Efficient energy utilization in wireless sensor networks: an algorithm
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue.12
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EFFICIENT ENERGY UTILIZATION IN WIRELESS SENSOR NETWORKS: AN
ALGORITHM
M. N. Khan
Department of Electrical Engineering, University of Lahore, Lahore, (Pakistan)
*E-mail: muhammad.nasir@ee.uol.edu.pk
S. O. Gilani
SMME, National University of Sciences and Technology, Islamabad, (Pakistan)
M. Jamil
SMME, National University of Sciences and Technology, Islamabad, (Pakistan)
A. Shahzad
Department of Mechanical Engineering, University of Lahore, Lahore, (Pakistan)
A. Raza
Department of Electrical Engineering, University of Lahore, Lahore, (Pakistan)
Efficient energy utilization in wireless sensor networks: an algorithm
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue.12
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ABSTRACT
In the era of latest technologies, wireless sensor network (WSN) is becoming more
popular in many applications: e-health, e-commerce, banking, farming and many
others. However, WSNs have limitations in the sense of processing power, life time
and data gathering. Among the above said issues, energy efficiency is the main
hindrance of WSN deployment. Different data gathering schemes such as low
energy adaptive clustering hierarchy (LEACH) and power efficient gathering in
sensor information systems (PEGASIS) have been proposed. However, LEACH
and PEGASIS do not provide optimal results for the energy consumption problem
and are not feasible to implement. In this research paper, energy efficiency in
terms of data gathering in WSN is presented. In this research work, a combined
flavor of particle swarm optimization (PSO) with simulated annealing (SA) is given.
A novel algorithm is proposed in which best chain formation procedure is
adopted. Using the proposed algorithm, a balance energy utilization occurred
between nodes, which results in increasing the network performance. Simulation
results are obtained and compared with other schemes, which shows better
performance as compared to LEACH and PEGASIS.
KEYWORDS
Wireless sensor network (WSN), low energy adaptive clustering hierarchy
(LEACH), power efficient gathering in sensor information systems (PEGASIS),
base station (BS), sensor nodes (SN).
1. INTRODUCTION
In the recent era, wireless sensors have become most prominent subsystem in
many applications: e-Health, e-commerce, e-banking, farming, habitat
monitoring, forest fire detection [1,2]. Wireless sensor networks (WSNs) are
composed of one to many sensor nodes in a sensing area. Sensor nodes (SNs) are
deployed in large area comprising of a base station (BS), which is located at
variable distances. The SNs locating at larger distance from the BS, consume more
energy while communicating or transferring information to the BS. The nodes
locating at near distance dissipate less energy while others dissipate more energy.
The energy dissipation depends on distance and communication time during data
gathering [3,4]. Thus energy efficiency in data gathering is a major task to count
in WSN design.
In a WSN small SNs are known to be the basic components on which processing
is done. SNs are smaller in size, require low power, less memory and least
expensive and can communicate over short distance [1,5]. Figure 1 is an example
of a WSN, where a large number of SNs (spread in the sensing area) combine to
make a fully operational sensor network. The SNs collect data and then forward
it to the BS through a leader node (i.e., cluster head (CH)). The crucial task of SN
is to listen an event and respond quickly by sending information to the sink node
or BS [5].
Efficient energy utilization in wireless sensor networks: an algorithm
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Figure 1. Wireless Sensor Network Typical Structure Model.
SNs in a WSN have very limited energy, it is hard to recharge remotely or change
their batteries more often. To cope with this issue, we need to have an energy-
efficient data gathering protocol for balancing the energy dissipation among SNs
