Thermal-Aware Based Field Theory Routing in Wireless Body Area Networks

Invention Journal of Research Technology in Engineering & Management (IJRTEM) ISSN: 2455-3689
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Thermal-Aware Based Field Theory Routing in Wireless Body Area Networks
Samia Allaoua Chelloug
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint AbdulRahman
University, Riyadh, Kingdom of Saudi Arabia
ABSTRACT:Wireless Body Area Networks (WBANs) have emerged as a powerful solution for healthcare applications. They
investigate small devices that are instrumental for providing medical data to a remote base station. Recent developments in WBANs
have led to wireless implantable sensors that are able to transmit in vivo measurements. Two key issues have been dominated the field
of wireless implantable sensor networks: temperature rise and attenuation of the transmitted signals due to the properties of the skin.
This paper addresses thermal-based routing in wireless implantable sensor networks. Different from the existing methods that
estimate the temperature of the neighboring sensors, our method is based on the field theory to avoid the hotspots. Furthermore, we
conducted an Omnet++ simulation that supports IEEE 802.11 which promotes an implementation of CSMA/CA MAC scheduling. Our
simulation results demonstrate the convergence of the maximum temperature rise.
Keywords:WBANs, routing, implantable sensors, field theory, Omnet++, temperature rise.
INTRODUCATION
Since it was reported in [1], Wireless Sensor Networks (WSNs) have been attracting a lot of interest. WSNs extended ad hoc networks
by providing an application specific devices which ensure sensing, communication, and computation capabilities. Formally, a WSN
consists of sensor nodes and a set of wireless links that may exist between neighboring sensors. Part of the features of WSNs concerns
the energy constraint because they are battery-powered [2,3]. For some applications, this constraint is a severe one because it may be
impossible to replace the batteries. Commonly, a huge number of sensor devices is deployed to monitor an area. So, all sensors are
equal. Therefore, the concept of WBAN was first introduced in [4] to refer to a platform of sensors which may collect medical
parameters and transfer theme to a remote base station for online or offline analysis. Different from WSN, the number of sensor nodes
of a WBAN is small and each node is responsible to track a specific parameter. Furthermore, the energy consumption as well as the
quality of service (QoS) are the main issues of WBANs. More specifically, WBANs may be divided into on body and implantable
sensor networks. This latter type has been applied to situations where some in vivo measurements should be reported. Unfortunately,
the transmitted signals of implantable sensor networks may be affected by the skin properties and it is infeasible to recharge their
batteries. Further, the temperature of implantable senor networks may rise and affect the body. In fact, the protocols which are
designed for implantable sensor networks should be considerably different from those used for on body sensor networks. In this
regard, routing is a relatively traditional concept that refers to the process of finding the optimal route under some constraints. Many
studies have dealt with routing in wireless implantable sensor networks by estimating the temperature of neighboring sensors. The aim
of this paper is to present a new contribution that is inspired from the physics and enables each sensor to take a local decision for
routing any packet without estimating the temperature of its neighboring sensors. So, this paper will examine WBANs in section 2.
Section 3 sheds light on the related work. Section 4 is concerned with the proposed scheme. Then, section 5 illustrates and discusses
the obtained results. Finally, section 6 concludes this paper and highlights future work.
WBANS
WSNs play an important role in addressing the issue of monitoring various types of applications. The miniaturization, sensing, and
communication capabilities are dominant features of WSNs [1]. Moreover, each sensor consists of a microcontroller, a transceiver, a
source of power, and a sensing unit [2,3]. In recent years, the continuing growth of Micro-Electro-Mechanical Systems (MEMS),
along with Bio-Engineering and wireless communications, has led to WBANs that have been introduced to enable a remote
monitoring of mobile patients or elderly people [5]. A WBAN refers to a set of nodes that may be implanted or attached to the body
[6] and will be connected through a mesh, a star, or a tree topology that is subject to the specified network’s requirements [7]. The
characteristics of WBANS were presented in [8]. More specifically, table (1) illustrates a comparison between WSNs and WBANs.
