FEEL: Forwarding Data Energy Efficiently with Load Balancing in Wireless Body Area Networks

Abstract

In this paper, we propose a reliable, energy efficient and high throughput routing protocol for Wireless Body Area Networks (WBANs). In Forwarding Data Energy Efficiently with Load Balancing in Wireless Body Area Networks (FEEL), a forwarder node is incorporated which reduces the transmission distance between sender and receiver to save energy of other nodes. Nodes consume energy in an efficient manner resulting in longer stability period. Nodes measuring electrocardiography (ECG) and glucose level send their data directly to the sink in order to have minimum delay. Simulation results show that FEEL protocol achieves improved stability period and throughput. As a result it helps in continuous monitoring of patients in WBANs.

Full text

FEEL: Forwarding Data Energy Efficiently with
Load Balancing in Wireless Body Area Networks
M. M. Sandhu1, N. Javaid1, M. Akbar1, F. Najeeb1, U. Qasim2, Z. A. Khan3
1COMSATS Institute of Information Technology, Islamabad, Pakistan
2Internetworking Program, FE, Dalhousie University, Halifax, Canada
3University of Alberta, Alberta, Canada
Abstract—In this paper, we propose a reliable, energy efficient
and high throughput routing protocol for Wireless Body Area
Networks (WBANs). In Forwarding Data Energy Efficiently with
Load Balancing in Wireless Body Area Networks (FEEL), a
forwarder node is incorporated which reduces the transmission
distance between sender and receiver to save energy of other
nodes. Nodes consume energy in an efficient manner resulting
in longer stability period. Nodes measuring electrocardiography
(ECG) and glucose level send their data directly to the sink in
order to have minimum delay. Simulation results show that FEEL
protocol achieves improved stability period and throughput. As
a result it helps in continuous monitoring of patients in WBANs.
Keywords: Wireless Body Area Networks (WBANs), For-
warder node, Energy efficiency, Throughput, Threshold
I. INTRODUCTION
NOW a days, health care systems are facing challenges
due to increase in the elderly population and limited financial
resources. The total health care expenditure in Pakistan was
Rs. 3.791 billion in 2007-08 [1] and expected to increase in
the coming years. This appeals scientists and researchers to
find the best and economical solutions for health care. Remote
monitoring of patients’ vital signs presents a solution to the
increasing cost of health care. Therefore, monitoring of human
body and surrounding environment is important, especially for
patients, athletes and soldiers.
A WBAN consists of miniaturized, low power and intelli-
gent nodes deployed on, in or around the human body for
monitoring and diagnosis. It is one of the solutions to the
increasing cost of health care. We use the term sensors, nodes
and sensor nodes interchangeably in this paper. These sensors
collect data from the body and transmit via single-hop or
multi-hop mechanism to the sink. The sink further sends the
collected data to the medical server. The medical specialist
at a remote place can access the patients’ data and issue an
advice. WBAN provides long term health monitoring without
affecting the routine activities. It can also handle recovery from
surgical procedure and emergency situations [2].
In WBANs, non-invasive nodes can be used to monitor the
physiological parameters of the human body. These nodes send
the data to the nearest device e.g. cell phone, laptop, etc.
Therefore, it provides flexibility in terms of data gathering.
Furthermore, nodes give accurate data and are cost effective.
There are a number of applications of WBANs including real
time health monitoring of patients. They are also used to
monitor the soldiers in the field. The sensors deployed on the
body measure different physiological parameters and send data
to the concerned authorities. Interactive gaming is an emerging
application of WABNs. The players can physically move their
limbs and the sensors deployed on the body send data to the
gaming device. It provides enhanced entertainment.
The sensors used in WBANs have limited energy. It is difficult
to replace or recharge the batteries very often. Therefore, it is
necessary to use minimum energy in order to increase the
network lifetime. It also increases the throughput by sending
more packets to the sink.
There are different routing protocols used to enhance the
network lifetime. We propose a high throughput and reliable
routing protocol for WBANs having increased stability period.
We deploy eight nodes at different positions on the human
body. Two cases are considered for the placement of sink
on the human body. In the first case, sink is placed on the
chest while in the second case, sink is placed on the wrist.
Two sensors measuring ECG and glucose level communicate
directly to the sink. They possess critical data which is sent
to the sink immediately without any delay. The other six
nodes communicate to the sink via forwarder node. All nodes
are homogeneous and have same specifications. This scheme
uses energy efficiently and increases the stability period and
throughput of the network.
The rest of the paper is organized as follows. Section II con-
sists of related work, while section III discusses the motivation.
Radio model is shown in section IV and FEEL protocol is
presented in section V. Energy consumption analysis and sim-
ulation results are discussed in section VI and VII respectively.
Finally, section VIII gives the conclusion along with future
work.
II. RELATED WORK
A number of routing protocols are available in WBANs.
They can be categorized into single-hop and multi-hop com-
munication protocols. In single-hop communication protocols,
nodes send their data directly to the sink. On the other hand,
nodes in multi-hop communication protocols use intermediate
nodes to route their data to the sink.
A. Ehyaie et al. [3] propose an upper bound on the number
of relay nodes, sensors and their distance from sink. The
relay nodes are distributed on the human body as a network.
The sensors communicate to the relay nodes which further

