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Intelligent Communication and Computational Research
PRIYANSHU GUPTA, PALLAV RAJ
1
Energy Efficient Diagonal based Clustering protocol in Wireless
Sensor Network
Priyanshu Gupta a, Pallav Raj a , Shwetanshu Tiwari a,Puja Kumari a, Pawan Singh Mehra b*
aDepartment of CSE, G.L.Bajaj Institute of Technology and Management, Greater Noida
bDepartment of CSE, Jaypee Institute of Information Technology, Noida
*pawansinghmehra@gmail.com
Email: pawansinghmehra@gmail.com
Abstract: In Wireless Sensor network (WSN), the sensor nodes have short life span due to its limited power source (i.e. battery). So its
consumption needs to be optimized to increase the life span of WSN. We have developed and implemented a competent algorithm for cluster
head selection and efficient transmission through hop by hop in WSN. In our proposed work, we have divided the area diagonally into two zones.
The distance between sensor node and base station (BS) or transmission of data from SN to BS is decided by the zone in which node exists. In
cluster based WSN, information gathered from every SN is sent to their respective coordinator or CH of every cluster. The simulation results
were compared with state of the art algorithms depicting longer stability period, less dead nodes per round, larger average energy and extended
lifetime.
1. Introduction
WSN play a main role in far real time unattended application areas. They
are like analysing the environmental conditions, medical monitoring,
health monitoring, Traffic monitoring, Industrial application, Weather
monitoring, climatic conditions etc.(Akyildiz, Su, Sankarasubramaniam,
& Cayirci, 2002)(Kułakowski, Calle, & Marzo, 2013).. The advancement
of embedded electronics made it possible to expand the WSN in almost
every field. WSN is highly distributed networks which control more
complex functions in data collection and processing by deploying micro
sensor nodes in huge numbers. WSN has fixed power. It also has
restricted capacity for processing. In few cases, we may use solar energy
but it does not guarantee continuous supply as there are many hindrances
in smooth functioning of WSN (Want, Farkas, & Narayanaswami, 2005).
Major functions done by sensor node is to collect information about the
actual phenomenon from the surrounding and then working on that
information(Kumar, Mehra, Gupta, & Jamshed, 2012; Manju, Chand, &
Kumar, 2016). After that it communicates with rest of the SENSOR
NODEs. Utilization of energy is major addressing issue in WSN.
Researchers also focus on better performance of the network in many
areas or application. Clustering plays an important role in effectively
managing the network topology by partitioning the network and grouping
of nodes.
The total amount of energy which is needed by a bit to transfer the
data is equal to the individual SENSOR NODE’s processing of many
instructions in it (Kumar, Mehra, Gupta, & Sharma, 2013; Manju, Chand,
& Kumar, 2017; Tanwar, Kumar, & Rodrigues, 2015). Physical medium
is used by BS to transmit the sensed data. All the SNs are connected to
each other either by single hop or multi-hop. The sensor nodes have their
own property and parameters(Wang, Shi, Li, & Chen, 2012). The
performance of WSNs are analyzed on various parameters like type of
deployment of SNs, network lifetime, latency, transportation ratio of
packet, connectivity, reliability, stability, energy efficiency and cost
minimization of energy consumption. Sensors nodes are remotely located
with no direct access to power supply. Sensor node is a multi-functional
wireless device. The main issue with SENSOR NODE is the use of
battery due to which there is limited supply of power. As SENSOR
NODEs are remotely located so there is not any other proper way to have
continuous power. So, we need to develop an efficient algorithm which
helps in minimizing the use of power supply i.e. batteries. The type of
sensor decides the energy expenditure of the sensor’s sensing subsystem.
In this way WSN is used with its own advantages and limitations.
In this proposed work, we have developed an efficient algorithm on
the basis of division of the area into zones. We have divided the area into
two zones and then form the clusters and after that cluster heads are
chosen. We have used K-Means algorithm for the formation of clusters.
The BS is located at remote area from the network. The sensed data is
transmitted from SN to its CH and then by using multi hop method sensed
data is further transmitted to the BS.
