. This figure shows the saving rate and
accuracy of the inference system in the VR percentage range.
VR=0 represents applying relative extrema inference layer
and beacons without using VR inference layer. Figure 10(a)
shows the results of the inference system applied to the low
fluctuation data set. This study aimed to reduce the size of
data points (more than 90%) with an acceptable accuracy
rate of more than 90%. An optimized inference system is
defined as the system resulting in a savings and accuracy rate
of greater than 90%. It can be inferred that the closer these
values are to 100%, the better the results are to be expected.
The optimum accuracy and savings rate would ultimately be
adjusted and defined based on the requirements of the end
Graphical distortions of inferred values against original
values in VR=0 and VR=3% are depicted in Figure 10(a).
This subgraph shows the heart rate along with the 500 data
points. As it is demonstrated, VR=3% is the optimized
point which has an acceptable savings rate and accuracy
rate. VR=3% is extracted as the optimized VR based on
Figure 8(b).
FIGURE 10. Savings rate and accuracy rate graphs after application of the
inference system to (a) low fluctuation data set (b) high fluctuation data
set. X-axis represents the variable rate and Y-axis represents Savings and
Accuracy rates.
Figure 10(b) shows the savings rate and accuracy rate of the
MIA with different VRs applied and all four steps of the MIS
on more volatile data set. Graphical distortions of inferred
values against original values in VR=7 and VR=13.5%
are depicted in Figure 10 with 1000 and 500 data points
respectively. As it has been shown, VR=13.5% is the opti-
mized point which has optimized savings and accuracy rates
resulting from applying the MIA. According to the subgraphs
(a) and (b) in Figure 10, it can be deduced that the increase
of VR reduced the accuracy rate while increasing the savings
rate. Based on this trend, it can be inferred that with a smaller
VR, the degree of accuracy provided by MIA is more signifi-
cant. In contrast, the degree of accuracy is lower at the higher
levels of VR.
As shown in Table 2, the data size was significantly
reduced after applying the MIA. As it is evident in this table
while applying the first layer, a high level of accuracy will
be obtained; the savings rates were not tolerable in data
sets with high fluctuation. After applying the second layer,
the savings rate improved to an acceptable level. The accu-
racy rate decreased but remained satisfactory. By showing the
savings and accuracy rates in one plot, points with acceptable
savings and accuracy rates can be extracted. In Figure 10(a),
the inference system with VR=3% is one of the optimal
points to choose as it has a savings rate of 97.9% and an
accuracy rate of 98.3%.
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Optimized values in high fluctuation data set are merely
different. As shown in Table 2, the first inference layer (with
Max and Min points and beacons) has a 55.2% savings rate
and a 98.6% accuracy rate. The first layer of the optimization
(detecting points with a max gap) is applied to improve the
savings rate, and the result is shown in Table 2. It is clear
that VR=13.5% is one of the best points within the inference
system to choose as it has a 92.3% savings rate and 93.5%
accuracy rate based on the Gap=15% in the second layer of
optimization. Achieving a higher accuracy rate is possible at
lower savings rates. For the purpose of optimization in the
low fluctuation data set, AR increased by about 0.3% and
2.6% after applying the first and second layers of optimiza-
tion respectively. Regarding high fluctuation data set, this
number increased by 0.3% from 98.6 in the first layer of the
inference system to 98.94 in the first layer of optimization,
and around 1.2% from 92.3 to 93.5%, which is a notable level
of optimization in this range.
Additionally, by applying the MIS in two tiers, the system
resulted in a 97.9% savings rate and 98.3% accuracy rate for
the low fluctuation data set. These figures were 92.3% and
93.5% for savings and accuracy rate respectively for the high
fluctuation data set. In comparing these two data sets, the
results show that while the low fluctuation data set with a
relatively lower VR can achieve the optimized results, a more
volatile data set with greater fluctuations requires a higher
VR implementation to obtain high accuracy and savings rates.
In summary, the MIA reduced data samples by 97.9%
while maintaining a 98.3% accuracy against existing methods
using 2-layer inferencing. Additional layers can be applied as
required depending on the size of the dataset, for example an
electronic health record whose dataset may be large.
When compared to a single layer inferencing algorithm [7],
MIA shows significant enhancement for accuracy and sav-
ings rates, whilst it is not fully comparable for the inferencing
variables may be different. For example, a single layer infer-
encing algorithm showed results with 89.7% and 93.7% for
accuracy and savings rates, whilst MIA demonstrated 98.3%
and 97.9% respectively.
