BACHS-Battery Aware Cluster Head Selection
Wireless Sensor Network (WSN) serves the purpose of monitoring the physical
quantities from the real world. The WSN are battery powered and energy depleting
in nature. Energy enhancement of WSN has attracted many researchers in enhancing
the energy of the Sensor Node (SN). This study concentrates on enhancing the
lifetime of the WSN and increasing the throughput of the network based on the
battery voltage of the SN. Energy depletion due to radio model is taken into
consideration, the distance between the sender and receiver plays the major
role in lifetime of WSN. Apart from distance, the battery curve of the SN is
taken into consideration. Node functionality is assigned as per these factors
and an unequal clustering is formed for avoiding energy hole attack in the
WSN. The proposed BACHS algorithm outperforms the other 2 protocols in terms
of lifetime by 1.7.
Received: January 03, 2014;
Accepted: March 14, 2014;
Published: April 16, 2014
Small tiny embedded sensing machines together communicate with each other to
form a network called Wireless Sensor Network (WSN) (Akyildiz
et al., 2002). These tiny embedded machines monitors the temperature,
pressure, humidity and other physical parameters to monitor the event taking
place in the Region of Interest (ROI). The monitoring serves the purpose in
military, agriculture, health care, industry and several regions. These sensor
nodes are powered by battery since it monitors events in an anti-third environment.
Replacement of power resource or charging up the battery resource in these areas
is termed to be impossible. This makes the WSN prone to energy constraint and
making researchers to concentrate on building a energy efficient network which
survives for greater lifetime duration (Lindsey and Raghavendra,
2002; Chamam and Pierre, 2010; Farooq
et al., 2010; Loscri et al., 2005;
Gou and Yoo, 2010). The WSN has 2 basic architecture
namely layered architecture and clustered architecture. The layered architecture
is mainly meant for monitoring small Region of Interest (ROI). Clustered architecture
based sensor network are mainly used for monitoring very large acres of area
such as crop field, industrial areas and other volcanic regions (Jung
and Nittel, 2008; Chung and Yang, 2009; Al-Ali
et al., 2010).
Figure 1 shows the basic clustered architecture of the WSN.
The sink is where the data from all the SNs is collected and surveillance is
done in the sink. The cluster has their own individual cluster head which sends
the data to the sink in a single hop fashion (Yang and Zhang,
2009; Xin et al., 2008; Wang
et al., 2011). Energy efficiency is an important issue which affects
the lifetime of the WSN (Dhulipala et al., 2012).
LEACH is the first hierarchical clustering protocol (Heinzelman
et al., 2002; Akyildiz et al., 2002).
||Wireless sensor network scenario
Farooq et al. (2010) proposed multi hop routing
with Low Energy Adaptive Clustering Hierarchy (MR-LEACH) which lags by addressing
the energy issue existing in the WSN. Tabibzadeh et al.
(2009) proposed a hybrid routing protocol for prolonged network lifetime
in large scale WSN. Handy et al. (2002) proposed
a deterministic cluster head selection algorithm based on hierarchical method.
Sengottaiyan et al. (2010) proposed a hybrid class
of routing protocol in wireless sensor network proposing combination of two
algorithms. This study presents the algorithm which takes battery end voltage
and current consumption of the battery during each participation in the network
and the cluster head is selected. By choosing the battery end voltage as the
criteria the network shown an improved lifetime and unequal clustering mechanism
in the WSN.
MATERIALS AND METHODS
LEACH: In LEACH protocol, each node generates a random number in the interval
between (0,1) at the beginning of election, if the number is less the threshold
Pi(t) calculated from equation given below:
where, K is the No. of Cluster Heads (CH), N is No. of nodes in the network,
r is current No. of rounds and Ci(t) = 0 if node already been a CH
and 1 otherwise.
The ordinary nodes in the clusters send the data to the CH of the corresponding
cluster and CH contributes the data to the sink in a single hop. The CH after
dissipating the energy below a certain level, next CH is being selected for
its contribution as CH for the corresponding cluster. Equation
1 shows the election probability of each node to be a CH which means that
all the nodes can themselves have the chance to get elected as a CH no matter
what far the distance they have with the sink. If the elected node is far away
from the sink, node dissipates more energy in transceiving the data with the
sink thereby it dies soon and makes the next election to the network. If the
probability of nodes nearby the sink is high, it ensures the energy dissipation
of the node to be low and in making an enhanced lifetime of the network (Akyildiz
et al., 2002).
ALEACH: Similar to LEACH protocol, ALEACH considers the residual energy
of the corresponding node so as to elect the CH as per the residual energy.
Node with residual energy gets high probability of being a CH (Al-Fares
et al., 2009):
||Current energy of the node
||Energy at the beginning
Radio energy model: The transmission of data is based on the radio model
Receivers energy consumption is:
where, Eelec is the energy dissipated per bit to run the transmitter
or the receiver circuit. εfsd2(pJ/bit-m2),
εmpd4(pJ/bit-m-2) = Energy dissipated
per bit to run the transmit amplifier based on the distance between the transmitter
and receiver. Etx = Energy dissipated during transmission of data.