The literature has been divided into following segments. Related work is
covered under Segment 2. Segment 3 covers System model. Proposed
work is covered in segment 4. Segment 5 contains details about
Electronic copy available at: https://ssrn.com/abstract=3565781
Intelligent Communication and Computational Research
PRIYANSHU GUPTA, PALLAV RAJ
2
simulation and performance evaluation. Finally, we conclude under the
segment 6.
2. Related work
In WSN, we know that clustering is one of the prime methods for
increasing the network efficiency(Azad & Sharma, 2013; P. S. Mehra,
Doja, & Alam, 2016). It involves grouping of nodes i.e. clustering and
selecting a cluster head (CH) among them. Major challenge in WSN is the
selection of suitable cluster head. There are various algorithms available
which uses clustering technique for efficient utilization of energy in
WSNs(Dattatraya & Rao, 2019; P. Mehra, Doja, & Alam, 2019; P. S.
Mehra, Doja, & Alam, 2015). One of the pioneer techniques is
LEACH(W. R. Heinzelman, Chandrakasan, & Balakrishnan, 2000).
LEACH algorithm follows distributive approach. It selects CH on the
basis of local criteria. The premature death of the SNs in network which is
static is prevented by alternation of CH. LEACH satisfies even
distribution of energy wastages by rotation of CH randomly. The
information directed towards BS is lessened by including fusion of the
collected data. The aim of this protocol is to decrease the energy
utilization needed to form and maintain clusters. This helps in improving
the life time of the WSN. LEACH-C (W. B. Heinzelman, Chandrakasan,
& Balakrishnan, 2002) is a hierarchical protocol. In this, nodes other than
CH transmit data to it, and the CHs are responsible for aggregation and
compression of data. After that, CHs passes it to the base station (sink).
To determine whether a node can be CH or not in a particular round,
stochastic algorithm is used. LEACH-C assumes that each node has
enough powerful radio which can straight reach to the base station or the
cluster head which is nearest of it. But, continuous consumption of radio
power can reduce the energy very fast.
Another protocol enhancing LEACH is stable election protocol
(SEP) (Smaragdakis, Matta, & Bestavros, 2004). It has heterogeneous
clustered sensor network. It is heterogeneous-aware protocol which
increases the time span prior to the first node death (FND). It is crucial for
application in which feedback from SENSOR NODE is reliable. SEP is
based on weighted election probabilities. In this work, each node’s
remaining energy is used to find whether it will become CH or not. In
SEP, at each round, it is not necessary to know the overall energy of the
network. The major disadvantage of SEP method is that selection of CH
among two different nodes is static, which may lead to the death of distant
nodes.
In (P. S. Mehra, Doja, & Alam, 2018b), authors discussed a
protocol in which two different BS are used on either side of network
area. This protocol shows network lifetime is increased by using two-level
energy heterogeneity. Author in (Nayak & Devulapalli, 2016) proposed a
mechanism for proper election of cluster head. It implements FL by
extending LEACH. Cluster’s centrality, BS mobility and remnant battery
level are inputs to FIS. Super CH is selected by Mamdani rule. Lifetime
and stability are the factors used to depict improved performance of this
protocol. Authors in (Manju, Chand, & Kumar, 2018) proposed energy
based meta-heuristic for target full coverage in WSNs. Each sensor cover
is represented as chromosome where fitness function is used to decide the
selection of sensor in chromosome thereafter. In HEED(Younis & Fahmy,
2004) uniform cluster are formed across the area. It considers consider
energy left and node density.
In (Singh, Chand, & Kumar, 2016) author proposed HEED with
various level of heterogeneity. It is based on the parameters with models
& display energy efficiency with better output. In EEFL-CH, (Alami &
Najid, 2016) the author uses fuzzy logic to increase energy efficiency
which is an improved version of LEACH. Remnant energy, closeness to
BS and expected efficiency are responsible for CH formation in this
protocol. An algorithm based on clustering is proposed in (Nayak &
Vathasavai, 2017). It uses a type-2 FL which helps in multi hop
communication to handle the decisions which were uncertain for the
previous model. LEASE (P. S. Mehra et al., 2015)protocol is propound
for energy efficient clustering. It contemplates remnant energy for
choosing good candidate for cluster head role.