The study of an iris recognition system is considered for
the measurement of physiological characteristics in identity
The performance of the iris recognition system was
evaluated on a publicly available database (CASIA-IrisV3-
Interval [72]) in which there were 2639 iris samples in the size
of 320 ×280 pixels collected from 395 different eyes. Only
the 1332 left iris samples from this database were utilized
for input into VeriEye SDK for feature extraction. Feature
extraction for 179 samples failed, and thus these samples
were excluded from progressing through the experiment.
A total of 1153 (=1332-179) samples were included in the
experiment to evaluate the system performance through the
following three metrics: false acceptance rate (FAR): the pro-
portion of times the system grants access to an unauthorized
person, false rejection rate (FRR): the proportion of times the
system fails to grant access to an authorized person, and the
equal error rate (EER): the value when FAR and FRR are
equal. In the experiment, the feature vector extracted from
the first iris image of each eye is compared with the rest of
the iris images of the same eye to calculate the FRR, while the
feature vector extracted from the first iris image of each eye
is compared with the feature vector extracted from the first
iris image of different eyes to calculate the FAR [70].
FIGURE 11. Performance of the iris recognition system, EER=2.54%,
when M=1.
As there is possible iris rotation during the iris image
acquiring process, shifting of the obtained binary string in
the matching process is essential to obtain satisfactory per-
formance. In this experiment, the template feature vector is
not shifted, but the query feature vector will be shifted left
or right up to M=1, 4 and 7 bits, similar to [70]. After each
shifting, a similar score is calculated between the template
feature vector and the generated query feature vector. In this
way, a set of 2M+1 score is calculated by using equation (2),
and the maximum score is considered as the similarity score
between the two compared iris images. After the matching
process is run on the whole database, the Receiver Operating
Characteristic (ROC) curves of the system is generated with
the calculated similarity scores and is shown in Figures 11
- 13. These figures show that with an increasing similarity
threshold, the FAR reduces, while the FRR increases. The
crossing point of the FAR and FRR lines is the EER. It can
be seen that when M is set to be 1, the EER is 2.54%, while
the EER=0.22% when M increases to 4 or 7. Even if the
EERs are the same, there is a greater computational cost
when M=7 than when M=4 as more template and shift query
feature vectors have to be compared.
The FAR and FRR can be configured by adjusting param-
eter settings; in this way, the system security level can also
be adjusted. The lower the FAR, the higher the security of
the system as the proportion of false users wrongly accepted
is less; however, a lower FAR means a higher FRR, which
VOLUME 8, 2020 201511
J. J. Kang et al.: No Soldiers Left Behind: An IoT-Based Low-Power Military mHealth System Design
FIGURE 12. Performance of the iris recognition system, EER=0.22%,
when M=4.
FIGURE 13. Performance of the iris recognition system, EER=0.22%,
when M=7.
means the system will be less convenient as the proportion
of wrongly rejected users is higher. Therefore, there is a bal-
ance between system security and user convenience, and the
acceptable standard for each should be carefully considered
and designed in the practical application of the biometric
authentication system.
In this paper, a general wireless body area network-based
framework was constructed to assist soldiers in emergency
situations such as in field operations. The proposed frame-
work includes various military network applications. Multi-
factor authentication has been enhanced using health data and
biometrics for personnel identification. Multilayer inference
algorithms have been used to improve the accuracy and effi-
ciency to reduce power consumption. Results showed that the
second inference layer improved with further savings rate of
dataset increased by 10.3% whilst accuracy rate decreased
only by 0.91% comparing to the first layer inference algo-
rithm, which has already improved savings and accuracy
rates. As the accuracy trades off with efficiency due to the
nature of data points which play a key role in calculating the
rates, future study is needed to improve both rates to minimise
the impact of data points to the inferencing algorithm.
TABLE 3. Multilayer Inference Algorithm.
201512 VOLUME 8, 2020
J. J. Kang et al.: No Soldiers Left Behind: An IoT-Based Low-Power Military mHealth System Design
TABLE 3. (Continued.) Multilayer Inference Algorithm.