of the network. For WSN to be efficient and acceptable, following requirements
are very crucial [6-10];
The network lifetime enhancement.
Energy efficient deployment schemes.
Need for an energy-efficient data gathering protocol.
Recently, various data gathering schemes including the low energy adaptive
clustering hierarchy (LEACH) [11] and power efficient gathering in sensor
information systems (PEGASIS) [12] have been suggested. The LEACH protocol
provides solutions for energy utilization problem. In LEACH, SNs with higher
energy are chosen as a CH randomly and CH communicates to the BS. The CH
forwards data to the BS also causes energy dissipation which is not required. The
un-necessary dissipation limits the applicability of LEACH for WSNs. On the other
hand, PEGASIS protocol is proposed for further improvement in LEACH
protocol. However, in PEGASIS, greedy chain formation allows all nodes to
become the leader at the same time, which needs more rounds in forwarding data
to the BS. This way of energy utilization is a big hurdle in PEGASIS
implementation.
Both algorithms do not provide optimal results in terms of energy efficiency
problem. We present particle swarm optimization (PSO) with simulated annealing
(SA) for efficient chain formation and ensures minimum energy consumption. For
the selection of efficient leader that will communicate to the sink, data gathering
chain for done with SA-PSO. In a chain a leader is selected based on its residual
energy 𝐸
𝑟𝑒𝑠𝑖𝑑
and each node can transmit information to its neighboring node, this
result in increased network performance as balance energy utilization occurred
between nodes. Following are the major contribution of the proposed research
work;
A new scheme for efficient energy is developed and tested as compared to other
protocols using extensive simulations. It is shown from simulation results that the
proposed protocol can prolong the network lifetime.
The SA-PSO maximizes the performance in terms of energy efficiency and also
increases the network lifetime.
In our scheme all the nodes contribute in communication to the BS and thus it
eliminates unequal energy consumption of individual nodes.
The simulation results also show significant improvements as compared to
LEACH and PEGASIS. Our further goal is to compare the SA-PSO performances
with ant colony optimization (ACO) technique.
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2. PRE-LIMINARIES
Different data gathering schemes and heuristic algorithms have been suggested in
[1-4]. Following are the main ones;
Conventional PSO and its variants.
LEACH and its variants
PEGASIS
Before discussing about the LEACH and PEGASIS, brief description of PSO
and its variant are considered to be compulsory, because the proposed
algorithm is the combination of PSO and SA.
Basically LEACH is cluster based routing protocol, consists of distributed
clusters. In LEACH protocol, CH is randomly selected and this is constantly
rotated to distribute energy load along all node in the entire network. In
LEACH protocol, to decrease the load of transmitted data, the CH node
compress data arriving from the sensor nodes and send an aggregated packet
of data to the BS [7]. The drawback of LEACH protocol is that it randomizes
he selection of cluster heads for equal energy dissipation. Whereas the
PEGASIS protocol uses a greedy chain to sink. To overcome these drawbacks,
we use particle swarm optimization with simulated annealing (SA).
2.1. Conventional PSO
PSO is a swarm intelligence based optimization technique. PSO was first
developed in [13], inspiring from the social behavior of flocking birds. A wide
variety of particles are exploited to constitute a swarm and it moves in various
direction of sensing area for locating the best possible solution. Each SN in
the sensing area keeps tracking the coordinates of optimum solution or fitness
and is known as the pbest. There is also another value called the gbest that is
obtained the particle in the neighborhood. The position of each particle
in the given space is formulate as [13,14],