PEGASIS (Lindsey & Raghavendra, 2002) is a cluster-based routing
protocol. In this local collaboration among SENSOR NODE is increased
which helps in improving the network lifetime. In (Tamandani, Bokhari,
& Shallal, 2017)author proposed a fuzzy based approach. It helps in
balancing the loads and reducing the energy depletion. Z-SEP (Faisal et
al., 2013) is based on SEP. In Z-SEP, there are three zones for the targeted
area. Near BS normal nodes are kept or placed, while advance nodes are
placed farther from BS. Direct transmission is used for normal nodes
while cluster based transmission is used for advanced nodes as they are
placed farther from BS.
We have developed a protocol which is better than the existing
protocols. We have performed a performance evaluation based on
dividing area of zone into different types (P. S. Mehra, Doja, & Alam,
2018a). First, we used complete area without dividing it then we
considered area divided into two equal halves. Later, we divided the area
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Intelligent Communication and Computational Research
PRIYANSHU GUPTA, PALLAV RAJ
3
into two halves but diagonally. After analysing all these experiments, we
found that area without any division is least performing while area with
half division is most performing. Data is transferred using multi hop in
our proposed model. In this paper, K-Means algorithm for clustering is
used for the formation of clusters(Fakhet, Khediri, Dallali, & Kachouri,
2017).
3. System Model
Energy is conserved in WSN by forming cluster(W. R. Heinzelman et al.,
2000). Every cluster has a CH which is the main head of that cluster. The
data is collected from targeted area by the SENSOR NODE and then they
send it to CH of their cluster(P. S. Mehra et al., 2018b). Every SENSOR
NODE is responsible for sensing data. Among those SENSOR NODEs,
any one node is selected as CH. The CH collects the sensed data from
nodes of its cluster and sends it through multi-hop to the BS. Tremendous
amount of energy dissipation takes place in this process of sharing the
information. Therefore, our aim is to increase energy efficiency of SNs
and lifetime of WSN.
3.1 Assumptions
The main motive of WSN is to monitor the surrounding by deploying
SENSOR NODE in that area where data collection is required. The
network’s size is assumed to be R = a*b square meter with n number of
SENSOR NODE which are distributed over the given area R. The
position of BS is far away from targeted area. Following are few
assumptions that are needed to be made for this proposed work:
1. The deployed SENSOR NODE are randomly scattered in the
network.
2. Both BS and SENSOR NODE are static in nature.
3. After deployment of SENSOR NODE the power supply is
irreplaceable.
4. The BS is situated far away from target area.
5. The energy of BS is limitless.
6. The separation distance and transmission power are in line.
7. Power exhaustion is only reason for failure of SENSOR NODEs.
8. The report generated by network is continuous in nature.
3.2 Network Model
The SENSOR NODE is randomly scattered throughout the field. The
target area considered being of 100x100 m2 and division is done on the
basis of:
Diagonal Equal Division
Region 1: X <= Y
Region 2: X >= Y
We used advanced node (P. S. Mehra et al., 2018a) for increasing lifetime
also. Data is transferred using multi-hop from one Cluster to another.
3.3 Radio Model
The model for amount of energy consumed similar as work shown in (W.
R. Heinzelman et al., 2000).
(1)
Where d is the separation distance and do is 87.5m [14].