The alarm notification module can raise an alarm when
the integrated sensor devices with activity recognition system
monitors the status of the situation programmed with a prede-
fined threshold level. Additionally, the actuation module will
take corresponding operations to assist the user and rescue
team members based on specific circumstances. It is envis-
aged that the solution can provide real-time monitoring and
actuation features with embedded mHealth devices and wear-
ables using LPWAN networks. In future studies, centralizing
the power supply and battery types across equipment used
by soldiers should be considered to maximize compatibility
across all equipment. Lightweight security measures need
to be implemented for LPWAN, due to its computational
and battery power constraints. Enhanced sensors and devices
should be considered to minimize power consumption and
to improve security. For example, smart hydrogel biomedical
sensors can be implanted on the hand or wrist for capturing
health data and identification of personnel.
See Table 3.
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JAMES JIN KANG has worked in the ICT areas
including telecommunication networks, health
informatics, IoT and sensor networks, cybersecu-
rity, and disaster recovery using smart sensors in
low-power wide area networks. He has worked
in the telecommunications industry for more than
25 years with projects in Spark NZ, Nokia
(Alcatel-Lucent), NBN Co., Telstra, Siemens, and
Vodafone Australia. He has specialized in network
intelligence for wired and mobile networks during
the earlier stages of his career, and later worked on career networks, such
as IP/MPLS, IMS, NGN, NBN, and VoIP technologies. He is currently a
Lecturer in computing and security with the School of Science, Edith Cowan
University. He recently went to Africa as a volunteer IT Advisor sponsored
by the Australian Government (DFAT) to help NGOs, and plans to teach in
developing countries as a volunteer in the future.
WENCHENG YANG is currently a Postdoctoral
Researcher with the School of Science, Security
Research Institute, Edith Cowan University. His
major research interests include biometric secu-
rity, biometric recognition, and network security.
He has authored a number of articles published in
high-ranking journals, e.g., IEEE TRANSACTIONS ON
GORDANA (DANA) DERMODY is currently a
teaching and a research scholar with the position
of Lecturer with the School of Nursing and Mid-
wifery. She is also actively involved in conducting
research with the Clinical Nursing and Midwifery
Research Centre (CNMRC), Edith Cowan Uni-
versity. Her research interests include the devel-
opment and implementation of aging in place
technologies (AiPT) and solutions to support safe
mobility and home management of older adults
with significant chronic disease who desire to remain living at home as
they age.
MOHAMMADREZA GHASEMIAN received the B.S. degree in indus-
trial engineering and the M.S. degree in health system engineering from
the Amirkabir University of Technology, Tehran, Iran, in 2013 and 2016,
respectively. He has worked in areas including Health/Hospital Information
Systems (HIS), Quality of Services (QoS), the Internet of Things (IoT), and
health statistics. He has specialized in evaluating the performance of services
in the health area, including hospital information systems utilizing mixed
research methods.
SASAN ADIBI (Senior Member, IEEE) received
the Ph.D. degree in communication and infor-
mation systems from the University of Waterloo,
Waterloo, ON, Canada. He is the first author
of more than 85 journal/conference/book chap-
ter/white article publications, and is a co-editor
of four books, two of which are in the areas of
mHealth and the other two books are in the areas
of fourth Generation Mobile Networks and QoS.
He holds five U.S. patents in the areas of Health
Informatics and has more than 12 years of strong industry experiences,
having worked in a number of high-tech companies, including: Nortel Net-
works, Siemens Canada, BlackBerry Corporation, WiMAX Forum, Huawei
Technologies, and Hyundai KIA America Technical Center, Inc., (HATCI),
in leadership roles. He was a recipient of the Best Ph.D. Thesis Award from
the IEEE Society. He is currently a Faculty Member of the School of IT,
Deakin University.
IEEE) is currently the Associate Dean of Comput-
ing and Security with the School of Science, Edith
Cowan University. He has delivered keynotes,
invited presentations, workshops, professional
development/training, and seminars across the
world for audiences including RSA Security, Sri
Lanka CERT, ITU, and IEEE. He has appeared on
local and national media (newspaper, radio, and
TV) commenting on current cyber issues, as well
as contributions through articles published in The Conversation. He has more
than 20 years of experience in cyber security research and education in both
the U.K., and Australia. He is the author of more than 80 articles in refereed
international journals and conference proceedings and edited 29 proceedings.
