enhancing coverage using weight based clustering in...
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Enhancing Coverage Using Weight Based Clusteringin Wireless Sensor Networks
Amandeep Kaur Sohal1 • Ajay K. Sharma2 • Neetu Sood3
Published online: 7 November 2017� Springer Science+Business Media, LLC 2017
Abstract Energy conservation in wireless sensor networks (WSNs) is a fundamental issue.
For certain surveillance applications in WSN, coverage lifetime is an important issue and
this is related to energy consumption significantly. In order to handle these two interlinked
aspects in WSN, a new scheme named Weight based Coverage Enhancing Protocol
(WCEP) has been introduced. The WCEP aims to obtain longer full coverage and better
network life time. The WCEP is based on assigning different weight values to certain
governing parameters which are residual energy, overlapping degree, node density and
degree of sensor node. These governing parameters affect the energy and coverage aspects
predominantly. Further, these four different parameters are prime elements in cluster
formation process and node scheduling mechanisms. The weight values help in selection of
an optimal group of Cluster Heads and Cluster Members, which result in enhancement of
complete coverage lifetime. The simulation results indicate that WCEP performs better in
terms of energy consumption also. The enhancement of value 24% in full coverage lifetime
has been obtained as compared to established existing techniques.
Keywords Clustering � Energy efficient � Prolonged coverage � Wireless
sensor network
& Amandeep Kaur [email protected]
Ajay K. [email protected]
Neetu [email protected]
1 Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute ofTechnology, Jalandhar, India
2 National Institute of Technology, Delhi, India
3 Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Instituteof Technology, Jalandhar, India
123
Wireless Pers Commun (2018) 98:3505–3526https://doi.org/10.1007/s11277-017-5026-1
1 Introduction
The Wireless Sensor Network (WSN) is defined as a network of huge number of sensor
nodes that can observe the monitoring environment through wireless links. The gathered
information from environment is forwarded through intermediate nodes to the controller.
The controller is called as sink or Base Station (BS) [1]. In WSN, sensor nodes are usually
powered by batteries, which impose strict constraints on available energy for utilization
and for output.
Recently, various logical techniques such as energy harvesting, cognitive networking
methods, and tools of artificial intelligence have also been used in WSN to circumvent the
limitations imposed by conventional WSNs. The field of energy harvesting suggests some
solutions for battery operated WSNs and this concept is termed as Energy Harvesting in
WSNs (EHWSNs) [2, 3]. Energy harvesting is a process to harvest the energy from
surrounding environmental sources of networks. The various environmental energy har-
vesting sources include solar power, wind, thermal energy, mechanical vibrations, tem-
perature vibrations, magnetic fields, and etc. Energy from these sources can be captured
and converted to usable energy for WSNs. Thus, harvested energy can be used to improve
the performance of WSNs by increasing battery life. Moreover, surplus energy can be
stored using rechargeable batteries and supercapacitors. But, there are certain design
challenges to implement EHWSNs, which incorporate efficient harvesting of available
energy, storing the charge, managing of the stored energy and variety of application
specific load in WSNs. Another holistic approach i.e. cognitive networking in WSNs is
used to improve end-to-end goals of application-specific sensor networks [4]. The term
‘‘Cognition’’ refers to the process of knowing through perception, reasoning, knowledge
and intuition with a focus on information available from the environment. It can be
achieved by incorporating learning and reasoning in the upper layers, and opportunistic
spectrum access at the physical layer. However, applying cognitive techniques involve
artificial intelligence and game theoretic approach to increase knowledge in the system and
has several challenges [5, 6]. In spite of this new era of investigations in WSNs, extensive
research is still going on to overcome energy constraints in homogeneous WSNs. The
utilization of available limited energy has triggered further systematic designing of energy
efficient coverage enhancing algorithms in WSNs. The emerging circumstances/applica-
tions demand better coverage and network lifetime by WSN.
The clustering algorithms in WSNs are helpful in improving scalability, network life
time, and energy efficiency [7, 8]. In clustering, network is partitioned into group of sensor
nodes called clusters. Each cluster is managed by a particular sensor node called as Cluster
Head (CH) and other remaining sensor nodes of cluster are termed as Cluster Members
(CMs). The CMs monitor or sense the specific environment periodically and transmit the
sensed information to their respective CH. The CMs cannot send the gathered data directly
to the BS in clustering algorithms. Further, corresponding CHs aggregate the data received
from their respective CMs and forward this information to the BS. This technique promotes
decrease in numbers of messages to be communicated to BS which leads to reduction in
energy consumption. Hence, clustering achieves prolonged network lifetime [7, 8]. To
evaluate the performance of clustering protocols, various performance parameters such as
energy efficiency, network lifetime, number of CHs per round, number of nodes per round,
timeliness, QoS support, and etc. are taken into account for improving network lifetime [1].