The function of WBAN is to collect biological information and generate a traffic that should be transmitted to a base station [5] which
is responsible for storing and processing biological data either online or offline depending on the application requirements. The
transmission from the WBAN to the base station may take different forms depending on the distance between them: direct or indirect
communication. In this latter case, other potential intermediate devices are used. Many scenarios include a PDA that is attached to the
body to gather the sensors’ data and relay them to the base station via a telephone network, a private hospital network, Wi-Fi, or
3G/4G network [7, 9]. WBAN’s traffic can be categorized into three classes: normal, on-demand, and emergency. The normal traffic is
not time critical and it is generated in normal conditions. In this situation, sensor nodes are expected to wake-up at high, medium, or
low frequency to measure a set of specific parameters and send them to the base station. On-demand traffic is generated if a doctor or
an administrator is interested to a certain information. However, emergency traffic is generated if a sensor node detects that data
exceeds a certain threshold or it is under the limit [7, 10]. An actuator may be included for some drug delivery and Insulin injection

applications and it should execute an action for helping the patient based on the obtained indication. The components of on-body
sensors are powered by a battery [7] while in-body sensors rely on coupling techniques to generate the power [11]. Wireless
communication in WBANs is mainly affected by the skin properties and the body parts which may result in a weak signal that is
coupled with noise. To this effect, the transceiver applies two steps before converting the analogue signal to a digital one. Namely, the
amplification and the filtering are applied before the signal goes to the Analogue to Digital Converter (ADC). Example of such sensors
include: Electrocardiogram (ECG), electroencephalogram (EEG), pulse rate, blood pressure, body temperature, and respiratory rate.
WBANs are of interest because they provide a flexible way for transferring physiological data to the nearby device: cell phone, PDA,
robot… depending on the application requirements. In addition, WBANs may effectively process the generated data and may easily be
integrated to other wireless technologies [12]. WBANs are low frequency in nature. It has been shown that EEG sensors require the
highest frequency which varies from 0.01 to 250Hz compared to other sensors. If the distance between the nodes and the gateway is
short, WBANs may use a variety of standards consisting of Zigbee [13], IEEE 802.15.4 [14], and Wireless Local Area Networks
(WLANs) [15]. Thus, GSM [16] is used for long range communication. WBANs are expected to avoid or minimize the interference in
the ISM band (2.4GHz). Furthermore, the standardization efforts were being made to fully support WBANs through the specification
of licensed medical bands that contain the Medical Implant Communication Service (MICS) which is reserved for in-body sensors and
the Wireless Medical Telemetry Service (WMTS) that is used by on-body sensors. MICS operates around 400 HZ in USA, Japan,
Korea, and EU. Japan has allocated the frequencies ranging from 420 to 449HZ for WMTS but USA has assigned the frequencies
ranging from 608 to 1429.5HZ for that band [5]. The physical layer of WBANs should provide adequate modulation and encoding
techniques to reduce the bit error rate. The power management may also be provided by the physical layer to minimize the
interference ratio with other devices that are operating at the ISM band. The role of the MAC layer is to eliminate the collisions of
multi-patients signals. Different fro ‫ة‬WSNs, WBANs should implement energy-efficient solutions. More specifically, replacing the
batteries of body sensors is not a practical solution. Therefore, many research studies have proposed energy harvesting in WBANs and
have presented different transmission policies that consider the current energy level and the predicted charge level to schedule event
detection. Further, the authors of [17] discussed the challenge of designing small wireless ECG sensors that are useful in mobile
applications. The amplification of ECG signals and the prolongation of the lifetime of the ECG battery are the main considerations for
WBANs-based ECG sensors. Moreover, a WBAN contains heterogeneous sensors that can greatly vary in terms of bandwidth and
power consumption and their number depends on the application. They are all equally important and the malfunctioning of one of
them will have severe consequences for critical applications. The security is a main concern in WBANs as medical information is
confidential and should be safely transmitted to the base station. In addition, the communication between WBANs and the base
station should be robust against the mobility. Online processing requires low delay which is an important challenge for some
applications [6]. Existing projects have paid attention to WBANs. In particular, CodeBlue [18, 19] has been developed at Harvard
University as a heterogeneous platform of resource-constrained and powerful devices that are intended to transport emergency traffic
through publish-subscribe mechanism. CodeBlue supports filtering, aggregation, authentication, and tracking issues to ensure the
quality of service. Alarm-Net is [18] another project that has been introduced in this regard. It adopts a three tier architecture where
ears body sensors send physiological data to the environmental ones. The collected data is send through IP protocol. The key issue of
healthGear [20] is to monitor the hemoglobin saturation with Oxygen. The architecture of healthGear includes Oximetry sensors that
send physiological data to the cell phone through Bluetooth. HealthGear also allows the visualization and analysis of the physiological
data on the cell phone. The Mobicare system [21] seeks to provide healthcare services to a wide range of scenarios. The
communication infrastructure of Mobicare enables the mobility and cellular connectivity of the patients via 2.5 G, 3G or WLANs.