VOLUME 8, 2020 201515
... The aim of this design is to assess and propose a solution through real-time monitoring of the soldier (or warfighter), and the development of a personalised health index for use by the warfighter in the field. To expand upon previous works in the field of military mobile networks [2][3], machine learning and inference algorithms are used to monitor and analyse health data of warfighters including their physiological measurements (cardiovascular, sleep/rest, calories burned, activity and exertion, respiration); psychology (unusual behaviour); and the environment around them (location and temperature). The PMSML comprises 1) a warfighter interface for selfmanagement; 2) a decision support system to enhance decision-making in the field for management of an acute or impending health event; and 3) a warfighter personalised health index for longer-term monitoring to enhance selfmanagement of potentially chronic changes in health and well-being such as hypertension, diabetes, depression, and anxiety. ...
... Next stage of Performance Management System (adapted from[3]) ...
... M ilitary strategies are trying to hone their battlefield tactics by adopting Internet of Things (IoT) enabled Wireless Sensor Network (WSNs) [1]- [3]. The deployment of sensors, unmanned aerial vehicles, tiny robots are envisioned to transform the military personnel on the battlefield [4]. ...
... The deployment of sensors, unmanned aerial vehicles, tiny robots are envisioned to transform the military personnel on the battlefield [4]. Since this is the era of the digital age, the US Army Research Laboratory (ARL) is foreseeing the usage of these smart devices by their soldiers so that they all are connected to the military's digital communication networks [5] and also know their health status [3], [6]. The aim is to develop a connected network to monitor the soldiers. ...
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A major limitation to the use of Wireless Sensor Networks (WSNs) in asset monitoring applications is security and privacy, particularly the privacy of source location information. In this paper, we develop two phantom routing-based solutions to provide source location privacy for the case of multi-source/asset scenario the case that has received very little attention in the literature. The idea of phantom routing is to relay the packets to a distant node in a random fashion to obfuscate the traffic flows and confuse the attacker. The first technique PRBRW uses a combination of backward random walk and a greedy forwarding approach to route the packets to the base station (BS). Although the first approach has better performance improvements in terms of capture ratio and safety-period it hampers the lifetime of the network and has a poor entropy metric. To better this problem, an improved phantom routing scheme PRLPRW is proposed. The second technique has three phases: pure random walk, L-walk, and greedy walk. This technique performs well in terms of capture-ratio, safety period, and entropy metrics. The improvement in network lifetime is 10-folds and entropy is 477-folds when compared with PRBRW. The performance is evaluated using the developed analytical models and compared with the base-line protection-less scheme SPR. It is observed that PRBRW and PRLPRW respectively have 60-and 73-fold improvement in terms of capture ratio when compared with SPR. Whereas existing PRPRW and FRW techniques respectively have only 54-and 34-fold improvements.
... M ilitary strategies are trying to hone their battlefield tactics by adopting Internet of Things (IoT) enabled Wireless Sensor Network (WSNs) [1]- [3]. The deployment of sensors, unmanned aerial vehicles, tiny robots are envisioned to transform the military personnel on the battlefield [4]. ...
... The deployment of sensors, unmanned aerial vehicles, tiny robots are envisioned to transform the military personnel on the battlefield [4]. Since this is the era of the digital age, the US Army Research Laboratory (ARL) is foreseeing the usage of these smart devices by their soldiers so that they all are connected to the militarys digital communication networks [5] and also know their health status [3], [6]. The aim is to develop a connected network to monitor the soldiers. ...
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Till date source location privacy preserving techniques have aimed at fake backed or fake source approaches. Also, the work is concentrated to a single source scenarios. In this work, aim to explore the random-walk approach to mitigate passive eavesdropping attacker who backtracks to the source of information. Random-walk based solutions have proven to be effective for energy constrained WSNs. However, there is very little work that has worked for the case of multiple asset (sources) scenario till date. To understand the effect of the random-walk based solutions on level of location privacy in WSN intended IoT systems, for the multiple asset scenario, we developed two solutions. Through simulations we show the performance of the proposed two solutions by comparing them with existing random-walk based solutions. Our findings suggest that mere presence of multiple sources in the network alone does not provide location privacy, as one is intended to expect. It rather, need careful planning and designing of routing protocols to provide better privacy in presence of multiple-assets. The work also presents future research direction for prospective researcher.</div
... WBAN can provide potential benefits like connectivity, survivability for military networks, and applications regarding the health (heart rate, blood pressure, hydration level, etc.) of soldiers and field personnel during a mission. On the battlefield, the soldiers can communicate with each other about giving commands to attack, run, and share information [189]. ...