In this paper, an energy efficient scheme has been introduced as Weight based Coverage
Enhancing Protocol (WCEP). This scheme targets the prolonged full coverage of moni-
toring field in the WSN. The WCEP also aims for less energy consumption along with full
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coverage. The WCEP algorithm partitions the monitoring field into group of sensor
nodes/clusters by two processes i.e. cluster formation process followed by another process
i.e. data transmission process. It is noted that for maintaining prolonged full coverage
lifetime a sensor node should remain alive in each part of monitoring field for longer time
[9]. The WCEP technique works on weighted sum method during cluster formation pro-
cess. The WCEP selects appropriate CMs and CHs periodically to maintain the coverage in
each round of network operation. The selected CMs and CHs ensure prolonged coverage
with proper utilization of energy. The WCEP algorithm works with weight values for
different governing parameters viz. residual energy, overlapping, node density and degree
of sensor node. The weighted sum method targets to conserve energy by assigning different
weight values to respective governing parameters. This conserved energy is utilized to
extend full coverage lifetime of the monitoring field. The comparison WCEP with the well-
established Coverage Preserving Clustering Protocol (CPCP) [10] reveals the benefits of
proposed algorithm over CPCP.
The motivation behind the use of weighted sum method in cluster formation process is to
preserve limited available energy. This has led to more coverage lifetime. The selection of
governing parameters is based on energy as well as coverage issues. The improvement in
network lifetime relies on residual energy [10–13], distance between sensor node to sink
[12–14], node density [14, 15], and etc. Therefore, present work is primarily focused on
consideration of residual energy, overlapping degree, node density, and degree of sensor node
simultaneously. This accomplishes to selection of appropriate group of active nodes andCHs.
The contents of the paper are organized as follows. Section 2 describes related work in
context with energy efficient coverage preserving algorithms for WSNs. Section 3 dis-
cusses Weight based Coverage Enhancing Protocol (WCEP) and Sect. 4 discusses simu-
lation results. Finally, Sect. 5 is devoted to conclusion.
2 Related Work
There are number of clustering algorithms related to energy efficiency and coverage
preserving techniques. These algorithms have certain merits and demerits. These algo-
rithms are discussed below as part of literature survey.
The energy efficient clustering algorithms [11, 12, 14, 16–28] are based on balancing of
energy consumption and load balancing of sensor nodes to achieve longer network life-
time. The clustering process in these algorithms may be uniform/non-uniform, equal/
unequal, and etc. These clustering processes use parameters viz. distance, residual energy,
node density, and etc. in probabilistic manner/weighted sum method manner to achieve
improved network life time. But, network coverage issue which is a key element has not
been dealt significantly in these energy efficient clustering algorithms [11, 12, 14, 16–28].
The energy efficient coverage algorithms in [9, 29–42] discuss energy as well as cov-
erage issues simultaneously. The various coverage problems such as type of sensor cov-
erage (point, area, and target coverage), sensor development mechanisms (deterministic/
statistical) and WSN properties (network connectivity, energy efficiency, and fault toler-
ance for connectivity and sensing etc.) are well explained in detail in [9, 32]. Generally,
energy efficient coverage algorithms are based on energy conservation issues [30, 31, 33],
node scheduling technique [10, 29], load balancing [34, 38–41], type of coverage area and
target [36, 37, 42] along with clustering method and/or routing method [35]. These
algorithms target the minimization of energy consumption for achieving better network
Enhancing Coverage Using Weight Based Clustering in… 3507
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lifetime and with coverage accordingly. In this context, certain energy and coverage related
recently proposed protocols are discussed in following paragraphs.
The protocol based on CH selection techniques for coverage preservation was proposed
as CPCP in [10]. The CPCP considers various coverage-aware cost metrics. The CPCP
prefers densely deployed sensor nodes as CHs, active nodes and routing nodes respec-
tively. Although, CPCP solves the problem of network coverage and balance the energy
efficiently but suffers from large computational burden on sensor nodes. The CPCP does
not able to be dispensable for covered area in each round.
The Coverage-Aware Clustering Protocol (CACP) for randomly deployed networks was
presented to simplify the selection of CHs and active nodes in [33]. But in CACP, each CH
consumes more energy, because it has to transmit the aggregated data to the sink directly.
This process leads to the quick death of CHs and more consumption of energy. Another
clustering technique named Flow Balanced Routing (FBR) protocol was introduced in
[35]. The FBR works on multi-hop clustering technique is to obtain both energy efficiency
and coverage preservation. The FBR is able to achieve longer network lifetime with
coverage but computational burden of sensors remain same.
The Distributed Clustering based algorithms for energy and coverage awareness were
investigated in [34, 38, 39]. The Distributed Energy Efficient Clustering Algorithm with
Improved Coverage (DEECIC) was proposed using average clustering scheme in [34]. In
DEECIC, the CH is selected from a dense area on the basis of residual energy and degree
of the node. The DEECIC improves coverage lifetime but algorithm does not discuss
sensor activation properly.
An Energy and Coverage-aware Distributed Clustering protocol (ECDC) was proposed
to improve the coverage lifetime of WSN in [38]. The ECDC considers neighbor’s
information and average remaining energy of neighbor to select the CH. The tree based
approach is used for inter cluster data transmission and communication to conserve energy
consumption. Another, distributed scheme called as Coverage aware Unequal Clustering
Algorithm (CUCA) was introduced in [39]. The CUCA aims to achieve uniform load
distribution to avoid the hot spot problem in the network. The aim of ECDC and CUCA is
to achieve energy efficient coverage preservation. The ECDC and CUCA require
retransmitting of control packets for routing purposes. This retransmission results in
wastage of some energy in each stage which is not desirable.