 (1)
The previous best position stored by each particle as pbest,

, (2)
and the velocity for each dimension is given by,

. (3)
The position and velocity updates are given respectively as,

(4)




, (5)
where is the dimension,
and
denote positive constants, ,
and
are
random numbers between [0-1],
is position of a particle,
is velocity of
a particle. PSO becomes popular because of its superior performance in may
application and was used with combination of other techniques as a hybrid
algorithm [15,16]. On the other hand, SA proposed in [17] is found to be most
extensive optimization technique. It is stochastic process based on Metropolis
Law [8] that search for the best optimal solution. It discusses about two
factors: - the energy factor and temperature factor , where is referred to
as annealing temperature. The value of is decreased by the cooling schedule.
The region of best point is attained as approaches the lower-limit [1].
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2.2. Leach and its variants
LEACH is a hierarchical routing protocol [11,15] and is a self-organizing
scheme. In LEACH, SNs are divided into multiple clusters. Each cluster
calculates its CH, which collects data from all neighboring nodes in the
cluster. Then CH forwards the aggregated data to the BS. In this process, CH
consumes more energy if it falls near to the BS, which results its quick death
[15]. To overcome this problem, LEACH randomizes the selection of CH, in
order to balance the energy dissipation. LEACH can be found in many
application [11,15,18].
In LEACH, once the cluster is formed, all the nodes decide to be the CH with
probability 𝑝
𝑟
and broadcast its advertisement to all neighbors. A non-cluster
head node finds its cluster by selecting the CH, which has the least energy.
To balance the load, the CH data message is delivered in a sequence of node.
When a CH dies, the cluster is not operated. It is assumed that CH has long
transmission range so that network can have long lifetime. But it is not a good
assumption in terms of signal propagation. Although LEACH was found to be
useful, but it still has problem of short network lifetime because of inefficiently
consumed energy [15,19]. To overcome issues various variants of LEACH,
i.e., LEACH-C, Multi-hop LEACH, Energy-LEACH, MOD-LEACH and many
more were suggested in [11,20,21,22].
2.3. Pegasis and its variants
PEGASIS is an improved form also referred to as an extension of LEACH
protocol. PEGASIS greedy chain formation based algorithm having chain
construction and fusion functions [12]. In PEGASIS, each SN has global
information of its whole sensing network and full knowledge of location of its
neighboring nodes. The chain formation is initialized by a node over the
furthest distance from BS. After the connection of node in chain, all its
correspondence turned off from remaining chain formation process.
It is assumed that SNs are capable to vary the signal strength after getting the
global information. By doing so, energy consumption can be minimized up to
an extent that the network lifetime is maximized. It is presented that
PEGASIS is comparatively more energy efficient as compared to LEACH in
the sense of network lifetime [20]. The energy is saved by forwarding the
aggregated data rather than bulk data to BS. The drawback of PEGASIS is
that due to greedy chain formation process, whole data transmission to BS
takes more time, which causes higher latency. PEGASIS have been modified
and proposed by many researcher [22-27].
3. PROPOSED DATA GATHERING SCHEME
In the proposed energy efficient scheme, a chain is supposed to be formed
allowing the transmission of data by individual nodes. This transmission of
data is carried out to the BS for unequal number of times. To achieve our
design objective, we use PSO [13] and SA [17] that result in energy efficient
network by the individual nodes and enhanced network performance.
3.1 Selection of leader
An optimal chain formation using SA is presented and the selection of a leader
(i.e., CH) is done for communication. In our scheme there is data transmission
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between the closed neighbors and the node become the leader in their turn
depending on its residual energy and location. In the proposed scheme a
chain is formed such that all nodes have equal rights to become the leader.
The network lifetime is maximized because of transmitting unequal number
of times by individual node to the BS based on its

and thus it eliminates
unequal energy consumption of individual nodes, which results in best
performance than LEACH and PEGASIS.
Once a sub-optimal chain formation is done for the first time, a node with
maximum value of

, here is the distance of the BS from that node, is to
be located (i.e., search a node with maximum energy) before initializing the
round of data gathering. In this process, a node, which gives the maximum
value of

is chosen as the leader. The multi-path fading (i.e., distance
power loss) channel mode is assumed in case the leader is concerned to
communicate with the distant BS.
3.2 Data gathering algorithm: SA-PSO
To overcome drawbacks of LEACH and PEGASIS, we present a scheme in
which an efficient leader is chosen for communicating to BS. The SA
algorithm is combined with PSO to efficiently solve the chain formation
problem. Our aim is to minimize the energy usage by SNs in forming an
optimal chain over which the data gathering is done. To simplify the model,
we consider the same radio model as is presented in [7].
To eliminate the local minima trapping of the existing algorithm, the
proposed method of application of SA with PSO solve two major issues: -
increase the diversity of the particle and efficiently solve chain formation
problem. Let's consider $n$ total nodes and solution space which is
ɛ {1,2,3, … ,n} a collection of arrangement and the consecutive nodes are
linked together by a direct link. The combination, i.e., denotes a chain and
=
 represents a permutation of (1,2,.. n).
Proposed algorithm
Step 1: Initilization.
Initializing all parameters (
,
), where 0.7≤≤1.
The randomly selected swarm of m particles is expressed as