More specifically, Mobicare is based on HTTP [22] to access the available services. Further, DexterNet [23] supports an open-source
signal processing library and allows indoor and outdoor communications. The paper of [24] describes two mobile health solutions for
monitoring chronic diseases: MobiHealth is a telemedicine solution that has been investigated at the Netherland while the Personal
Health Monitoring (PHM) has been investigated at Sydney to provide local personal health services.
RELATED WORK
Paper [8] indicates that routing protocols in WBANs may be classified into four categories: QoS aware protocols, temperature aware,
postural-movements and cross-layer routing protocols. Overall, QoS protocols rely on a certain number of modules to ensure routing
and QoS constraints. The aim of thermal-aware routing protocols is to deal with hotspots which should be characterized by a
temperature rise that exceeds a certain threshold. The idea of cluster-based routing protocols consists to construct one or more clusters
such as the routing process is organized by the cluster heads. So far, postural movement-based routing protocols take into account the
body movement and the topology disconnection. Cross-layering routing is an interesting idea that combines the parameters of the
physical and/or the Mac layer within the network layer constraints to find the optimal path. We mention that the idea of QoS, cluster-
based and cross-layer protocols has been already adopted for routing in WSNs. However, postural movement-based and thermal-based
routing are dedicated for wireless implantable sensor networks.

Table 1. Comparison between WSNs and WBANs
The authors of [25] presented a suitable protocol for indoor hospital WBAN. The idea of [25] consists to classify collected data into
ordinary and reliability sensitive data. It calculates the path reliabilities of all possible paths from the source to the destination. The
reliability between a source and its neighbors is obtained by considering the number of packets sent and the number of received
acknowledgments. The proposed protocol in [25] is also based on Hello messages that allow to construct the routing table by
providing the ID of each neighbor and its reliability. The routing module selects the best path based on the reliability. The proposed
protocol in [25] includes two queues for priority and ordinary packets. The obtained Castalia simulation results in [25] demonstrate
that the successful transmission rate is very high.
Different from the idea of [25], the proposed technique in [26] is based on the concept of global routing and uses Djikstra algorithm
and a certain function that balances energy through sensors. The authors of [26] defined the channel attenuation for each link and used
a predefined target RSSI to calculate the accumulated energy. The proposed Djikstra algorithm is based on the link cost which is
defined via a function that depends on the energy consumed if that link is selected and a certain cost factor. Further, the suggested idea
in [26] avoids a sensor node if the energy is much greater than the minimum. The proposed protocol in [26] demonstrates a good
lifetime.
Thermal-Aware Routing Algorithm ―TARA‖ [27] has been developed to solve the temperature issue of implanted sensor networks.
Initially, TARA attempts to determine the hotspots by estimating the temperature. The originality of the work presented in [27]
concerns the temperature estimation that depends on the radiation from the antenna and the power dissipation of the sensor’s circuitry.
TARA transmits the generated packet to one of the neighbors of the current sensor node. If the selected neighbor is a hotpot, TARA
establishes an alternative route around it. TARA has been simulated and implemented on Crossbow Mica 2 to demonstrate the tradeoff
between the throughput and the delay.
Least Temperature Routing ―LTR‖ [28] improves TARA by selecting the neighbor which has the least temperature.
The QoS metric that is defined in [29] expresses that the number of losses should be small and the delay should be non-infinite.
Similar to [26], the authors of [29] used Djikstra algorithm. They defined the outage probability to determine the optimal path. Paper
[29] includes an analysis of the performance of routing according to random and TDMA medium access.
The work in [30] considered wireless implantable sensor networks that communicate to a coordinator which is provided by a
replicable source. A classification module which distinguishes normal and reliable constraint data is adopted. The rule for selecting the
best neighbor depends on the path loss and the link reliability. The work presented in [30] belongs to both QoS and thermal-based
routing. The reliability has been defined as a function of successful transmissions over a time window. The proposed protocol in [30]
estimates the temperature and selects the neighbor with the minimum temperature.
The focus of [31] is to avoid hotspots under QoS requirements. Four classes of the generated traffic have been established in [31].