Another protocol i.e. Distributed, Energy and Coverage Aware Routing algorithm
(DECAR) is proposed to achieve improved coverage lifetime in [40]. The DECAR pro-
vides solution to hot spot problem during data transmission. The protocols such as CACP,
ECDC, CUCA and DECAR are based on traditional well known energy efficient LEACH
protocol [11]. In spite of certain merits, these algorithms are unable to overcome the basic
drawback of LEACH i.e. network scalability.
To summarize above discussion a detailed view is given below in the form of Table 1.
It can be discern from Table 1 that algorithms viz. CPCP, CACP, DEECIC, FBR, ECDC,
CUCA andDECAR suffer from certain common drawbacks such as less prolonged coverage,
scalability, versatility and complexity issues.Whereas CPCP handles these issues effectively
up to certain extent but it suffers from computational burden on sensor nodes. So, considering
these issues a new algorithm i.e.WCEP has been proposed. TheWCEP is an energy efficient
algorithm. The WCEP minimizes computation burden of sensor nodes and achieves pro-
longed full coverage, more versatility, and improved scalability.
Although CPCP and WCEP both involve cluster formation process, but there is sig-
nificant strategically difference between them. The CPCP works on coverage aware cost
metrics viz. energy-aware (Cea), minimum-weight (Cmw), weighted sum (Cws), and
3508 A. K. Sohal et al.
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Table
1Comparativeanalysisofenergyefficientalgorithmswithproposed(W
CEP)algorithm
Protocol
Network
size
(m2)/
no.of
nodes
Position
ofBS
Sensing
model
Approach
Location
aware
Deployment
strategy
Typeofnode
Energy
efficient
Coverage
type
Drawbacks
LEACH
[11]
1009
100/
100
Outside
Disc
Centralized
Yes
Radom
Homogeneous
Yes
No
Transm
itsaggregatedatato
sink
directly.Only
formicro
sensor
networks
CPCP[10]
2009
200/
400
Centre
Disc
Distributed
Yes
Random/
non-
uniform
Homogeneous
Yes
Area
Computationburden
onsensor
nodes
CACP[33]
1209
120/
1000
Outside
Hexagonal
Distributed
Yes
Randomly
and
uniform
Homogeneous
Yes
Area
Quickdeath
ofCHs,dueto
transm
issionofaggregatedata
tosinkdirectly.only
suitable
forsm
allnetworks
DEECIC
[34]
1009
100/
100
Outside
Disc
Distributed
No
Random
Homogeneous
Yes
Area
(Lim
ited)
Energyefficientclustering
algorithm
than
coverage
efficientalgorithm
FBR[35]
1509
150/
1000
Outside
Disc
Centralized
Yes
Random
Homogeneous
Yes
Area
Communicationoverhead
increases,so
burden
onsensors
remainsame
ECDC
[38]
2009
200/
100–200
Outside
Disc
Distributed
Yes
Random
and
non-
uniform
Heterogeneous
(energy)
Yes
Pointand
area
Needsretransm
issionofcontrol
packetsforroutingpurposes
andconsumes
more
energy
CUCA
[39]
409
40/
60–100
Outside
Disc
Distributed
Yes
Notdiscussed
Homogeneous
Yes
Area
Only
suitable
forsm
allnetworks
DECAR
[40]
409
40/
100
Inside
Disc
Distributed
Yes
Random
and
Grid
Homogeneous
Yes
Area
Transm
itsaggregatedatato
sink
directly
Notfeasible
ingeographically
largenetwork
WCEP
(proposed
algorithm)
2009
200/
400(and
above)
Centre/
outside
Disc
Distributed
Yes
Random
Homogeneous
Yes
Prolonged
fullarea
coverage
Noburden
onsensornodes,
Prolonged
Network
Lifetim
ewithcomplete
coverage
Enhancing Coverage Using Weight Based Clustering in… 3509
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coverage redundancy (Ccc) respectively. These coverage aware cost metrics are depended
on sensor node’s remaining energy and its neighbors’ remaining energies. During cluster
formation of CPCP, CHs and active sensor nodes are selected on the basis of these cost
metrics values. As a result, CPCP does not aim to minimize redundant covered area in each
round. In WCEP, CMs and CHs are selected on the basis of weighted sum method. It
decides weight values for different governing parameters viz. residual energy, overlapping,
node density and degree of sensor node which are energy as well as coverage aware
parameters. The CPCP takes into consideration the remaining energy of each sensor node
while cluster formation. In contrast to CPCP, WCEP considers the governing parameters
which are related to coverage as well as to network lifetime. The appropriate selection of
CHs and active sensor nodes in case of WCEP is main design feature of cluster formation
process, which contributes to WCEP effectiveness over CPCP.
3 Weight Based Coverage Enhancing Protocol (WCEP)
The WCEP is based on certain elements, which are discussed below.