Equation 3, 4 signifies the energy dissipated
by the node in sending the data between nodes. The energy consumed by node in
sending the data and receiving the data is given in Eq. 3,
4. The energy dissipation of sending and receiving data with
respect to distance is given in Eq. 3. The energy of the sensor
nodes is represented in Joules. The equation helps in simulating the network
and to show the energy dissipation in the network.
Proposed work: The LEACH protocol and ALEACH protocol selects the CH
based on the random number and residual energy of the nodes in the network.
The proposed algorithm selects the CH based on the voltage level of the battery
in each sensor node. The voltage of the battery is exponentially decaying in
nature. The energy required to transmit a bit or to receive a bit is given in
Eq. 3. Since the voltage of the battery is decaying in nature,
in order to meet out the required energy, it demands more current from battery
to meet out the requirement. CH has the major role in WSN of transceiving the
data (i.e.) receiving and transmitting the data. It multihops the data to the
sink. Therefore, an additional burden is given to the CH. In case of end node,
it either transmits or receives data to the CH or from CH. This algorithm proposes
a methodology of electing a cluster head based on the Eq. 5.
The equation illustrates the battery drain curve of the SN. Probability of election
of cluster head favours the cluster head selection and increases the lifetime
of the network.
Figure 2 illustrates the proposed formation of cluster based
on the battery voltage. Three stage of sensor nodes are depicted here, one with
high charge indicating peak voltage (with three strikes). Second with medium
voltage (with two strikes) and third one with very low voltage (with a single
Figure 3 depicts the proposed algorithm based WSN, the node
with more residual charge is likely to become the cluster head. The sink is
always a non-power starving in nature.
||Proposed work CH selection, (a) Cluster and (b) Voltage curve
of SN battery
||Clustered architecture of the proposed work
Once the sensor node drains its charge to a particular level, it loses its
quality to be a cluster head and calls for a reelection. Then the candidate
with high residual charge is selected as cluster head.
Mathematical proof: The voltage curve of typical sensor node is given
by the Eq. 5:
where, F(x) = Favor factor of a node to become a cluster head.
Equation 5 represents the favor factor of a node to become
a cluster head for the BACHS. Equation 5 describes the exponentially
decaying voltage curve of the wireless sensor node. The voltage decreases as
the charge stored in the battery is dissipated:
where, E is the energy required for transceiving purpose, V is the voltage
of the battery, I is the current consumed by the sensor node, and t is the No.
of bits transmitted time.
Equation 6 represents the energy equation of the battery:
where, P = VxI total power dissipated in sensor node.
Equation 7 represents the energy equation of the battery
dissipated for a particular duration with respect to power and time.
When the voltage of the battery powering the sensor node reduces, the sensor
node extracts more amount of current from the battery to compensate the demanding
power required for transceiving operation. Hence more current is been dissipated
more than the rated current hence draining the battery soon. By scheduling the
operation based on the battery voltage level the battery life is extended.
||All the sensors are deployed in the ROI (Region of Interest)
||All nodes are energy starving and initially with same energy
||The nodes are simpler or CHs
||The nodes are immobile
||Sink node is connected with permanent power source
Simulation results and network parameters:
RESULTS AND DISCUSSION
Figure 4 represents the sensor nodes are randomly distributed
in the ROI. The nodes are deployed in ROI in the random manner. The nodes are
distributed within the ROI only. All the nodes have equal energy at the beginning
of the simulation.
Figure 5 illustrates the number of nodes vs. number of rounds
of the proposed BACHs algorithm. The algorithm survives 1.7 times than the other
2 algorithms. The first node death in case of BACHs is also delayed when compared
to other 2 protocols. Performance evaluation of proposed algorithm is done using
MATLAB simulator, initially 200 nodes were deployed randomly in the region of
interest and probability of becoming a cluster head among the nodes is given
as 0.1. The sink is located in 250, 750 location of sink is placed far away
from the ROI because this gives a good results on showing the protocol enhances
unequal clustering. The energy efficiency of the 3 algorithms with respect to
network lifetime is examined. The unequal clustering approach solves the HOT
spot problem, thereby avoiding energy hole in the sensor network. BACHS also
avoid individual node overloading and overloading the nodes nearer to the sink.
The other 2 protocols provides energy holes in the network causing a break to
the connection between the sink and the network.
Voronoi diagram illustrates the node within a single cluster, it also depicts
the boundary of clusters in the ROI. The common boundary sharing 2 clusters
are termed as voronoi edge, Fig. 6 represents the Voronoi
diagram of the proposed BACHs.
||Sensor node deployment in ROI (Region of Interest)
||No. of rounds vs. No. of nodes
||Voronoi diagram of the BACHS
The Sink in the simulation is located in location (250, 750) in order to check
whether the algorithm is following unequal clustering. The voronoi diagram shows
the cluster near to the sink is small and cluster far away from the sink is
large, showing an unequal clustering approach.
This study proposed a novel cluster head selection method based on the voltage
of the battery. This heuristic algorithm proposes that BACHS algorithm could
perform better than the earlier works. Results show the better performance to
support our algorithm. By optimally selecting the CH based on the voltage of
the battery the lifetime of the battery and the network lifetime is improved.
This algorithm serves its purpose in industrial wireless sensing systems, agricultural
monitoring purpose and other remote monitoring location.
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