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Evolution of mobile broadband is ensured by adopting a unified and more capable radio interface (RI). For ubiquitous connectivity among a wide variety of wireless applications, the RI enables the adoption of an adaptive bandwidth with high spectrum flexibility. To this end, the modern-day communication system needs to cater to extremely high bandwidth, starting from below 1 GHz to 100 GHz, based on different deployments. This instigates the creation of a platform called the Internet of Everything (IoE), which is based on the concept of all-round connectivity involving humans to different objects or things via sensors. In simple words, IoE is the intelligent connection of people, processes, data, and things. To enable seamless connectivity, IoE resorts to low-cost, compact, and flexible broadband antennas, RFID-based sensors, wearable electromagnetic (EM) structures, circuits, wireless body area networks (WBAN), and the integration of these complex elements and systems. IoE needs to ensure broader information dissemination via simultaneous transmission of data to multiple users through separate beams and to that end, it takes advantage of metamaterials. The precise geometry and arrangement of metamaterials enable smart properties capable of manipulating EM waves and essentially enable the metamaterial devices to be controlled independently to achieve desirable EM characteristics, such as the direction of propagation and reflection. This review paper presents a comprehensive study on next-generation EM devices and techniques, such as antennas and circuits for wearable and sub 6 GHz 5G applications, WBAN, wireless power transfer (WPT), the direction of arrival (DoA) of propagating waves, RFID based sensors for biomedical and healthcare applications, new techniques of metamaterials as well as transformation optics (TO) and its applications in designing complex media and arbitrary geometry conformal antennas and optical devices that will enable future IoE applications.
... Commonly utilized wearable sensors in HAR systems [1] include accelerometer [2], magnetometer, gyroscopes, inertial measurement unit (IMU), electromyogram (EMG) [3], force-sensitive resistors (FSR) [4] and wearable wrist camera [5]. The wearable sensors-driven HAR has critical applications in healthcare [6]- [9], human-robot interaction [10], [11], interactive gaming [4], sports [2], [12], military [13], [14] and so on. ...
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Human Activity Recognition (HAR) is a problem of interpreting sensor data to human movement using an efficient machine learning (ML) approach. The HAR systems rely on data from untrusted users, making them susceptible to data poisoning attacks. In a poisoning attack, attackers manipulate the sensor readings to contaminate the training set, misleading the HAR to produce erroneous outcomes. This paper presents the design of a label flipping data poisoning attack for a HAR system, where the label of a sensor reading is maliciously changed in the data collection phase. Due to high noise and uncertainty in the sensing environment, such an attack poses a severe threat to the recognition system. Besides, vulnerability to label flipping attacks is dangerous when activity recognition models are deployed in safety-critical applications. This paper shades light on how to carry out the attack in practice through smartphone-based sensor data collection applications. This is an earlier research work, to our knowledge, that explores attacking the HAR models via label flipping poisoning. We implement the proposed attack and test it on activity recognition models based on the following machine learning algorithms: multi-layer perceptron, decision tree, random forest, and XGBoost. Finally, we evaluate the effectiveness of K-nearest neighbors (KNN)-based defense mechanism against the proposed attack.
... MANET are a set of mobile radio devices capable of self-configuration parameters to connect each other nodes without relying on any base station system [5]. Although it has ability and capacity limited, MANET have been demonstrated to be a superior communication solution with flexible infrastructure and applied in a variety of fields for humanity such as healthcare [12,31], intelligent transport [19,27], military [17], rescue and disaster recovery [18,41], smart retail [46], smart-agriculture [25,34], as well as promise important contributions to the future of Internet [40]. A set of rich and diverse MANET services and applications in IoT ecosystems is introduced in Fig. 1. ...
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With the powerful evolution of wireless communication systems in recent years, mobile ad hoc networks (MANET) are more and more applied in many fields such as environment, energy efficiency, intelligent transport systems, smart agriculture, and IoT ecosystems, as well as expected to contribute role more and more important in the future Internet. However, due to the characteristic of the mobile ad hoc environment, the performance is dependent mainly on the deployed routing protocol and relative low. Therefore, routing protocols should be more flexible and intelligent to enhance network performance. This paper surveyed and analysed a series of recently proposed routing protocols for MANET-IoT networks. Results have shown that these protocols are classified into four main categories: performance improvement, quality of service (QoS-aware), energy-saving, and security-aware. Most protocols are evolved from these existing traditional protocols. Then, we compare the performance of the four traditional routing protocols under the different movement speeds of the network node aim determines the most stable routing protocol in smart cities environments. The experimental results showed that the proactive protocol work is good when the movement network nodes are low. However, the reactive protocols have more stable and high performance for high movement network scenarios. Thus, we confirm that the proposal of the routing protocols for MANET becomes more suitable based on improving the ad hoc on-demand distance vector routing protocol. This study is the premise for our further in-depth research on IoT ecosystems.