3.1 Network Model
The WCEP considers a set of ‘N’ number of sensor nodes, denoted as S = {SN1, SN2, …,
SNN} where SNi is the ith sensor node (i = 1, 2, 3,…, N). These sensor nodes are deployed
randomly in 200 9 200 m2 monitoring field. The following assumptions are considered
during design and implementation of WCEP algorithm.
1. All the sensor nodes and sink are static after deployment.
2. The sink is situated at center of monitored field. The location of sink is fixed and
assumed to be known to other sensor node.
3. The network is homogeneous and all the sensor nodes are equivalent i.e. they have
the same computing and communication capacity respectively.
4. Each sensor node is assigned with unique identification number.
5. Each sensor node has a confined sensing range.
6. The sensing range of a sensor node is modeled as disk sensing model in 2-D space.
The sensor node is assumed to be at the center of disk [32].
7. The transmission range of a sensor node is modeled as disk sensing model in 2-D
space. The data sent from each sensor node can be obtained by all sensor nodes
which lie within the vicinity of its transmission range [32].
8. The transmission range is considered at least twice of the sensing range i.e. TR � 2 �RS where TR and RS indicates transmission range and sensing range respectively
[40].
9. The wireless link between sensor nodes is symmetric and bidirectional.
10. The sensor nodes are well aware of their coordinates.
3.2 Energy Model
The WCEP uses simple radio model for the measurement of energy dissipation
[11, 33–35, 38–40]. The radio model consists of transmitter, power amplifier and receiver.
The transmitter consumes energy to perform transmitter circuitry operations. The power
3510 A. K. Sohal et al.
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amplifier dissipates energy during data transmission to receiver. The receiver consumes
energy to perform the receiver circuitry operations while receiving data. There are two
propagation models i.e. free space model and multi path propagation model. Both the free
space propagation model (d2 power loss) and multi path propagation model (d4 power loss)
can be used, depending on the distance between transmitter and receiver [11]. When the
distance is less than a threshold distance, the free space (fs) model is used. Otherwise, the
multipath (mp) model is used. The energy required for transmission of one-bit message at a
distance ‘d’ is as follows [11]:
ETx l; dð Þ ¼ lEelec þ l 2 da
lEelec þ l 2fs d2 d\d0
lEelec þ l 2fmp d4 d� d0
ð3:1Þ
where a = 2 or 4.
The threshold distance (d0) can be obtained from:
do ¼ffiffiffiffiffiffiffiffiffi2fs
2fmp
r
ð3:2Þ
And energy required for receiving one-bit message is given by following formula [11]
ERx lð Þ ¼ lEelec ð3:3Þ
The electronics energy Eelec depends on digital coding, modulation, filtering, and
spreading of the signal. The amplifier energies (2fs d2; and 2fmp d
4) rely on the distance to
the receiver.
3.3 Network Operation
The WCEP algorithm consists of two processes. The first process is the cluster formation
process, where active nodes and CHs are selected to represent respective clusters. The
second process is the data transmission process. In this process, data is transferred from
CMs to their respective CH (intra-cluster communication) and further from different
designated CHs to sink (inter cluster communication) in each communication round. The
WCEP assigns different weight values to governing parameters according to particular
application requirement. Those applications could be prolong complete coverage (100
percent coverage), etc. Using weighted sum method, WCEP selects active nodes, CHs and
then performs data transmission operation.
At the beginning of WCEP algorithm, deployed ‘N’ numbers of sensor nodes collect
information about its residual energy. The WCEP takes into record the number of times a
particular point is being covered by sensor nodes and their respective co-ordinates. The
sensor nodes are well aware of their coordinates, distance between themselves and distance
to sink. These distance calculations are performed on the basis of Euclidian formula.
Since sensor nodes are stationary, information about location, node density, and over-
lapping degree are to be computed only once at the beginning of network operation. All the
deployed sensor nodes update their information on the basis of remaining energies at the
start of each communication round. Then, each sensor node broadcasts an updated packet
of information in its cluster radius i.e. sensing range. After receiving the message, each
node calculates its Net weight (Nw). The Nw is calculated on the basis of residual energy of
Enhancing Coverage Using Weight Based Clustering in… 3511
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node, overlapping degree, node density and degree of sensor node. The steps involved
during selection of active nodes and CHs in one communication round of WCEP are as
follows:
1. Deployment of set of N sensor nodes with complete coverage of monitoring area.
2. Computation of all the distances between each node.
3. Computation of overlapping degree Olap, node density q, and degree of sensor
node Deg respectively.
4. Calculation of residual energy Eres of each sensor node, if Eres[Ethres only then
that sensor node can participate in next communication round.
5. Calculation of Net weight of each sensor node with equation based on weighted
sum method i.e. Nw ¼ Eres � w1 þ Olap � w2 þ Deg � w3 þ q � w4, for every covered
point. Here, values of weights may be varied from 0 to 1. The values of w1, w2, w3
and w4 are 0.6, 0.2, 0.1 and 0.1 for particular application.
6. Selection of sensor node with maximum value of ‘Nw’ as an active node for
covered point.
7. Broadcasting of an updated packet by each selected active node within its sensing
range.