route data to the sink. J. Elias et al. [4] give Energy-Aware
WBAN Design (EAWD) model. It gives the position and
optimum number of relay nodes in WBANs. Relay nodes
are responsible for data collection from sensors and routing
it towards the sink. They propose integer linear programming
for relay nodes for energy efficient routing. Authors in [5]
derive a propagation and radio model for energy efficient
communication in WBANs. They studied energy efficiency on
a line and tree topologies using these models. They found that
single-hop communication is inefficient in WBANs.
A two tier hierarchical architecture for WBANs is presented
in [6]. Authors present an interference free routing protocol.
Nodes send their data to cluster head (CH). This scheme
monitors multiple patients and routes their data to the base
station (BS). S. H. Seo et al. [7] present an adaptive routing
protocol. The priority and vicinity of nodes is taken into ac-
count for the selection of parent node for mobile human body.
T. Watteyne et al. [8] formulate a self organization protocol
for BANs. Nodes are grouped into clusters which send their
data through CH to reduce energy consumption and increase
the network lifetime. The protocol shows that clustering based
approach is suitable for WBANs. In [9], authors suggest a
WBAN protocol for monitoring the patients at home. The
home server collects the data from nodes deployed on the
human body and routes it to the medical server via internet.
In [10], C. Wang et al. propose a distributed WBASN for
medical supervision. The system contains three layers: sensor
network, mobile computing network, and remote monitoring
network. It collects and stores vital signs such as ECG, blood
oxygen, body temperature, etc.
M. Quwaider et al. [11] present a routing protocol for WBANs,
which counts for changes in the network. It uses store and
forward mechanism to increase the probability of successful
packet transmission. The location based packet routing is
developed in this protocol.
DARE [12] uses multi-hop scheme to monitor the patients
in a ward of the hospital. Sensors attached to the patients
send data to the body relay. The body relay aggregates the
received data and routs it to the sink. S. Akram et al. [13]
give THE-FAME to measure the fatigue in the soccer players.
They employ a composite parameter for fatigue measurement
which consists of a threshold parameter for lactic acid and
distance covered. The implanted sensor sends the data to the
nearest sink deployed at the boundary of the field. Similarly,
N. Javaid et al. [14] present a routing protocol for fatigue
measurement of a soldier. Three sensors are attached to the
body to measure temperature, heartbeat and glucose level in
the blood. Different scenarios are considered for the movement
of soldier.
Authors in [15] form virtual groups between doctors and
nurses for efficient patient monitoring. Virtual groups are
formed and modified according to the requirements of patients
and doctors. They propose a new metric called Quality of
Health Monitoring.
III. MOTIVATION
WBANs monitor human health with limited energy re-
sources. Different routing schemes are used to route data
towards sink, which further sends data to the medical server
or other monitoring station. Mobility-supporting Adaptive
Threshold-based Thermal-aware Energy-efficient Multi-hop
Protocol (M-ATTEMPT) [16] uses multi-hop communication
for normal data delivery to sink. Nodes communicate directly
to the sink for routing critical data. However, they deplete their
energy quickly resulting in shorter stability period and lack
of critical data from some nodes. Stable Increased-throughput
Multi-hop Protocol for Link Efficiency in Wireless Body Area
Networks (SIMPLE) [17] uses a cost function for forwarder
node selection and uses energy efficiently to prolong the
stability period. However, load is not distributed uniformly
on all the nodes. The placement of sink is also an important
parameter as it affects the throughput greatly. In addition, the
human comfort level must also be taken into account when
deciding the position of sink.
In order to improve the stability period and network through-
put, we propose FEEL for WBANs. Our contribution includes:
•Efficient criterion for forwarder selection.
•The sink is placed at two different locations on the human
body.
•FEEL for WBANs consumes energy efficiently resulting
in longer stability period. Nodes stay alive for more time
resulting in long time monitoring of vital parameters of
the human body.
•Increased stability period results in high throughput.
IV. RADIO MODEL
There are different radio models in the literature. We use
first order radio model given in [18]. The equations for first
order radio model are given below:
ET X (k, d) = ET X elect(k) + εamp (k, d)(1)
ET X (k, d) = ET X elect.k +εamp .k.d2(2)
ERX (k, d) = ERX elect(k) = ERXelect .k (3)
Where ET X is the energy consumed in transmission process
and ERX is the energy consumed by the receiver. ET X elect
and ERXelect are the energies required to run the electronic
circuit of transmitter and receiver respectively. εamp is the
energy required by the amplifier circuit, kis the packet size
whereas dis the distance between transmitter and receiver.
In WBANs, the communication medium is human body which
contributes attenuation to the radio signals. Therefore a path
loss coefficient parameter nis included in the radio model.
Equation for the transmitter energy consumption is:
ET X (k, d) = ET X elect.k +εamp .k.dn(4)
The energy parameters depend upon the hardware of the
system. We consider two transceivers, Nordic nRF 2401A
and Chipcon CC2420 , which are used frequently in WBAN
technology. The energy parameters for these transceivers are
shown in table I.