,
Step 2: Locating a local best chain.
Search for the local best chain for a given
, based on binary swapping
resulting

is find out with the help of binary swapping between the two
positions of

.

, updated by the newly formed chain.



Local best solution of particle
Step 3: Updating gbest values.
The newly formed chain is updated by the following rule:









(6)







Comparing the

values




(7)
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Step 4: Formation of new chain.
A new chain

 is formed from the

by crossing method.
Step 5: Loop.
The algorithm comes to end if the value of temperature
is less than or
equal to
. The best chain formed is

else move to step 2.
4. SYSTEM DISSIPATION MODEL
Radio model also referred to the energy dissipation model is the most
common model known in WSNs. Block diagram of the energy dissipation
model is shown in Fig. 2. During the processing of different tasks, SNs
dissipate energy while transmission and reception of data from/to BS. Let's
assume that SNs consume the transmitted energy

in transmitting a -bit
packet over a distance meters as shown in Fig. 2.
Figure 2. Block diagram of energy dissipation model.
4.1 Mathematical model
According to the radio model, -bit message is transmitted covering a distance
and the energy required to transmit the message is given by,





(8)



where,

represents the dissipated energy by the transmitter or the
receiver circuit, denotes the distance between sender and receiver, ϵ is the
energy dissipated by electronic and
is the threshold distance. Noted that we
consider the effect of free space and multi-path model in the dissipation
model. Further to the dissipation model, free space path loss is considered for
the transmission/reception between the CNs and CH, whereas, multi-path
model is for the CH to the BS. Then, the total energy
consumed by the
network for a round is given by [5,6],