8. Calculation of ‘CH_wp’ based on calculated ‘Nw’ of sensor node and its distance
from sink.
9. Selection of sensor node with Minimum value of ‘CH_wp’ as a CH candidate for
selected active nodes.
10. Broadcasting of an updated packet by each selected CH within its cluster radius.
The flow chart of WCEP is shown in Fig. 1. The Algorithm 1 used in WCEP for cluster
formation is presented below.
In WCEP, following terms are used which are defined as follows:
(a) Neighboring nodes Using disk sensing model, all sensor nodes present within SNi’s
cluster radius is called SNi’s neighboring nodes. For example, in Fig. 2, SN2, SN3
and SN4 are neighboring nodes of SN1.
(b) Covered point A point ‘pt’ is covered by a sensor node SNi if the Euclidean distance
between the point and the sensor node is less than or equal to the sensing range i.e.
d(SNi, pt) B Rs.
(c) Full sensing coverage In surveillance region, the monitoring field is said to have full
sensing coverage when each point in this region is covered by at least one sensor
node and there is no sensing hole in the field.
The various governing parameters which help to decide how well a sensor node is
suitable for becoming active node/CH are viz. residual energy, overlapped degree, degree
of sensor node, and node density. The first performance parameter for weight value is
residual energy i.e. ‘Eres’. The residual energy is termed as the remaining energy of the
sensor node at the end of each simulation round. The eligibility of sensor node to become
active node is its higher value of residual energy. The second performance parameter for
weight value is overlapping degree i.e. ‘Olap’ as shown in Fig. 2. It is defined as number
of sensor nodes which are responsible for generating the overlapping area. The eligibility
of sensor node depends on its higher value of overlapping degree.
The third performance parameter for weight value is degree of sensor node i.e. ‘Deg’.
The number of covered points in SNi’s sensing range is called degree of sensor node. The
sensor node which covers more number of covered points has higher possibility to become
active nodes. For example, in Fig. 3, Deg(SN1), Deg(SN2), Deg(SN3) and Deg(SN4) are 27,
3512 A. K. Sohal et al.
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26, 29 and 23 respectively. The fourth performance parameter for weight value is nodedensity i.e. ‘q’. The node density is defined as number of sensor nodes within its cluster
range. For example, in Fig. 3, q(SN1) is 1. Similarly, q(SN2), q(SN3) and q(SN4) are 2, 0
and 1 respectively.
In our implementation monitoring field is divided into a grid of equidistant points. It is
further ensured that each sensor covers equidistant points within its sensing range. The
computation of Net weight (Nw) for each sensor node is done for entire monitoring area. If
Fig. 1 Flow chart of WCEP procedure
Overlapped area of SN1, SN2 and SN3
Fig. 2 Overlapping area of sensor nodes with its neighbors
Enhancing Coverage Using Weight Based Clustering in… 3513
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two or more sensor nodes are covering same area then sensor node having higher residual
energy gets an opportunity to become active node in the communication round. The sensor
node announces an activation message to tell about its participation in communication
round. The set of selected active nodes is capable of covering each part of monitoring area
efficiently. The selection of these active nodes is based on optimal usage of energy,
overlapping, sensing area, and node density with full coverage of monitored area. Rest all
nodes remains inactive and termed as sleeping nodes.
For CH selection, CH weight parameter (CH_wp) is considered. The ‘CH_wp’ includes
‘Nw’ of sensor node and its cumulative distance from sink. The cumulative distance is the
minimum distance path from SNi to sink which is denoted as d(i, j). The cumulative
distance is computed on the basis of shortest path algorithm. All the sensor nodes equally
participate in the CH selection. It should be noted here that before entering into CH
selection procedure, each sensor node must have residual energy greater than threshold
energy i.e. Eres(SNi)[Ethres. The minimum value of ‘CH_wp’ of sensor nodes for entire
monitoring area will decide the eligibility of set of CHs in the communication round. The
selected CHs take into account efficient energy consumption along with full coverage of
the monitoring field. The selected CHs make an advertisement message to all the sensor
nodes within its cluster range. There is no constraint on the number of active nodes in
single cluster. Thus, any number of sensor nodes can join a CH which is in the vicinity of
its cluster radius.
In second process of data transmission, intra and inter cluster communication take place.
The active nodes communicate their sensed information to their respective CH directly
using single hop method during intra cluster communication. Whereas, in inter cluster
communication, CHs opt multi hop method while transmitting information from CHs to
sink. The shortest path method is used to select communication route for this. Hence,
WCEP selects appropriate CHs and active nodes to ensure full coverage for prolonged
period of time.
4 Simulation Results
The MATLAB version 2014 program is used to implement and simulate the performance
of WCEP. The comparison between proposed algorithm (WCEP) and coverage-preserving
clustering protocol (CPCP) [10] has been done. In simulations, network model with a
square area of size 200 9 200 m2 is taken. The sensor nodes are deployed randomly and
Fig. 3 Depiction of degree ofsensor node
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non-uniformly in the monitoring field. The random deployment implies random dispersion
of sensor nodes in each grid zone. The non-uniform deployment implies non-uniform
dispersion of sensor nodes in the monitoring field irrespective to grid zone. The Fig. 4a, b
indicates 100% coverage of monitoring field for different deployment scenarios. The sink
is situated at centre of the monitoring field in both scenarios. The time in the experiments is
proceeded in seconds. The parameter values used in WCEP are shown in Table 2.