8
6
1
2
3
4
7 5
Sink
Node
Fig. 1. Deployment of nodes on the human body
TABLE I
ENERGY PARAM ETERS OF TRANSCEIVERS
Parameter nRF 2401A CC2420 Units
DC current (TX) 10.5 17.4 mA
DC current (RX) 18 19.7 mA
Min. supply voltage 1.9 2.1 V
ET Xelect 16.7 96.9 nJ/bit
ERXelect 36.1 172.8 nJ/bit
εamp 1.97 271 nJ/bit/mn
V. FEEL: PROPOSED PROTOCOL
In this section, we discuss a novel routing protocol for
WBANs. Uniform energy consumption of nodes is important
for long term health monitoring in WBANs. We propose
FEEL, a new routing protocol with improved stability period
and throughput. The following subsections give detail of the
proposed protocol.
A. Deployment of Nodes
In FEEL, we deploy eight homogeneous nodes on the
human body. Node 8is ECG and node 7is glucose level
sensor. These two nodes send their data directly to the sink.
We use two different topologies for the placement of sink on
the human body. In the first case sink is placed on the chest
while in the second case it is placed on the wrist. Fig. 1 shows
the placement of nodes and sinks on the human body. It also
shows the distances of nodes from sinks.
B. Start-up Phase
In the initial phase sink broadcasts a HELLO message
containing following three types of information.
•Location of sink.
•Location of neighbours.
•Information about possible routes to the sink.
The nodes receive this HELLO packet and update their routing
table. They also send information about their IDs and residual
energy status to the sink. Fig. 2 shows the contents of HELLO
message.
Fig. 2. Contents of HELLO message
C. Selection of Forwarder Node
In this section, we present the selection criteria of forwarder
node. In order to save energy and balance the energy con-
sumption of the network, FEEL selects a new forwarder in