The energy necessary for reception of m-bits message is as follows:
(2)
where d is the distance between CH and BS. Similarly, the energy used
by cluster member is computed by Eq. (4)
(3)
4 Proposed Work
This segment throws light on the proposed protocol. We have compared
the performance of SENSOR NODE with LEACH and Z-SEP. In our
protocol, area is divided equally by a diagonal. After dividing area into
zones, we form clusters and both the zones have equal number of clusters
and then for each cluster, we decide CH. We use the concept of
heterogeneity in terms of energy level to develop this protocol. For
transmission of data we use multi hop. We have taken two main
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Intelligent Communication and Computational Research
PRIYANSHU GUPTA, PALLAV RAJ
4
parameters for the selection of CHs. First is distance of SENSOR NODE
from there point to BS or division area in divided area. Second, we
consider remaining energy of a node. Also, if a node is near to base station
as compared to diagonal (division line), then data is transmitted without
passing through the line. We also deploy some advanced nodes to increase
performance of protocol. These advanced nodes help in increasing
efficiency of WSN. As we have 'N' total number of sensor nodes and ID is
their primary ID. Also, there is Cluster head CH for each cluster and each
CH is store in array CH_list. Node(i).EN is the initial energy for each
normal node and Node(i).EA is initial energy for each advance node
which is also CH initially for each of their clusters. As discussed, there are
two zones, Each sensor node is having its zone information in
Node(i).zone. Distance between each node Node(i) and separating line is
Node(i).dist. BS is located at a distance of Node(i).DTBS from each node.
K- means clustering Node(i).cluster is used for clustering. When the dead
node is less than N then if the zone number is 1 then Node(i).EN of each
node and also its distance to line Node(i).Distance is compared with all
other nodes of each cluster after each round if the distance of any other
node is less than the existing node and also its energy is less than new
node then that new node is selected as a CH for next round otherwise CH
remains same for next round also. For Zone 2, energy criteria remain the
same while node(i).DTBS is checked in this zone to decide CH. After
checking this in each zone for each cluster after each round CHs are
maintained in CH.list. Initially, the advance node is decided as CH.
Node(i).clusterhead_distance helps in deciding node to cluster head
distance of the respective cluster. Also Node(i).EN of each node is
checked after each round and if it is less than or equal to zero then we
increase the number of dead nodes after each round depending on the
number of nodes having Node(i).EN less than or equal to zero. After
deciding CH using the above algorithm data is sent using the multi-hop
method to the BS which minimizes the energy utilization for each node.
The process is described in Algorithm 1 which continues until all the
nodes are dead.
Algorithm 1: Protocol Implementation
1 : N // Total no. of nodes in the network
2 : ID // Primary ID of SN
3 : CH_list // Array for cluster head
4 : Node(i).EN // Initial Energy
5 : Node(i).EA // Advance Energy
6 : Node(i).zone // Zone of Node
7 : Node(i).dist // Distance from diagonal to Node(i)
8 : Node(i).DTBS // Distance from BS to Node(i)
9 : Node(i).cluster // K-means Clustering
10 : WHILE(Dead<N)
11 : {
12 : IF(ZONE==1)
13 : D=Node(i).Distance
14 : E=Node(i).EN
15 : WHILE(Node(i).Distance<D && Node(i).EN>E)
16 : {
17 : D=Node(i).Distance
18 : E=Node(i).EN
19 : }
20 : CH_list // ID of all CHs
21 : ENDIF
22 : IF(ZONE==2)
23 : D=Node(i).Distance
24 : E=Node(i).EN
25 : WHILE(Node(i).DTBS<D && Node(i).EN>E)
26 : {
27 : D=Node(i).DTBS
28 : E=Node(i).EN
29 : CH_list // ID of Sensor Nodes
30 : }
31 : ENDIF
32 : Node(i).CH_distance // Node to CH distance
33 : IF(Node(i).EN<=0)
34 : Dead=Dead+1
35 : ENDIF
36 : }
Terminate
5 Simulation and Results
For the performance analysis, we have simulated proposed work in
Matlab. In this proposed algorithm, our main focus is on increasing
runtime of wireless sensor networks of randomly distributed nodes in a
particular area. Base station knows the location of sensor nodes which is
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Intelligent Communication and Computational Research
PRIYANSHU GUPTA, PALLAV RAJ
5
decided earlier. The sensors nodes can communicate with each other
directly. They can also transfer data to and fro from BS. The nodes sense
data periodically from their environment. They send data to BS in each
round. The CH combines data received from other nodes and their own
data into one data packets. This one data packet is sent to BS instead of
multiple data packets. To validate the proposed work simulation is
necessary. We use deployed randomly 200 nodes in a network in the
range of (x=0, y=0) and (x=100, y=100). Location of our base station is at
(50,150).Size of data message in every round is 4000 bits. 200 bits is size
of short message. 0.5 J is initial energy of nodes and Popt = 0.05. The
parameters are described in Table 1.