The Fig. 5a, b shows voronoi diagram of different deployment scenarios employed in
WCEP for 100% coverage of monitoring field.
During simulations, performance metrics viz. network lifetime, coverage lifetime, CHs
residual energy, number of CHs, average number of active nodes per cluster, First Node
Dead (FND) time, Half Node Dead (HND) time, Last Node Dead (LND) time are con-
sidered for performance investigations of algorithm. These performance metrics contribute
to coverage life time and energy aspects. The coverage lifetime is the ratio of the coverage
of the current alive sensors to the coverage of the whole sensors falls below a predeter-
mined threshold [10]. The network lifetime is termed as the duration from the beginning
time of the network operation to the instant when a defined percentage of the sensors die.
On the other hand, CPCP works on coverage aware cost metrics viz. energy-aware
(Cea), minimum-weight (Cmw), weighted sum (Cws), and coverage redundancy (Ccc)
respectively. Out of these cost metrics, CPCP (Cmw) provides best result for 100% cov-
erage of the monitoring field.
Following paragraphs are devoted to comparative results and analysis of WCEP and
CPCP.
4.1 Coverage Life Time with Time
The Fig. 6a, b shows the results of coverage lifetime with time. The comparison has been
made between WCEP and CPCP cost metrics for different deployment scenarios. During
cluster formation process, WCEP selects efficient active nodes and CHs. Thus, WCEP
provides 24 and 27% longer full coverage with improved network lifetime as compared to
CPCP (Cmw) for both scenarios (a and b) respectively. The better performance of WCEP
is due to proper selection of governing parameters and then followed by selection of CHs
and active nodes.
(a) (b) -100 -80 -60 -40 -20 0 20 40 60 80 100
-100
-80
-60
-40
-20
0
20
40
60
80
100
-100 -80 -60 -40 -20 0 20 40 60 80 100-100
-80
-60
-40
-20
0
20
40
60
80
100
Fig. 4 Different deployment scenarios in WCEP for 100% coverage of monitoring field. a Randomdeployment, b non-uniform deployment
Enhancing Coverage Using Weight Based Clustering in… 3515
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The Fig. 7a, b compares WCEP with CPCP for specified coverage lifetime (100, 95, 90
and 85%) with time for different deployment scenarios. In random and non-uniform
deployment scenarios, WCEP maintains complete coverage for a longer period of time. For
certain full coverage based applications such as surveillance, WCEP is much preferable.
4.2 Residual Energy of CHs with Time
The Fig. 8a, b shows the behavior of residual energy of CHs in joules (J) with network life
time in seconds (s). The CHs residual energy is defined as residual energy of CHs at
particular instant of the network operation. It has been observed that overall CHs residual
energy of WCEP is more as compare to CPCP cost metrics. The CHs in WCEP consume
16% less energy in comparison to CHs of CPCP (Cmw) for 100% complete coverage of
monitoring field in random deployment scenario whereas 18% less energy consumption
has been observed in non-uniform deployment scenario.
-100 -80 -60 -40 -20 0 20 40 60 80 100-100
-80
-60
-40
-20
0
20
40
60
80
100
(a) (b)
Fig. 5 Voronoi diagram for different deployment scenarios in WCEP for 100% coverage of monitoringfield. a Random deployment, b non-uniform deployment
Table 2 Parameters used insimulation
Parameter Value
Number of nodes 400
Monitoring field 200 9 200 m2
Base Station location (100, 100)
Tx/Rx electronics constant [11] 50 nJ/bit
Amplifier constant [11] 10 pJ/bit/m2
CH energy threshold [11] 10-4 J
Packet size [11] 30 bytes
Packet rate [11] 1 packet/s
Sensing range [11] 15 m
Cluster radius [11] 30 m
Initial energy (Eint) 1 J
Transmission range 40 m
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4.3 Number of CHs with Time
The Fig. 9a, b shows the number of selected CHs in the network operation. The number of
CHs in WCEP at particular instant in network operation is 20% less as compared to CPCP
(Cmw) for random deployment scenario whereas for non-uniform deployment scenario it is
14% less number of CHs in comparison to CPCP (Cmw).
4.4 Number of Active Nodes per Cluster with Time
The Fig. 10a, b shows behavior of average number of active nodes per cluster in the
network operation. Initially, in WCEP number of active nodes are little bit more i.e. 5 and
8% as compared to CPCP (Cmw) to maintain complete coverage of monitoring field for
both scenarios respectively. This higher number of active nodes at initial phase is required
to maintain longer full coverage in monitoring area. Later, these active nodes decrease with
time accordingly. Moreover, active nodes consume less energy as compared CHs.