each round. As sink knows the residual energy of all nodes,
it broadcasts the ID of the node having maximum residual
energy to make it the the forwarder node.
F orwardernode =N odemax(R.E)(5)
Where R.E is the residual energy of a node. Residual energy
is calculated by subtracting the consumed energy from initial
energy.
Energyr esidual =Energ yinitial −Energyconsumed (6)
The node having maximum residual energy is selected as a
forwarder node. All the neighboring nodes send their data
to the forwarder node. The forwarder node aggregates the
received data and routs it to the sink. In the next round, again
a new forwarder node is selected based upon the residual
energy. In this way, forwarder node rotates uniformly and
all the nodes get a chance to become a forwarder. Therefore,
energy is consumed more uniformly as compared to SIMPLE
and M-ATTEMPT resulting in increased stability period and
throughput.
D. Scheduling Phase
In this phase, forwarder node assigns Time Division Multi-
ple Access (TDMA) based time slots to its children nodes. All
nodes send their data to the forwarder node in their allocated
time slots. Proper scheduling of nodes minimizes their energy
consumption.
E. Data Transmission Phase
All other nodes except ECG and glucose level measuring
nodes send their data to the forwarder. The forwarder node
aggregates the received data and routs it to the sink. Nodes
measuring ECG and glucose level communicate directly to
the sink as they have critical data. If a node possesses energy
less than a threshold (γ), it communicates directly to the sink.
In addition, it does not further take part in the selection of
forwarder. This is done to save the data aggregation energy of
nodes. If a node has shorter distance to the sink than forwarder
node, it routs its data directly to the sink.
VI. ENERGY CONSUMPTION ANALYSIS
In this section, we develop equations for single-hop and
multi-hop communications. Energy consumed for single-hop
communication is:
ESH =ET X (7)
ET X is the transmission energy as given by:
ET X =k×(Eelect +εamp)×d2(8)
Where, Eelect is the energy consumed by electronic circuit.
Now, energy consumed during multi-hop communication is
given by:
EMH =k[m×(ET X ) + (m−1) ×(ERX +Eda)] (9)
Here, ERX is the reception energy and mis the number of
nodes.
VII. SIMULATION RESULTS AND ANALYSIS
In order to verify the performance of FEEL protocol, simu-
lations are performed in MATLAB. We study the performance
of the proposed protocol in comparison with SIMPLE and M-
ATTEMPT. The initial energy of all nodes is same i.e. 0.5
J. In simulation, we ignore the sensing energy consumed by
the nodes. Simulations are performed five times and average
results are plotted. Table II shows the values of different
parameters used in simulation.
We evaluate different performance metrics of the proposed
protocol. Introduction to some of the metrics is given below.
A. Network Lifetime
It is the total time till the death of last node. It
represents time for which the network operates. In
WBANs, a protocol is required to offer maximum
network lifetime.
B. Stability Period
It is the time before the death of the first node. It is
an important parameter in WBANs.
C. Throughput
Throughput is the number of packets successfully
received at sink.
D. Residual Energy
It is the difference of initial energy and consumed
energy.
E. Path Loss
It is the difference between transmitted power and
received power. It is represented in decibel (dB).
TABLE II
SIMULATION PARAMETERS
Parameter Value Units
ERXelect 36.1 nJ/bit
ET Xelect 16.7 nJ/bit
εamp 1.97 nJ/bit/mn
Eda 5 nJ/bit
do0.1 m
γ0.1 J
Packet size (k) 4000 bits
Frequency (f) 2.4 GHz
Initial energy (Eo) 0.5 J
A. Network Lifetime
Figs. 3 and 4 show the stability period and network lifetime
of FEEL protocol. Our protocol selects the forwarder node on
the basis of residual energy of nodes. So, energy is consumed
in a balanced way. As a result, stability period of FEEL
protocol is increased. In SIMPLE, the nodes closer to the
sink have more chance to become forwarder node. So energy
is consumed in an imbalanced way, decreasing the stability
period. FEEL has stability period of about 5428 rounds and
network lifetime of 7486 rounds in the first case. In the second
case, the stability period is increased to 5635 rounds. It is due
to the fact that sink is closer to most of the nodes in this
case. As a result less distance between nodes and sink causes
less energy consumption of nodes. So, the stability period is
increased.

0 1000 2000 3000 4000 5000 6000 7000 8000
0
2
4
6
8
10
12
81%
39%
Rounds (r)
Number of dead nodes
FEEL
SIMPLE
M−ATTEMPT
Fig. 3. Network lifetime for case −1
0 1000 2000 3000 4000 5000 6000 7000 8000
0
2
4
6
8
10
12
78%
38%
Rounds (r)
Number of dead nodes
FEEL
SIMPLE
M−ATTEMPT
Fig. 4. Network lifetime for case −2
B. Throughput
It shows the number of packets successfully received at
sink. WBANs require maximum data reception at the sink with
minimum packets dropped. We use Random Uniformed Model
[19] for packet drop calculation. The status of communication
link can be good or bad depending upon the probability. We
suppose the probability of link status to be good is 0.7. FEEL
protocol achieves higher throughput than M-ATTEMPT and
SIMPLE as shown in figs. 5 and 6. Throughput depends
upon the number of nodes which are alive. More nodes send
more packets so throughput increases. As the stability period
of M-ATTEMPT and SIMPLE is less, so less number of
nodes send packets resulting in less throughput. Whereas, the
FEEL protocol has longer stability period, so more nodes send
packets resulting in increased throughput. Throughput of the
FEEL protocol is even higher in second case due to increased
stability period.
0 1000 2000 3000 4000 5000 6000 7000 8000
0
0.5
1
1.5
2
2.5
3
3.5 x 104
Rounds (r)
Packets received at sink
FEEL
SIMPLE
M−ATTEMPT
Fig. 5. Network throughput for case −1
0 1000 2000 3000 4000 5000 6000 7000 8000
0
0.5
1
1.5
2
2.5
3
3.5 x 104
Rounds (r)
Packets received at sink
FEEL
SIMPLE
M−ATTEMPT
Fig. 6. Network throughput for case −2
C. Residual Energy
The residual energy of the network is shown in figs. 7 and
8. The FEEL protocol uses multi-hop communication for data
transmission to the sink. All nodes except 7and 8, transmit
their data to the forwarder node which routs it to the sink.
The forwarder node is selected at the start of each round.
The selection of new forwarder in each round saves energy.
In FEEL protocol a new forwarder node is selected in each
round, removing the burden of data transmission from a single
node. In M-ATTEMPT and SIMPLE, nodes die early due to
heavy traffic load and non-uniform load distribution.
D. Path Loss
Path loss shows the difference in the transmitted and re-
ceived power represented in decibels (dBs). The posture of
human body affects the signal. As a result path loss shows
different behaviour during the movement of human body.
There are different models used to estimate the path loss. It is
a function of distance and frequency as expressed in [20] and

