Resource aware and reliable cluster based communication scheme for wireless body area network using genetic algorithm

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
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ISSN 0976 – 6375(Online)
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© I A E M E

RESOURCE AWARE AND RELIABLE CLUSTER BASED
COMMUNICATION SCHEME FOR WIRELESS BODY
AREA NETWORK USING GENETIC ALGORITHM
Anu Singh1, Dr. Anil Kumar Sharma2
1M. Tech. Scholar,
2Professor Principal
Department of Electronics Communication Engineering
Institute of Engineering Technology, Alwar-301030 (Raj.), India
ABSTRACT
Wireless Body Area Network (WBAN) is a stimulating technology that has potential to bring
healthcare systems to a new level. The transmission unit in a WBAN is used to accumulate health
data from sensors, store and even partially process data locally, and transmits that data over wireless
links to a back-end processing server. In WBAN, due to the inadequacy in the availability of energy
supply, network endurance is a most important encounter. Since 90% of entire energy is disbursed
only because of communication purpose in WBAN, routing protocols play a key character towards
building such networks energy efficient. In this paper, we proposed a distributed energy-efficient
clustering scheme, this clustering scheme balance the selection of cluster heads using genetic
algorithm intelligently. The network efficiency will obtain in terms of network lifetime, throughput,
and end to end delay. This new intelligent scheme provides a better efficiency as compare to the
traditional approaches.
Keywords: Energy Efficient Clustering, Genetic Algorithm, Intelligent Clustering, Network
Lifetime, WBAN.
1. INTRODUCTION
The rapid growth of wireless technologies enables continuous healthcare monitoring of mobile
patients using compact biomedical wireless sensor. These small wearable devices- restricted in
memory, energy, communication abilities – are deployed on a patient; then, they self-configure to
form a networked cluster that is able to continuously monitor vital symptoms for e.g. Blood pressure

flow, fundamental temperature, ECG, oxygen saturation, CO2 concentration (for respiration
monitoring). Reliable and continuous collection of patient vital signs via wireless communications is
crucial for real time, which is the procedure of selecting patients centred on the severity of their
situation.Health monitoring is an emerging issue in wireless sensor network, where a number of
sensors are placed or implanted on human body to form a wireless body area network (WBAN).
According to IEEE 802.15, a WBAN is defined as a communication norm improved for small
powerdevices act on, in or all over the human body (but not restricted to humans) to assist a range
of applications comprising medical, consumer electronics/personal entertainment others. In
WBAN, the sensor nodes, known as body nodes, are connected wirelessly and controlled by a central
controller, known as a Body Node Coordinator (BNC). Here, in order to make the communication
effective among the body nodes or simply nodes, routing protocol plays a vital role. WBAN is a 24-
hour monitoring system where body nodes continuously monitor a patient’s various bio-signals such
as EEG, ECG, blood pressure, sugar level, heart beat rate, body temperature and BNC provides an
efficient means of communication between body nodes and the outside world. Here, depending on
the specific applications that which bio-signal of human body needs to be sensed, the number of
body nodes and their positions are varied. There are some sensing inflictions such as deep brain
stimulation sensing, cranial pressure sensing, ECG, EMG, EOG, EEG signal sensing where a
number of sensors nodes are placed consecutively, close to each other at a specific part of human
body for sensing a specific bio-signal and maintain a certain distance from BNC. The goal of this
paper is to quantify the limitation, in term of network lifetime, of the existing conventional routing
protocols and introduce a novel concept of an intelligent and energy efficient clustering based
approach as a prominent solution in WBAN. The primary objective of clustering is to maximize the
network longevity.
2
2. HEALTH MONITORING USING WBAN

A WBAN system is integration of small, low power, light weight sensor nodes. Nodes placed
on body may be wired or wireless. Wireless nodes are easy to handle and patients feelscomfortable
with their use as shown in Figure.1.
.
Figure 1: Working of WBAN system