They depend on the delay and the reliability. The proposed algorithm in [31] is proactive and the routing table includes the reliability,
delay, temperature and the hop count. Upon receiving a packet, it is first classified and then the best path is calculated. The idea
explained in [31] avoids the path that includes a number of hops which exceeds the minimum hop count. The temperature of any
hotspot should be replaced by infinity to prevent any sensor nodes from sending packets to the hotspot. The proposed protocol in [31]
relies on exchanging beacon messages to construct the routing table. Paper [31] presents an evaluation of the proposed protocol in
terms of the temperature rise, reliability, and the energy efficiency.
Network
Characteristics
WSNs WBANs
Topology The topology may be disconnected
due the mobility or the depletion of
the battery
The topology may be disconnected due
to the human body movements.
Communication Multi-hop communication due to the
huge number of sensors.
Single and multi-hop communications
are adopted according to the
application.
Battery Some applications enable the
replacement of batteries.
Replacement of batteries is infeasible.
QoS Depends on the application Required
Radiation absorption No Wireless implantable sensor networks
are characterized by the antenna
radiation absorption.
Path loss The path loss is generated by the
environment.
The path loss may be generated by the
environment or the human body.
Security and privacy Depends on the application Required

The work in [32] describes an improvement of OLSR for routing in WBANs. The contribution of the authors consist to select the node
with minimum delay from those which are not covered with MPR.
The idea of [33] consists to place sensor nodes according to their rates such that direct communication is used for real-time traffic. M-
Attempt [33] is based on TDMA and supports mobility by placing high data rate nodes at less mobile places on the human body. In
addition, each hotspot breaks down its links until its temperature become normal. The authors of [33] propose to select the path with
the minimum number of hops. If two routes have the same number of hops, the route with the minimum energy is selected. The
simulation results in [33] show that M-Attempt demonstrate less energy and more reliability.
The core idea of [34] consists to place sensor nodes according to their rate like the idea presented in [33]. The proposed protocol in
[34] is suitable for a smart home scenario that may help the patients and generates home-signals to allow sensor nodes to be linked to
the routing table of home nodes. Single or multi-hop communications are used depending on the type of the traffic. The lifetime has
been evaluated in [34].
Recently, the authors of [35] have developed a novel cross-layer routing approach that is based on the MAC layer. It also specifies a
set of cluster heads. The method proposed in [35] contains a modified TDMA schedule that defines many types of TDMA slots. The
second contribution in [35] concerns the improvement of GinMac.
CICADA is a hybrid protocol that combines cross-layer and cluster-based routing [36]. It establishes a spanning tree and uses TDMA
scheduling. The parent of the spanning tree allocates time slots to its child. CICADA reserves an inactive period after the data sub-
cycle transmission to allow sensor turn off their radios. The obtained simulation results in [36] illustrate that rate of delivery is 100%.
PROPOSED PROTOCOL
The specific objective of Thermal-Aware Field Theory based Routing (TAFTR) protocol is to show the efficiency of the field theory
for routing in wireless implantable body sensor networks. Furthermore, TAFTR is based on a simplified model. All the notations are
explained in table 2.
TAFTR comprises the following steps:
1. Field generation.
2. Hello message exchange.
3. Greedy routing.
The first step consists to define a scalar fields at the network with peaks at the hotspot implantable sensors. The idea consists to assign
a charge at each hotspot that should be propagated to the other sensors like a wave. The resulting potential at sensor node 𝑆𝑖 that
results from the broadcasted charge of sensor𝑆𝑗 is calculated through equation (1) [37].
𝜑𝑖 = 𝜑𝑖 +
𝑄 𝑗
𝐷(𝑆 𝑖 ,𝑆 𝑗 )
(1)
Table 2. Notations of TAFTR
Notation Definition
𝑆 the set of implantable sensors such as 𝑆 = 𝑁
𝑆𝑅 the source. It is selected randomly.
𝐻 the set of hotspots
𝑥𝑖, 𝑦𝑖 position coordinates of sensor 𝑆𝑖
𝜑𝑖 potential assigned to sensor 𝑆𝑖. It is initialized to 0.
𝑄𝑖 Charge assigned to sensor 𝑆𝑖.
𝐷(𝑆𝑖, 𝑆𝑗 ) Distance between 𝑆𝑖 and 𝑆𝑗 .