 (9)
where the average distance between the SNs and CH is 
and is the
dimension of sensing area [6].
4.2. Numerical results
We provide numerical results using the parameters given in Tab.1. To
evaluate the performance of the proposed algorithm, large number of
simulations were done for a given sensing area and the number of sensing
nodes over the given parameters. MATLAB is used as a simulation tools to get
results.
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Table 1. Simulation Result Parameters.
Parameters
Values
Sensing area
100×100
SNs
200
Initial E
0.5J
Message size
4k bits
E
elect
50nJ/bit
∈
0.0015pJ/bit
d
0
70m
Data aggregation cost
5nJ/bit
Figure 3. SNs lifetime versus number of nodes.
It is clearly seen in Fig. 3 that the lifetime of SNs in case of the proposed
algorithm is more as compared to that of using LEACH and PEGASIS. It
means that the performance of the network increases because of enhanced
lifetime. From Fig. 3, it is seen that using SA-PSO, SNs die after completing
more than 6000 rounds while for PEGASIS, nodes die out up to 4500 and for
LEACH nodes die at 3500.
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Figure 4. Data packet transmission to BS.
Figure 5. SNs dead time versus number of nodes.
Figure 4 presents the transmission of data packets to the BS. It is noted that
SA-PSO outperform the other two LEACH and PEGASIS algorithms. It can
be easily figured out that the data gathering efficiency is increased using the
SA-PSO algorithm. Figure 5 shows dead nodes with the passage of time. It is
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seen that using SA-PSO, WSN has better performance because of longer
lifetime.
For bigger WSNs, the low death rate of nodes not only reduces the network
lifetime but its stability as well. After looking results, it is observed that the
SA-PSO algorithm is comparatively more energy efficient algorithm for WSN
rather than LEACH and PEGASIS in terms of lifetime and packet
transmission as shown in Fig. 3 and Fig. 4.
5. CONCLUSION
In this research paper, an energy efficient scheme for data gathering in WSN
is presented. A novel SA-PSO algorithm is proposed in which best chain
formation procedure is adopted. The presented analysis shows that SA-PSO
outperforms than PEGASIS and LEACH in terms of network lifetime,
communication overhead and rate of SNs deaths. From simulation results, it
is confirmed that SA-PSO outperforms LEACH and PEGASIS by eliminating
the communication overhead in cluster formation and introduces low latency
since it forms a chain among SNs. It is seen that SNs using SA-PSO remains
active for over 1500 rounds than PEGASIS and 2500 rounds than LEACH.
6. ACKNOWLEDGMENTS
The authors would like to thank Dr. Ishtiaq Ahmad and Dr. Noor M. Sheikh for
the follow discussion throughout the research work and thanks to Higher
Education Commission Islamabad (HEC), Pakistan for their financial support to
present the research work.
7. REFERENCES
S. Kalantary, and S. Taghipour, “A survey on architectures, protocols,
applications, and management in wireless sensor networks”, Journal of
Advanced Computer Science and Technology, vol. 3, no. 1, (2014), pp. 1--
11.
S. Gurbhej and K. Rajneet, “Review on Data dissemination and gathering in
Wireless Sensor Networks”, International Journal of emerging research in
management and Technology, vol. 2013, no. 1, (2013), pp. 63--67.
S. A. Imam, V. K. Sachan, and V. P. Dubey, “Design and analysis of energy
efficient communication methods for wireless sensor networks”,
Telecommunications and Radio Engineering, vol. 74, no. 19, (2015), pp.
1705--1714.
Y. Wu, Z. Mao, S. Fahmy, and N. B. Shroff, “Constructing maximum-lifetime
data-gathering forests in sensor networks”, IEEE/ACM Transactions on
Networking, vol. 18, no. 5, (2010), pp. 1571--1584.
C. Karakus, A. B. Gurbuz, and B. Tavli, “Analysis of energy efficiency of
compressive sensing in wireless sensor networks”, IEEE Sensors Journal, vol.
13, no. 5, (2013), pp. 1999--2008.
Yongming Qin, Qiuling Tang, Ye Liang, Xiuyu Yue, Xian Li, “An Energy-
Efficient Cooperative MIMO Scheme for Wireless Sensor Networks”, 14th
IEEE International Conference on Computational Science and Engineering.
Dalian, China, (2011), pp. 471--474.
X. He, X. Zhou, M. Juntti, and T. Matsumoto, “Data and error rate bounds for
binary data gathering wireless sensor networks”, Proceedings of the IEEE
