**Wireless Sensor
Networks: Network Life Time Enhancement using an Improved Grey Wolf
Optimization Algorithm **

Kanthi
M and Ravilla Dilli^{*} ^{}

*Electronics
and Communication Engineering, Manipal Institute of Technology, Manipal Academy
of Higher Education, Manipal, Udupi District, Karnataka 576104, India.*

**E-mail: *dilli.ravilla@manipal.edu
(R. Dilli)

**Abstract
**

Clustering
is an effective technique in a wireless sensor network (WSN) to minimize energy
consumption and low-energy adaptive clustering hierarchy (LEACH) is the most
popular clustering protocol in WSNs. However, a random selection of cluster
head (CH) in LEACH protocol results in poor performance in real network
deployments. Dynamic formation of CHs and energy-aware clustering schemes helps
in enhancing the lifetime of WSNs. In
this paper, we have proposed an improved version of the grey wolf optimization
(IGWO) algorithm to overcome the premature convergence of conventional GWO
algorithm. The algorithm is applied to optimize the CH selection in WSNs to
maximize the network lifetime. The improvements of IGWO algorithm are based
on sink distance, CH balancing factor, residual energy, and average intra‐cluster
distance. The proposed algorithm has been tested in terms of the number of
rounds, number of operating nodes, number of transmissions, and energy levels
used for communications. The performance results of
IGWO algorithm are compared with a conventional LEACH protocol, it is observed
that the total number of operational rounds has been increased by 441.4% and
869.6% for a network size of 50 and 699.8% and 990.8% for a network size of 100
when CH selection probability is 5% and 10%, respectively. The simulation
results show that the proposed IGWO based LEACH protocol outperforms the
existing state-of-the-art algorithms based on GWO for enhancing the WSNs
lifetime.

**Table of Contents**

*Keywords:*** **Wireless
sensor network; Low-energy adaptive clustering; Grey wolf optimization; Sensor
network lifetime; Cluster head selection; Improved grey wolf optimization
algorithm.

**1. Introduction**

A wireless sensor network (WSN) is a combination of
several sensors installed on physical devices that are geographically
distributed in a network field to collect the data based on the application.
The scattered nodes can be grouped into clusters to deliver the information to
the base station (BS). The sensor nodes are self-organized, having the ability
to sense, process, and communicate. In most WSN applications, it is not
feasible to recharge or replace the batteries of sensor nodes. The main
challenges in WSNs are the optimal number of CHs, energy efficiency, optimal
network coverage, network lifetime, stability, reliability. Designing an
energy-efficient protocol for optimum selection of CHs is essential for
maximizing network lifetime in WSNs. Low energy adaptive clustering (LEACH) is
a hierarchical protocol at MAC sublayer used to form clusters in WSNs.^{[1]} The node
which has maximum residual energy becomes CH to improve energy efficiency and
network lifetime. The functioning of LEACH protocol has two phases: the “setup
phase” in which cluster formation is performed and in the next phase called the
“steady phase”, information is transferred to the sink node.

**1.1 Setup phase**

In this phase, every sensor
node chooses a random number in the range 0 to 1 and if this number is lower
than the threshold of n^{th} node ‘T(n)’, then the sensor node becomes
CH for that specific cluster and the remaining nodes will act as cluster
members. The CH selection phase and clustering phase are the key parts of the
LEACH protocol. Each sensor node is having threshold energy as:

T(n) = Ɐ n ϵ G

=
0 otherwise (1)

Initially, CHs are chosen based on a threshold value
T(n) and BS transmits the message to every CH of the first round. This strategy
ensures that all the sensor nodes uniformly spend equal energy. Once CH selection
is completed, the CHs advertise their selection to all sensor nodes in the
network. After receiving the advertisements, the sensor nodes choose their
nearest CH based on the received signal strength, and then CHs assign a TDMA
schedule for the nodes in their respective clusters.

**1.2 Steady phase**

In this phase, cluster
members join their CHs and transmit data based on the TDMA schedule. To achieve
energy conservation, CHs perform data aggregation (or fusion) through local
computation and the resultant data is further transmitted to BS. A longer
steady phase minimizes overhead of cluster formation. After a certain amount of
time in the steady phase, the selection of CHs is repeated through the setup
phase. Adaptive CH selection based on event occurrence instead of periodic
selection significantly minimizes message overheads and computations without
compromising for network throughput and end-to-end delay.^{[2]}

**1.3
Grey Wolf Optimization (GWO) algorithm **

GWO is a meta-heuristic
algorithm inspired by the food searching behavior of grey wolves and it mimics
the hunting mechanism and leadership hierarchy of grey wolves present in
nature. The wolves do attacks in a group and the best position of any wolf can
be provided with the best solution. They follow a strict social dominance
hierarchy as alphas (α), beta (β), delta (δ), and omega (ω)
which is shown in Fig. 1 and the implementation
of hunting, searching, encircling, and attacking the prey. The dominance\
decreases from top to bottom of the hierarchy. This algorithm is applied to
WSNs in which the position of wolves represents the position of CH in each
cluster and the position of the grey wolves updates the position of CH in LEACH
protocol.