The second step consists to exchange Hello messages which are broadcasted in the network. Each Hello message should carry the ID,
position coordinates of the sensor node and its potential. The routing table of each implantable sensor includes the ID , position
coordinates , and the potential of each neighbor. TAFTR attempts to route a packet far away from the hotspots. Hence, it selects the
next hop that has the lowest potential.
𝑁𝑒𝑥𝑡ℎ𝑜𝑝 𝑆𝑖 = 𝑆𝑗 (2) 𝜑𝑗 ≤ 𝜑 𝑘

Figure1. Handshake of TAFTR
According to figure1, each implantable sensor has a hotspot detection module which monitors the temperature of the considered
sensor and triggers an event if the temperature exceeds a certain threshold. In this case, a scalar field is generated and propagated to
the other implantable sensors. By another hand, the localization module is responsible to monitor the position coordinates of the
considered sensor and passes down this information to the module that is responsible for generating periodically Hello messages. The
routing module will use the above information to select the best neighbor which may receive the generated packet according to
CSMA/CA scheduling that is adopted by IEEE 802.11.
SIMULATION RESULTS
Our simulation results are based on Omnet++. Omnet++ standing for Objective Modular Network Test-bed in C++ is an object-
oriented modular discrete event network simulation framework that has a generic architecture. Omnet++ itself is not a simulator of
anything in particular, but rather provides infrastructure and tools for writing simulations. Omnet++ is currently gaining widespread
popularity as a network simulation platform in the scientific community as well as in industrial settings. It provides a discrete event
simulation environment. Oment++ provides a component architecture for models. The Modules are programmed in C++ and
assembled into components using NED. The main characteristic of OMNET++ is that the reusability of models comes for free.
Omnet++ is successfully used for complex and queuing systems. Ad hoc and sensor networks, …[38]. At the MAC and the physical
layers, we used IEEE 802.11 standard [39]. We mention that many studies [40] confirmed that WBANs require new Mac protocols.
Unfortunately, IEEE 802.11 may provide a good compromise between energy consumption and QoS. Table 2 shows the parameters of
our simulation.
Table 2. Parameters of the simulation.
Parameter Value
Propagation model Free space
Transmission power 2Mw
Sensitivity -85dBm
Snir threshold 4dB
Bandwidth 2MHz
Frequency 2.4GHz
Bitrate 2Mbps
Number of sensors 15
Packet generation rate Exponential (0.9)
The simulation duration for each experiment is set to 7 minutes and the result of all runs are averaged together to generate the
corresponding graph. In order to assess the performance of our approach, three key performance parameters have been chosen.
Namely, the maximum temperature rise, the average temperature rise, and the throughput are evaluated according to the simulation
time. Figure 2 presents the temperature rise of TAFTR. The blue curve illustrates the maximum temperature rise however, the red
curve shows average temperature rise. What is interesting in this figure is the convergence of the maximum temperature rise.

Figure 2. Temperature rise of TAFTR
Figure3. Throughput of TAFTR.
Figures4 and6 show the temperature rise of LTR and TARA respectively. It is clear that the maximum and the average temperature rise
increase linearly in LTR and TARA.
Figure 4. Temperature rise of LTR.

Figure 5. Throughput of LTR.
Figure 6. Temperature rise of TARA.
Figure 7. Throughput of TARA.
Figures 3, 5, and 7 illustrate the variation of the throughput versus the simulation time for TAFTR, LTR, and TARA respectively. It can
be seen that LTR has the best throughput and the performance of TAFTR in term of the throughput is better than TARA. It is apparent
from figures 3, 5, and 7 that the throughput is a logarithmic function.
Thus, the most interesting finding of TAFTR was the convergence of the maximum and the average temperature rise due to the
proposed simple routing rule. The simulation results show that the scalar field is an efficient technique that supports the routing
process to avoid the hotspots. The big problem of TARA concerns the withdrwal process while LTR uses the temperature which may
vary according to the simulation time.

CONCLUSION
The main goal of this paper was to investigate the field theory to route the packets that are generated by wireless implantable sensor
networks. Our contribution has shown how to avoid the hotspots without estimating the temperature of the neighboring nodes. Our
simulation results indicate the convergence of the maximum temperature rise of TAFTR. Futur work needs to be done to integrate QoS
constraints.
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Thermal-Aware Based Field Theory Routing in Wireless Body Area Networks