3

WBAN is basically a three tire system, which includes sensors on body (first level), then
personal server (second level) and finally remote server (sink).Nodes are placed all over the body
whose data we require. Finally information is gathered and transmitted to base station. A particular
node transmits the whole data to base station called cluster head.
Figure 2: Node placement positions on body for measurement of various parameters
3. GENETIC ALGORITHM
Genetic algorithm is a part of evolutionary computing which is a rapidly growing area of
artificial intelligence. We can see that, genetic algorithm is inspired by Darwin's theory about
evolution. In a genetic algorithm, a population of strings (called chromosomes or the genotype of the
genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an
optimization problem, is evolved toward better solutions. The process of evolution usually starts
from a population of randomly generated individuals and occurs in every generation. In each
generation, the fitness of every individual in the population is evaluated, multiple individuals are
randomly selected from the current population (based on their fitness), and modified (recombined
and possibly randomly mutated) to form a new population. The new population so forms is now used
in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum
number of generations has been produced, or a satisfactory fitness level has been reached for the
population. Here, in proposed methodology, the fitness function for WBAN model is the optimum
value of selection probability of cluster head by minimizing the value of fitness function.
4. THE PROPOSED ALGORITHM
Clustering Hierarchy: We consider a wireless body area network that is hierarchically clustered.
Our proposed algorithm maintains such clustering hierarchy. In our protocol, the clusters are re-established
in each “round.” New cluster heads are elected in each round and as a result the load is
well distributed and balanced among the nodes of the network. Moreover each node transmits to the
closest cluster head and only the cluster head has to report to the sink and may expend a large
amount of energy, but this happens periodically for each node. In our protocol there is an optimal
percentage popt(determined a priori) of nodes that has to become cluster heads in each round
assuming uniform distribution of nodes in space.

Figure 3: Patient Node placement in network environment. (a) For 50 sensors placed randomly in
the filed of 10000 meter square area. (b) For 100 sensors placed randomly in the field of 10000 meter
4

square area
If the random number is less than a threshold T(s) then the node becomes a cluster head in
the current round. The threshold is set as: Where, r is the current round number (starting from round
0). The election probability of nodes G to become cluster heads increases in each round in the
same epoch and becomes equal to 1 in the last round of the epoch. In this paper we will focus on the
process of election of cluster head for the heterogeneous nodes, which means that not all the nodes in
the field have the same initial energy.
Optimal Clustering: Previous work results showed that the optimal probability of a node being
elected as a cluster head as a function of spatial density when nodes are uniformly distributed over
the sensor field. Optimal clustering means that energy consumption is well distributed to all patient
sensors maintaining the total energy consumption as minimum. Such clustering(optimal clustering)
highly depends on the energy model we use. So for the purpose of study we use similar energy
model and analysis as proposed in. According to the radio energy dissipation model illustrated in
Figure below, in order to achieve an acceptable Signal-to-Noise Ratio (SNR) in transmitting an L-bit
message over a distance d, the energy expended by the radio is given by:
Figure 4: Radio Energy Dissipation Model
Here is the energy dissipated per bit to run the transmitter or the receiver circuit,
and depend on the transmitter amplifier model we use, and d is the distance between the sender

and receiver. By equating the two expressions at d = d0, we have . To receive an L-bit
Sl. No Parameters Values
1. Initial energy (E0) 0.5 J/node
2. Transmitter Electronics (Eelec) 50 n J/bit
3. Receiver Electronics (Eelec) 50 n J/bit
4. Data Packet Size (l) 2000 bits
5. Transmitter Amplifier ( fs) if d d0 10or100pJ/bit/
6. Transmitter Amplifier ( mp) if d d0 0.0013 p J/bit/
5

message the radio expends . This radio model will help us to calculate the amount
of dissipated energy after every round based on distance vector based calculation.
Procedural Steps: First section is network initialization, in this phase we have to decide the network
parameters, like field area, number of devices, device parameters. The routing is based on distance
vector, means we have to make communication between our network devices through calculation of
distance vector in hop by hop manner (Node to Node communication is based on distance vector and
node to cluster head communication is also based on distance vector). For this, first of all we have to
calculate distance vector between network devices based on their position, and path and cost is
calculate according to these distance vectors values.After the initialization and setup phase
completed, the transmission phase is starts, in this phase, initially we calculate and update the energy
values of every device and it will update at every transmission round. First thing to start a
transmission round is the selection of cluster head, we defined a criteria based on certain energy
values to select a node as cluster head, and the node will be selected as a cluster head only if it has a
proper energy values to continue the round as cluster head. In the selection of cluster head a
probability distribution is used based on probabilistic clustering, here classification of such devices is
based on energy parameters like residual energy, initial energy, average energy, and the total energy.
The considered network parameters are shown in table below:
Table 1: Parameter Settings of the First-Order Radio Model
After the selection of cluster head, a cluster region created around the particular cluster head,
and nodes belong to that region are labeled as cluster members. In transmission phase, the cluster
members transmit their data to cluster head and cluster head transmit the collected data to the
destination directly. The clustering and routing procedure continues till the network devices alive,
the node with proper energy levels is selected as cluster head one after another every round. After
every transmission round, device’s residual energy is calculated with the radio energy model for
wireless communication network.