Table 1 – Simulation parameters
Parameter
Value
Transmitter Electronics (ETx-elec)
50nJ/Bit
Transmitter Amplifier (Eamp)
100pJ/Bit/m2
Receiver Electronics (ERx-elec)
50nJ/Bit
Data Aggregation (EDA)
5nJ/Bit
Transmit Amplifier ( Ɛmp)
0.0013pJ/Bit/m4
Transmit Amplifier ( Ɛfs )
10pJ/Bit/m2
5.1 Number of Dead nodes per round
We know that the network works with 100% efficiency until it’s all the
nodes are active over the network. Figure 1 shows the total alive nodes
for proposed work, LEACH and SEP. It is clearly visible that the diagonal
division has greater number of alive nodes for more rounds than the other
two algorithms. Thus, making the proposed protocol more stable and
better. LEACH protocol has performed least among all. As we know the
maximum number of alive nodes in every round leads to maximum
efficiency. As we can see the number of alive nodes in every round is
more than other algorithms. We are minimizing the energy use by sending
the data using different CHs. By using the method of multi-hop and
diagonal division data is transmitted through various nodes that help in
reducing the energy use of each node. By using these methods energy use
is divided among various sensor nodes. The number of dead nodes in each
round is less as compared to other algorithms. As we can see in the
proposed algorithm availability of dead nodes is almost zero or equal to
zero in more than 1000 rounds which is far better than LEACH and Z-
SEP algorithm.
Fig. 1 –Dead nodes per round.
5.2 First Node Death(FND) and Half Node Death(HND)
When the node is deployed then the target is to get maximum information
with less energy used. Because of nodes being dead in every round, some
of the area are left without sensing any information by the SENSOR
NODE(P. S. Mehra et al., 2018a). Figure 2 clearly shows that the graph of
FND and HND obtained from the performance analysis of result. We
know that by an increase in the number of dead nodes in every round there
is an increase in the number of an area that is left without sensing any
information by the SENSOR NODE. By using data transmission through
different CHs to the BS we are minimizing the energy use which helps in
increasing time for first node dead and half node dead after more rounds
as compared to others. This helps in increasing the sensing range for the
maximum area for maximum time. As we see first node dead is after more
than 1000 rounds and half node dead is after more than 1600 rounds
which is far better than other compared algorithms which are dying in less
than 400 or 800 rounds for first node dead and less than 700 or 1100 for
half node dead.
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Intelligent Communication and Computational Research
PRIYANSHU GUPTA, PALLAV RAJ
6
Fig. 2 –FND and HND.
5.3 Packets to base station
Figure 3 depicts packets transmitted to BS per round. We can see that the
number of packets sent to BS station is maximum in the proposed
algorithm in comparison to other compared algorithms. Also, it is
important to note that the number of packets sent to BS is increasing
drastically and we can say it is three times larger than other compared
algorithms.
Fig. 3 – Packets to base station
5.4 Network Energy level
Figure 4 depicts the graph of network energy level per round. As we can
see from the graph that the network energy level is also better in
comparison to other algorithms as it is decreasing slowly as compared to
others. The reason is that the energy dissipation is shared among all the
sensor nodes as chance is given to highest energy nodes for CH role and
data is forwarded in hops resulting in better overall energy of the network.
Fig. 4 – Network energy level per round
6 Conclusion
In our protocol, the network is bisected virtually into two zones by
dividing the region diagonally for energy efficiency of WSN. Further,
clustering is used to divide the zones into sub-zones. We have used
distance to diagonal and energy as parameter for clustering and multi-hop
communication for within cluster communication. From obtained
experimental results, we can see that the efficiency of the network
throughput rises significantly if we divide the network in proper zones
with appropriate selection of CH on the basis of remnant energy of the
SENSOR NODEs and centrality distance as well as distance from base
station. The information shared in terms of packet delivery to base station
is more in proposed work than its comparatives.
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7
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