Fig. 6 Effect of time with coverage for different deployment scenarios. a Random deployment, b non-uniform deployment
100 95 90 850
500
1000
1500
2000
2500
3000
3500
4000
Coverage[%]
Tim
e[s]
CeaCwsCmwCccWCEP
100 95 90 850
500
1000
1500
2000
2500
3000
3500
4000
Coverage[%]
Tim
e[s]
CeaCwsCmwCccWCEP
(a) (b)
Fig. 7 Time versus coverage for different deployment scenarios. a Random deployment, b non-uniformdeployment
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4.5 FND, HND and LND
The other performance metrics i.e. network lifetime in terms of First Node Dead (FND)
time, Half Node Dead (HND) time, Last Node Dead (LND) time and the residual energy
are used to evaluate the performance of clustering algorithm in [43]. The dependence of
FND, HND, LND and residual energy for different deployment scenarios are shown in
Fig. 11a, b and Table 3 for WCEP and CPCP respectively.
The network lifetime at FND for WCEP is 2% better than CPCP (Cmw) for both
scenarios. In random deployment, network lifetime at HND and LND for WCEP is 45 and
70% better than CPCP (Cmw). In case of non-uniform deployment, network lifetime at
HND and LND for WCEP is 20 and 75% better than CPCP (Cmw).
Table 3 shows the comparison of network residual energy at FND, HND and LND for
CPCP cost metrics with WCEP. Here, WCEP has 2% more network residual energy at
FND than CPCP (Cmw) for both scenarios. At HND, WCEP consumes 45 and 40% less
energy for random and non-uniform deployment scenario in comparison to CPCP (Cmw).
Fig. 8 Residual energy of CHs with time for different deployment scenario
Fig. 9 Number of CHs with time for different deployment scenario
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4.6 Comparison of Other Performance Metrics
The other performance metrics such as increase in number of sensor nodes, variation in
initial energy of sensor node and variation in number of nodes per unit area have also been
evaluated for comparison purpose. The details are mentioned below.
4.6.1 Increase in Number of Sensor Nodes
The simulations are performed for 200,300,400,500 and 600 sensor nodes dispersed ran-
domly in the monitoring area i.e. 200 9 200 m2. The Fig. 12a shows simulation results for
network lifetime at full coverage with increasing number of nodes. For instance, at 400
nodes WCEP maintains 100% coverage for 2740 s in comparison to CPCP (Cmw) with
2466 s. As the number of sensor nodes increase, WCEP provides longer complete network
coverage as compared to CPCP cost metrics.
The Fig. 12b shows the results of network residual energy in joules with the time at full
coverage. These results show WCEP outperforms in terms of energy efficiency and pro-
longed 100% coverage as compare to CPCP cost metrics.
Fig. 10 Number of active nodes/cluster with time for different deployment scenario
FND HND LND0
2000
4000
6000
8000
10000
12000
No. of Dead Nodes
Tim
e[s]
CeaCwsCmwCccWCEP
FND HND LND0
2000
4000
6000
8000
10000
12000
No. of Dead Nodes
Tim
e[s]
CeaCwsCmwCccWCEP
(a) (b)
Fig. 11 Different time instances at First Node Dead (FND), Half Node Dead (HND), Last Node Dead(LND) for different deployment scenarios
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4.6.2 Variation in Initial Energy of Sensor Node
The simulations are performed with variations in the initial energy of each sensor node i.e.
0.2, 0.4, 0.6, 0.8 and 1 J. The parameters used in the simulations are taken from Table 2.
The sensor nodes are deployed randomly in the monitoring field. The Table 4 shows
comparison of network lifetime for CPCP (Cmw) and WCEP with respect to FND, HND
and LND for different amount of initial energy. The results indicate better network lifetime
of WCEP in comparison to CPCP (Cmw) at FND, HND and LND.
Figure 13a shows behavior of coverage lifetime of CPCP (Cmw) and WCEP with time
and with variation of initial energy for each sensor node. Figure 13b shows the amount of
total network residual energy of CPCP (Cmw) and WCEP based on variation of initial
energy for each sensor node. Figure 13c indicates comparative decay of dead nodes of
CPCP (Cmw) and WCEP with time for different amount of initial energy of each sensor
node. The simulation results show better overall performance of WCEP as compared to
CPCP (Cmw).
4.6.3 Variation in Number of Nodes per Unit Area
The performance of WCEP with the change in number of nodes and size of monitoring
field has been assessed. Table 5 shows the various values of number of nodes per unit area.