Efficient energy utilization in wireless sensor networks: an algorithm
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue.12
145
16th International Workshop on Signal Processing Advances in Wireless
Communications (SPAWC '15). Stockholm, Sweden, June (2015), pp. 505--
509.
M. Radi, B. Dezfouli, K. A. Bakar and M. Lee, “Multipath Routing in Wireless
Sensor Networks: Survey and Research Challenges”, Sensors, vol. 12, no. 1,
(2012), pp. 650--685.
X. Xing, D. Xie, G. Wang, “Energy-balanced data gathering and aggregating in
WSNs: a compressed sensing scheme”, International Journal of Distributed
Sensor Networks, vol. 10, no. 11, (2016), pp. 186.
K. Navdeep, S. Deepika, S. Prabhdeep, “Classification of Hierarchical Routing
Protocols in Wireless Sensor Network: A Survey”, International Journal of
P2P Network Trends and Technology, vol. 3, no. 1, (2013), pp. 56--61.
M. Tong and M. Tang, “LEACH-B: An Improved LEACH Protocol for Wireless
Sensor Network”, 2010 6th International Conference on Wireless
Communications Networking and Mobile Computing (WiCOM), Chengdu,
China, Oct., (2010), pp. 1--4.
HU Sen-lai, ZHANG Yu, JIN Xin-yu, LI Hui-zhong, “Improvement of PEGASIS
Algorithm in Wireless Sensor Networks Using GA”, Journal of Jiangnan
University, vol. 4, (2008), pp. 1-10.
J. Kennedy and R. C Eberhart, “Particle swarm optimization”, Proc. IEEE
International Conference on neural networks, IEEE service center,
Piscataway, NJ, (1995), pp. 1942--1948.
O. Cakir, I. Kaya, A. Yazgan, O. Cakir, E. Tugcu, “Emitter Location Finding using
Particle Swarm Optimization”, Radioengineering, vol. 23, no. 1, (2014), pp.
252--258.
Wen Liu, Zhiyuan Ma, Zhongliang Deng and Lianming Xu, “Energy Balance
Algorithm based on LEACH”, EEE 12th International Conference on
Communication Technology, Nanjing, China, China, Nov., (2010), pp. 681-
-684.
N. Jasmine, J. J. Paulraj, and R. P. Prapoorna, “A Faster Routing Scheme for
Stationary Wireless Sensor Networks - A Hybrid Approach”, International
Journal of Ad Hoc, Sensor & Ubiquitous Computing, vol. 1, no. 1, (2010),
pp. 1--10.
M. T. Nguyen and K. A. Teague, “Tree-based energy-efficient data gathering in
wireless sensor networks deploying compressive sensing”, Proceedings of the
23rd Wireless and Optical Communication Conference (WOCC’14).
Newyark, NJ, USA, May (2014), pp. 1--6.
A. Norouzi, F.S. Babamir and A.H. Zaim, “A New Clustering Protocol for Wireless
Sensor Networks using genetic algorithm approach”, Wireless Sensor
Networks, vol. 2011, no. 3, (2011), pp. 362--370.
M. Botta, M. Simek, “Adaptive Distance Estimation Based on RSSI in 802.15.4
Network”, Radioengineering Journal, vol. 22, no. 4, (2013), pp. 1162-1168.
S. K. Singh, P. Kumar and J. P. Singh, “A Survey on Successors of LEACH
Protocol”, IEEE Access, vol. 5, (2017), pp. 4298--4328.
Mounir Arioua, Y. el Assari, Imad Ez-zazi and A. el Oualkadi, “Multi-hop Cluster
Based Routing Approach for Wireless Sensor Networks”, Procedia
Computer Science, vol.~83, no.~Supplement C, (2016), pp. 584--591.
N. G. Palan and B. V. Barbadekar and S. Patil, “Low energy adaptive clustering
hierarchy (LEACH) protocol: A retrospective analysis”, International
Efficient energy utilization in wireless sensor networks: an algorithm
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue.12
146
Conference on Inventive Systems and Control (ICISC), Coimbatore, India,
(2017), p.~1-12.
Ez-Zazi, Imad, Arioua, Mounir, El Oualkadi, Ahmed and Lorenz, Pascal, “A
Hybrid Adaptive Coding and Decoding Scheme for Multi-hop Wireless
Sensor Networks”, Wireless Personal Communication, vol. 94, no. 4, (2017),
pp. 3017--3033.
M. N. Khan and M. Jamil, “Performance improvement in lifetime and throughput
of LEACH protocol”, Indian Journal of Science and Technology, vol. 9, no.
21, (2016), pp.1-6.
Muhammad Nasir Khan, “Importance of Noise Models in FSO Communications,”
EURASIP Journal of Wireless Communication and Networking (EURASIP,
JWCN), vol. 2014, no. 102, Dec. (2014), pp. 1-10.
Muhammad Nasir Khan and Mohsin Jamil, “Maximizing Throughput of Free
Space Communication Systems using puncturing Technique,” in Arab. J. Sci.
& Eng. vol. 39, no.12, (2014), pp. 8925-8933.
Muhammad Nasir Khan and M. Jamil. “EXIT chart behavior for the Hybrid
FSO/RF communication system” in 2015 10th International Conference on
Information, Communications and Signal Processing (ICICS), Singapore, (2015),
pp. 1-5.