... Very few studies have provided evidence on machine learning algorithms to improve healthcare data accuracy and efficiency on the network. Many studies [9][10][11][12][13], have focused on improving the trade-off ratio between data accuracy and efficiency using a multilayer inference algorithm. For example, when data accuracy is improved by using a larger quantity of samples, efficiency suffers. ...
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Patients are often encouraged to make use of wearable devices for remote collection and monitoring of health data. This adoption of wearables results in a significant increase in the volume of data collected and transmitted. The battery life of the devices is then quickly diminished due to the high processing requirements of the devices. Given the importance attached to medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data for network transmission may improve sensor battery life without compromising accuracy. There is a trade-off between efficiency and accuracy which can be controlled by adjusting the sampling and transmission rates. This paper demonstrates that machine learning can be used to analyse complex health data metrics such as the accuracy and efficiency of data transmission to overcome the trade-off problem. The study uses time series nonlinear autoregressive neural network algorithms to enhance both data metrics by taking fewer samples to transmit. The algorithms were tested with a standard heart rate dataset to compare their accuracy and efficiency. The result showed that the Levenbery-Marquardt algorithm was the best performer with an efficiency of 3.33 and accuracy of 79.17%, which is similar to other algorithms accuracy but demonstrates improved efficiency. This proves that machine learning can improve without sacrificing a metric over the other compared to the existing methods with high efficiency.
... It is also incorporated with buzzer to notify the fellow soldier about the danger. James Jing Kang reported a wearable health device to measure the health parameter of the soldier using multi-layer interference system there by potentially conserving the battery power of the sensor devices [4]. The device is also equipped with an enhanced authentication mechanism using data related to health and using biometrics for personnel identification. ...
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In the crisis filled world, it is necessary to protect our Country and its resources. Where a strong military force will enable in safeguarding the nation security, wealth and our valuable lives too. Soldiers are not only involved in providing protection against the military attacks but also plays a crucial role in resolving problems thereby maintaining prevalence of peace in the country. Therefore, it is necessary to safeguard and protect soldiers from dangers as they are the guardians to enrich the prosperity of one’s country. The proposed project focuses on monitoring the soldier’s environmental attacks like detection of gun firing attacks, toxic gas detection, and emergency alert in case of threats. Followed by creating a virtual fence using Global Positioning System (GPS) to track and guard them within the safer zone and with inclusion of two-way wireless communication between the soldier & the surveillance unit using Wi-Fi Module in case of emergency return from the battle field thereby protecting them from the possibilities of danger. The technologies in particular Artificial Intelligence of Things i.e. the collaboration of Internet of Things (IoT) with deep learning (RNN-LSTM Model) helps in achieving information transfer between soldier and administrator followed by prediction of toxic gas presence in the atmosphere. The Administrator at the surveillance unit will monitor the real time status of the soldier in the webpage created using Node Red Programming Tool where the data are carried out by means of Sensor Networks - Message Queuing Telemetry Transport Protocol (MQTT). Thus this system ensures the security of the soldier.
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In the healthcare system, patients are required to use wearable devices for the remote data collection and real-time monitoring of health data and the status of health conditions. This adoption of wearables results in a significant increase in the volume of data that is collected and transmitted. As the devices are run by small battery power, they can be quickly diminished due to the high processing requirements of the device for data collection and transmission. Given the importance attached to medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission will improve the device battery life via using inference algorithm. There is an issue of improving transmission metrics with accuracy and efficiency, which trade-off each other such as increasing accuracy reduces the efficiency. This paper demonstrates that machine learning can be used to analyze complex health data metrics such as the accuracy and efficiency of data transmission to overcome the trade-off problem using the Levenberg-Marquardt algorithm to enhance both metrics by taking fewer samples to transmit whilst maintaining the accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The result shows that the LMA has best performed with an efficiency of 3.33 times for reduced sample data size and accuracy of 79.17%, which has the similar accuracies in 7 different sampling cases adopted for testing but demonstrates improved efficiency. These proposed methods significantly improved both metrics using machine learning without sacrificing a metric over the other compared to the existing methods with high efficiency.