0 1000 2000 3000 4000 5000 6000 7000 8000
0
0.5
1
1.5
2
2.5
3
3.5
4
Rounds (r)
Residual Energy(J)
FEEL
SIMPLE
M−ATTEMPT
Fig. 7. Residual energy for case −1
0 1000 2000 3000 4000 5000 6000 7000 8000
0
0.5
1
1.5
2
2.5
3
3.5
4
Rounds (r)
Residual Energy(J)
FEEL
SIMPLE
M−ATTEMPT
Fig. 8. Residual energy for case −2
shown below:
P L(f, d) = P Lo+ 10.n.log10(d
do
) + Xσ (10)
Where, P Lois path loss at reference distance doand nis path
loss exponent. The distance between transmitter and receiver
is d,Xis a gaussian random variable and σis the standard
deviation.
Path loss at reference distance dois given by:
P Lo= 10.log10(4.π.do
λ)2(11)
Where, λis the wavelength of electromagnetic waves.
Figs. 9 and 10 show the path loss in each round. In simulation,
we use a fixed frequency of 2.4 GHz from ISM band. We use
path loss coefficient of 3.38 and standard deviation of 4.1.
FEEL has lower path loss as shown in the figs. 9 and 10. In
the proposed protocol, path loss decreases after 4000 rounds.
It is due to the fact that some nodes die after 4000 rounds.
So less number of nodes have lower path loss. FEEL protocol
has lower path loss than M-ATTEMPT.
The improvement (%) provided by the FEEL protocol to M-
ATTEMPT and SIMPLE is shown in tables III and IV.
0 1000 2000 3000 4000 5000 6000 7000 8000
0
50
100
150
200
250
300
350
400
450
Rounds (r)
Path Loss(dB)
FEEL
SIMPLE
M−ATTEMPT
Fig. 9. Path loss for case −1
0 1000 2000 3000 4000 5000 6000 7000 8000
0
50
100
150
200
250
300
350
400
450
Rounds (r)
Path Loss(dB)
FEEL
SIMPLE
M−ATTEMPT
Fig. 10. Path loss for case −2
VIII. CONCLUSION
We propose FEEL, a new routing protocol for efficient
utilization of energy in WBANs. Nodes send their data to
the forwarder node which routs it to the sink. Forwarder
node is selected on the basis of residual energy. The node
having maximum residual energy is selected as a forwarder
node. Two nodes measuring ECG and glucose level send their
data directly to the sink as their data is critical. These two
nodes do not deplete their energy quickly and stay alive for
longer time. Simulations show that FEEL protocol achieves
longer stability period and throughput. In future, we intend to
implement Expected Transmission Count (ETX) link metrics
as discussed in [21,22].
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TABLE III
IMPROVEMEN T IN PERCENTAGE FOR case −1
Parameter Improvement (%) Improvement (%)
in M-ATTEMPT in SIMPLE
Stability period 153 22
Network lifetime 0.5 0.2
Throughput 72 7
Average residual energy 7 0.2
Average path loss 19 0.000247
TABLE IV
IMPROVEMEN T IN PERCENTAGE FOR case −2
Parameter Improvement (%) Improvement (%)
in M-ATTEMPT in SIMPLE
Stability period 162 27
Network lifetime -0.005 -0.0083
Throughput 93 20
Average residual energy 1.08 0.0098
Average path loss 17 -0.278
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