Fig. 12 Different time (s) instances and residual energy (J) of network for different network sizes at 100%coverage
Table 3 Network Residual Energy (J) at FND, HND and LND for different deployment scenarios
Network residual energy Random deployment Non-uniform deployment
Cea Cws Cmw Ccc WCEP Cea Cws Cmw Ccc WCEP
FND 380 376 377 377 383 378 375 374 373 380
HND 44 70 75 115 110 46 86 85 127 119
LND 0 0 0 0 0 0 0 0 0 0
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The Fig. 14a–d illustrates the behavior of network lifetime, CHs residual energy,
number of CHs and number of active nodes for 100% coverage of monitoring field with
change in number of nodes per unit area. The WCEP outperforms CPCP (Cmw). The
Table 4 Comparison of Network Lifetime with different amount of initial energy
Initial energy (J/node) Protocol Network lifetimeat FND
Network lifetimeat HND
Network lifetimeat LND
1 CPCP (Cmw) 227 3310 5652
WCEP 255 3849 10,456
0.8 CPCP (Cmw) 197 2643 4922
WCEP 211 2953 9206
0.6 CPCP (Cmw) 132 1976 3657
WCEP 179 2419 7342
0.4 CPCP (Cmw) 62 1296 2442
WCEP 112 1532 4319
0.2 CPCP (Cmw) 38 678 1202
WCEP 52 740 1962
Fig. 13 Comparison of a coverage lifetime, b network residual energy and c number of dead nodes atdifferent values of sensor node’s initial energy
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WCEP considers efficient utilization of network energy to maintain the 100% coverage of
the monitoring field. Thus, appropriate number of active nodes and CHs contribute to
enhancement of the coverage. The Fig. 14b, c indicates that WCEP involves less number
of CHs as compare to CPCP (Cmw) for achieving 100% coverage of the monitoring field.
In Fig. 14d, WCEP employs little number of active nodes as compare to CPCP (Cmw) at
100% coverage of the monitoring field.
Table 5 Data used in simulation
Dimensions ofmonitoring field
200 9 200 m2 100 9 100 m2
No. of nodes 100 200 300 400 500 600 200 300 400 500 600
No. of nodes/area 0.0025 0.005 0.0075 0.01 0.0125 0.015 0.02 0.03 0.04 0.05 0.06
Fig. 14 Comparison of a network time, b CHs residual energy, c number of CHs and d number of activenodes at 100% coverage with variation in number of nodes/area
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5 Conclusion
The proposed scheme aims the prolonged full coverage issue along with energy efficient
prolonged complete coverage. The residual energy and location awareness of sensor nodes
are not only sufficient to balance the energy consumption and to prolong 100% coverage
lifetime in the network, but other coverage and energy governing parameters viz. over-
lapping degree, node density and degree of sensor node are to be included simultaneously.
The proposed scheme called as Weight based Coverage Enhancing Protocol i.e. WCEP
assigned weight values to governing parameters. During cluster formation, appropriate
number of Cluster Heads and active nodes are selected using weighted sum method in each
communication round. These selected CHs and active nodes are responsible for
enhancement of 100% coverage with minimum energy consumption. The simulation
results show that WCEP enhances coverage lifetime with balanced energy consumption.
Further, to evaluate the performance of WCEP, simulations are also performed for dif-
ferent parameters i.e. increase in number of sensor nodes, variation of initial energy of each
sensor node and variation of number of sensor nodes per unit area. The simulation results
favor the WCEP for 100% coverage of the monitoring field.
Acknowledgements The authors would like to thank Dr. W. B. Heinzelman of Rochester University, NewYork, USA for helping in problem formulation. A.K. Sohal would like to thank Ministry of HumanResource Development (MHRD), India for providing funding.
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Amandeep K. Sohal received a Bachelor degree in Electrical Engi-neering from Punjab Technical University, Jalandhar (Punjab) India in2001 and a Masters degree in Computer Science and Engineering fromDepartment of Computer Science and Engineering, Punjabi UniversityPatiala, India, in 2004. She was with Guru Nanak Dev EngineeringCollege, Ludhiana (Punjab) India as Assistant Professor, in CSEdepartment since 2004-2012. She is currently research scholar inDepartment of CSE at Dr. B.R. Ambedkar National Institute ofTechnology Jalandhar (Punjab), India. Her research area is datacommunications, networks and wireless sensor networks.
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Dr. Ajay K. Sharma is working as Director National Institute ofTechnology, Delhi since October 2013 and Past Professor in Dr. B.R.Ambedkar National Institute of Technology Jalandhar. He receivedBachelor Degree in Electronics and Electrical Communication Engi-neering from Punjab University Chandigarh, India in 1986, M.S. inElectronics and Control from Birla Institute of Technology (BITS),Pilani in the year 1994 and Ph.D. in Electronics Communication andComputer Engineering in the year 1999. His major areas of interest arebroadband optical wireless communication systems and networks,dispersion compensation, fiber nonlinearities, optical soliton trans-mission, WDM systems and networks, Radio-over-Fiber (RoF) andwireless sensor networks and computer communication. He is tech-nical reviewer of reputed international journals like: Optical Engi-neering, Optics letters, Optics Communication, Digital SignalProcessing. He has been appointed as member of technical Committeeon Telecom under International Association of Science and Technol-
ogy Development (IASTD) Canada for the term 2004–2007 and he is Life Member of Optical Society ofAmerica, USA, Computer Society of India, Mumbai, India, Advanced Computing and CommunicationsSociety, Indian Institute of Science, Bangalore, India, SPIE, USA.
Dr. Neetu Sood works as Assistant professor in the Department ofElectronics and Communication Engineering at Dr. B.R. AmbedkarNational Institute of Technology, Jalandhar in India since 2007. Herresearch interest includes wireless communication system design;OFDM based systems, channel modelling and its simulations,biomedical signal processing and plant consciousness.
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