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Painless, needle-free, and continuous glucose monitoring sensors are needed to enhance the life quality of diabetic patients. To that extent, we propose a first-of-its-kind, highly sensitive, noninvasive continuous glycemic monitoring wearable multisensor system. The proposed sensors are validated on serum, animal tissues, and animal models of diabetes and in a clinical setting. The noninvasive measurement results during human trials reported high correlation (>0.9) between the system’s physical parameters and blood glucose levels, without any time lag. The accurate real-time responses of the sensors are attributed to their unique vasculature anatomy–inspired tunable electromagnetic topologies. These wearable apparels wirelessly sense hypo- to hyperglycemic variations with high fidelity. These components are designed to simultaneously target multiple body locations, which opens the door for the development of a closed-loop artificial pancreas.
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The increased maturity level of technological achievements towards the realization of the Internet of Things (IoT) vision allowed sophisticated solutions to emerge, offering reliable monitoring in highly dynamic environments that lack well-defined and well-designed infrastructures. In this paper, we use a bio-inspired IoT architecture, which allows flexible creation and discovery of sensor-based services offering self-organization and self-optimization properties to the dynamic network, in order to make the required monitoring information available. The main contribution of the paper is the introduction of a new algorithm for following mobile monitored targets/individuals in the context of an IoT system, especially a dynamic one as the aforementioned. The devised technique, called Hot-Cold, is able to ensure proximity maintenance by the tracking robotic device solely based on the strength of the RF signal broadcasted by the target to communicate its sensors’ data. Complete geometrical, numerical, simulation, and convergence analyses of the proposed technique are thoroughly presented, along with a detailed simulation-based evaluation that reveals the higher following accuracy of Hot-Cold compared to the popular concept of trilateration-based tracking. Finally, a prototype of the full architecture was implemented not only to demonstrate the applicability of the presented approach for monitoring in dynamic environments, but also the operability of the introduced tracking technique.
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The paper presents designed quantitative methods and algorithms implemented in specialized mobile mCOP application for combat decision support. Military operations require sophisticated planning and execution in order to minimize risks and potential losses while fulfilling mission objectives in constrained time. To support such features the decision makers design methods of evaluating threats and tactical scenarios. The outcomes of such calculations provide mission alternatives called variants or Courses of Actions. To determine which possible CoA is matching the scenario and in the end is the most feasible, a set of quantitative methods can be used. The evaluation process considers both tactical and topographical characteristics of the scenario. The location, orientation and composition of forces, combat potentials, performed tasks, as well as terrain and battlespace environment features are the parameters considered in the algorithms, which in result provide threat levels, attrition rates, potential losses, objectives timelines etc. To assess tactical situation we implemented, a set of combat potential evaluation methods, which using doctrinal patterns, can be easily employed in mobile software. The advantage of equipping each decision maker at the tactical level with sophisticated methods provides means of increasing the speed of task execution, provides more accurate decisions but most of all supports instant reporting from combat units. Due to the development of hardware and software platforms, smartphones offer are capable of running complex algorithms for tactical support for individual soldiers and low level commanders support. Since they are capable to utilize tactical data (forces location, composition, tasks) in dynamic mobile military networks, accessible anywhere during mission, and provide provides rich functional support for situation awareness and decision superiority. These two key factors of 21st century military operations influence the efficiency of recognition, identification and targeting during combat missions. Military support tools providing rich tactical and their analytical capabilities, can serve as recon data hubs, but most of all can support and simplify complex analytical tasks for commanders. The tasks mainly include topographical and tactical orientation within the battlespace. Developed software platform mCOP, demonstrates all research findings, providing personalized combat-oriented distributed mobile system, supporting blue force tracking capabilities, reconnaissance data fusion as well as threat level evaluation for functionalities for management of military and crisis management scenarios. Presented research demonstrates and evaluates the proves the usefulness of deploying mobile applications for combat support, situation awareness development, and the delivery of augmented reality based threat level analytical data to extend the capabilities and properties of constructed methods and of software tools applied for supporting military and border protection operations.