Set-up of Field and Initialization of Parameters. Add some nodes with some
extra energy to make the network heterogeneous.
Calculate selection probability based on Genetic Algorithm
Set-up optimum value of probability based on energy values and GA
Selection of node, as a cluster head based on selection probability
The node will continue round as a cluster head and the region around the selected
node and the destination will be the cluster region. All other nodes which is a part
of this region participate in routing as a cluster member
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If not selected
If selected
Figure 5: Flow chart for the procedural steps evolved
5. RESULTS
This work is applied in a Wireless body area network Field of Area 100×100 m. Also, the
base Station is placed at the center of patient field initially. Initially the dissipated energy is Zero
residual energy is the Amount of initial energy in a Node, Hence Total energy also the Amount of
residual energy because it is the sum of dissipated residual energy. Simulations are carried out in
MATLAB R2013b (Version 8.2.0.703). The 100 Nodes are placed in the randomly manner in the
whole field, the number of clusters directly depends upon the number of cluster head. A single
cluster head is assigned to clusters which act as a sub-destination and route data from other cluster
member nodes to the destination (Sink or Base Station).
Node distance between the cells: The distance vector calculation is a very important process while
developing a communication protocol for body area network, as energy is directly dependent to
distance, so it is necessary for a system to calculate the distance between all patient devices with
each other. Let assume that the node position in the cell is . It can be defined the distance
between node and the other node as:
Figure 6: Shows the distance vector calculation between different devices. This distance information
is very useful for data communication based on distance in case of energy saving schemes

Throughput of receiving bits: It is the ratio of the total number of successful packets in bits
received at the sink or base station in a specified amount of time.It is measured in terms of
bits/second.
7

5
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
x 10
x(Number of Rounds)
y(Throughput (bits))
Proposed Protocol
Energy-efficient mechanism [34]
LEACH based Routing
Figure 7: The graph above shows a comparative view of obtained network throughput from both the
proposed scheme and the LEACH and Energy Efficient Scheme [1]
End-to-End Delay: It is the delay that could be caused by buffering during route discovery, queuing
delays at interface queues, retransmission delays at the media, and propagation and transfer times.
Hence, only the nodes with higher weight amongst the other nodes can fulfil the criteria
above and hence a node can transmit data as a cluster head for a longer period which results in
increment of network lifetime and throughput. After a higher weight node becomes Cluster Head,
Energy Models are applied to calculate the Amount of Energy Spent by it on that Particular Round
and complete the round of steady state phase. When a node residual energy is zero then the node is
called dead and is terminated from the network environment.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
x(Number of Rounds)
y(End to End Delay)
Proposed Protocol
LEACH based Routing
Figure 8: The graph obtained shows a comparative view of end to end delay measured at the base
station or delay introduced by the routing scheme in delivering data packets to the base station from
both the proposed scheme and the LEACH Energy Efficient Scheme

Network Lifetime: It is defined as duration of time until the first node failure occurs due to battery
depletion. Any decrease in lifetime will automatically decrease the usability, which will affect the
productivity of overall system. Figure-8 shows a comparative view of death of Patient nodes with
each round for both the proposed scheme and the LEACH and Energy Efficient Scheme.
8

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
100
90
80
70
60
50
40
30
20
10
0
x(Number of Rounds)
y(Lifetime of Wireless Network)
Proposed Protocol
LEACH based Routing
Figure 8: A comparative view of death of Patient nodes with each round for both the proposed
scheme and the LEACH and Energy Efficient Scheme
6. CONCLUSION
In this paper, an energy-efficient protocol for heterogeneous networks for monitoring patients
is proposed. Some of the sensors monitor data continuously while, others monitor only when a
certain threshold level is reached. The protocol defines a genetic algorithm and energy based criteria
for the selection of cluster heads in cluster based communication. The results clearly show that, the
network lifetime and the stability period in terms of more nodes to stay alive and in terms of reduced
energy consumption, our proposed protocol is better than the compared to Energy efficient
protocol[1]. It also provides reduced delay in transmitting packets to the network towards the
destination which makes it a feasible protocol for the networks where there is no room for huge
delay. This work proposed “Resource Aware and Reliable Cluster based Communication scheme for
Wireless Body Area Network using Genetic Algorithm”, which is further compared by Energy
Efficient clustering for WBAN[1]. This protocol is used to determine the optimal probability for
cluster formation in WBANs. As simulation results show that in terms of network lifetime of sensor
node, since the use of the optimal probability yields optimal energy-efficient clustering.Results
shows that, this protocol successfully extends the stable region by being aware of heterogeneity
through assigning probabilities of cluster-head election weighted by the relative initial energy of
nodes, also the lifetime of network extended to more than 4500 rounds in this protocol. Proposed
algorithm is implemented using MATLAB and tested multiple times and results are satisfactory.
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