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Biomedical monitoring is becoming a tool which can improve reliable and accurate medical diagnostic and health state evaluation. Mobile software and platforms are hosts for advanced health state monitoring systems, and recently are moving towards decision support tools. The analytical process must be supported by sensing equipment selected to evaluate specific symptoms (and preferably their intensity) of a given disease. This paper will discuss usage of surface multichannel electromyography and supplemented with arms activity evaluation. These data sources need to be selected with regard to not only detection accuracy but also mobility and usability features, which make the measuring process so cumbersome and difficult. In this work we summarize our effort to prepare the sensors, tune them to provide details on NERVE sensor, which has been based on IoT platform components and supplemented with MYO multi-sensor as a supplementary data source. Constructed system has been designed to consume electromyography and inertial data in order to feed seizure detection algorithms, responsible for health event detection and alarms. The application provides also novel approach in case of child seizures aimed not only at supporting parents but also at recording and assessing seizure symptoms. This work demonstrates signal processing algorithms, and describes functionality of designed system in the domain of hardware and software components.
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In military applications, mobile ad-hoc networks are commonly used due to the autonomy, self-configuration, and flexibility. In addition, spectrum sharing-based communication protocols are being actively considered due to spectrum shortage. We propose a cooperative phase-steering (CPS) technique for a spectrum sharing-based military mobile ad-hoc network which consists of a single secondary source (SS) node, multiple secondary relay (SR) nodes, a single secondary destination (SD) node, and multiple primary destination (PD) nodes. In the proposed technique, the SR nodes that succeeded the packet decoding cooperatively adjust the phase of their transmit signals such that the received signals at the SD node from the SR nodes are aligned to a certain angle, while the SR nodes control the transmit power such that the received power at all PD nodes is lower than a certain threshold. Through extensive computer simulations, it is shown that the proposed technique outperforms the conventional cooperative relaying scheme in terms of outage probability.
Conference Paper
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Fog Computing is a new computing paradigm which is grown ever since it is being used. It is aimed at bringing the computations close to data sources from healthcare centers. IoT driven Fog Computing is developed in the healthcare industry that can expedite facilities and services among the mass population and help in saving billions of lives. The new computing platform, founded as fog computing paradigm may help to ease latency while transmitting and communicating signals with remote servers, which can accelerate medical services in spatial-temporal dimensions. The latency reduction is one of the necessary features of computing platforms which can enable completing the healthcare operations, especially in large-size medical projects and in relation to providing sensitive and intensive services. Reducing the cost of delivering data to the cloud is one of the research objectives.
Conference Paper
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Electrocardiogram (ECG) is an electric signal of cardiac activity posing highly discriminative properties related to human recognition. ECG based authentication has gained much success in recent times however discriminant feature extraction and efficient pattern classification still encounter numerous challenges. This paper proposed a novel methodology for ECG based biometric authentication system. Proposed method first denoise single lead raw ECG signal through empirical mode decomposition (EMD). Region of interest from ECG signals having maximum characteristic information related to subject’s recognition is also extracted through EMD. Next, feature extraction is performed by combination of five features from statistical, time and frequency domains. Finally, selected features were categorized with range of different classification methods such as Support Vector Machines (SVM), K-nearest neighbor (KNN) and Decision Tree (DT). 10-fold cross validation based classification evaluation reveals that SVM with cubic kernel achieves best accuracy of 98.7%, sensitivity of 100% and 98.8% specificity for successful classification of 14 subjects.
A wireless sensor network consists of a large number of nodes, ending sensed data to the base station or sink, either directly or through intermediate nodes. Multi-hop communication results in increased volume of traffic and depleting the energy of nodes adjacent to static sinks. A method of dealing with this challenge is using mobile sinks. Mobile sinks balance the load and distribute energy consumption throughout the network. This paper suggests a method to divide the network into some cells in a geographic way and applies two mobile sinks to gather the data sensed by these cell nodes. Based on the communication between cells and mobile sinks, the cells are divided into two categories: single-hop communication cells (SCCs) and multi-hop communication cells (MCCs). Mobile sinks move over two concentric diamond-shaped orbits in such a way that each half of the network is covered by a sink at a time. Initially, both sinks move in one direction and stay at particular intervals in the corners of the orbits to gather data from sensor nodes. When sinks are stationary, SCCs send data to the sinks directly, but MCCs apply the proposed routing algorithm (EGRPM) to send data to mobile sinks. The proposed approach is simulated by NS2 software. A comparison between the performance of EGRPM and conventional methods shows that applying EGRPM results in a significant decrease in average energy consumption and data delivery delay and causes a substantial increase in packet delivery rate and network lifetime.