**Fig. 1 **Hierarchy of Grey Wolves.

A schematic illustration of the synthesis process of
the α wolves (both male and female) are most responsible in the decision
making, organization, the discipline of the group and the other category of
wolves in a group follows α wolf’s decisions. In the second level, β
wolves (either male or female) help, advice the α wolf in decision-making,
feedback and pass commands to lower level wolves in the hierarchy. δ
category wolf is subordinate of α, β and dominant of ω wolves.
δ wolves play a role of scouts, sentinels (guarantee the safety and
protection of group), elders (experienced wolves who are used by α and
β wolves, hunters (help the α, β wolves in hunting for providing
food for the group), caretakers (take care of weak, ill and wounded wolves in
the group). Grey wolf, ω is the lowest level animal in a group that
follows orders of all other dominant wolves. ω wolf assists the entire
group and if ω wolf is absent in a group, there are chances of the
internal fight due to frustration of all other wolves. The main steps of group
hunting by grey wolves are: 1) Tracing, chasing, and approaching the prey; 2)
pursuing, encircling, and harassing the prey till it stops movement; 3) attack
towards prey. The social hierarchy and hunting techniques of grey wolves are
modeled to design the GWO algorithm and its performance optimization. The GWO
algorithm is used in solving real time engineering problems across various
fields.^{[3-4]}

**2. ****Performance optimization in WSNs**

Optimization of CH selection in WSN greatly improves
the network lifetime. As energy is a major constraint in WSNs, optimum energy
utilization is an important aspect in WSNs to enhance the lifetime of the
network and this feature is essential for IoT and IIoT applications. As a part
of energy harvesting in WSNs, there are many algorithms and techniques proposed
in the literature for CHs selection based on residual energy, centrality, the
position of nodes, etc., to minimize the energy consumption and improve network
lifetime without compromising the reliability of the network. Cluster formation
and CH selection as well as re-formation and re-selections based on residual
energy further improve network efficiency over LEACH protocol. However, the
reformation of clusters occurs only if there exists a large separation between
CHs and sensor nodes. This approach improves the efficiency of the network in
terms of lifetime and decreases complexity over the LEACH protocol.^{[5]} In a
normal operation of LEACH protocol, the energy consumption of WSN is equalized
by randomly selecting CHs in an iterative fashion and it leads to network
instability. Therefore, it is essential to optimize the routing protocol and
number of CHs in order to minimize the energy consumption for data
transmissions. Decentralized clustering protocols that differentiate intra-cluster
and inter-cluster communications reduce the number of transmissions and routing
control packets thereby longer lifetime for the network.^{[6]}
CDMA-based LEACH protocol uses a different set of codes in each cluster to
reduce inter-cluster interference.^{[7]}
Rank-based algorithms consider a number of links between nodes and path cost
while electing CHs, LEACH protocol based on node rank enhances the lifetime of
WSN.^{[8]} Using
Voronoi diagrams to form clusters and ant colony algorithms to optimize
multi-hop routing can improve the first node death time by 14.5% and 127%
compared to SEP and LEACH protocols respectively.^{[9]} A
Combination of ARSH-FATI-based CH selection algorithm and rank-based clustering
minimizes the consumption of communication energy at sensor nodes and this
algorithm switches dynamically between exploration and exploitation of search
process during the run-time to have a 25% higher network lifetime over PSO.^{[10]} Fuzzy
based clustering provides an efficient data aggregation and enhances the life
span of WSNs.^{[11,12]} In
heterogeneous WSNs, adaptive clustering routing techniques consider residual
energy at a given node and its location for efficient CHs selection. The nodes
which are close to BS and have more residual energy become CH with higher
probability. The CHs which are far away from BS uses multi-hop routing and the
other nodes use single-hop routing. These features balance energy consumption
and enhances the network lifetime, network throughput over SEP and DEEC
protocols.^{[13]} A hybrid
meta-heuristic algorithm with a combination of cuckoo search and Krill herd for
homogeneous and heterogeneous WSNs environments enhance their lifetime. Krill
herd algorithm is used to compute the optimal cluster centroid positions and a
cuckoo search algorithm is applied to select optimal CHs.^{[14]}

**3.
Performance optimization of WSN based on GWO algorithm **

The selection of CH is not optimal in conventional
LEACH schemes, therefore usage of GWO improves the optimal selection of CH in
LEACH protocol.^{[15]} GWO is a
unique metaheuristic algorithm that mimics the social behavior of grey wolves
with respect to their leadership hierarchy and, attacking strategy.
Localization problems (search for the geographical position of unknown nodes
using anchor nodes) in WSNs can be resolved using the GWO algorithm. CHs selection
using GWO considers the distance between clusters and sink, current residual
energy of each node and predicted energy consumption. This technique considers
the same clustering in consecutive rounds to enhance energy efficiency and the
saved energy can be utilized for cluster reformation. For CHs that are far away
from BS can use dual-hop routing and it ensures optimum energy consumption.^{[16]} GWO is
applied in defining objective functions and weights for efficient cluster
formation and CH selection.^{[17]}
Appropriate fitness function ensures the coverage of WSN and it is fed to the
GWO algorithm to determine its optimum. This procedure outperforms the LEACH
clustering and routing algorithm in terms of network throughput, lifetime and,
residual energy.^{[18]} Energy
efficiency and network stability are two typical trade-off parameters in WSNs.
Distance-based stable CDS method and clustering algorithms using GWO in WSN
minimizes effective transmission distance with deterministic CH selection and
achieves a load-balanced, energy efficient and stable network. These algorithms
perform much better than GA and LEACH by 70.5% and 74.7% respectively in terms
of network stability and energy efficiency.^{[19]} To
improve the energy-efficiency of the clustering mechanism, a combination of two
meta-heuristic algorithms named whale and GWO can be used to define the objective
function for the formation of an optimal number of clusters, dynamic CH
selection, and relay nodes selection on a priority basis.^{[20,21]} MLHP
with three levels based on the GWO for WSNs shows better performance in terms
of maximum residual energy, network lifetime, stability period compared to
other algorithms named LEACH, DEEC, and SEP. At level 1, BS selects CHs, at
level 2, GWO routing gives the best route to BS for data transfer, and at level
3, distributed clustering is performed based on cost function.^{[22]} In the
cluster-based architecture of WSNs, gateways which are far from BS communicate
with BS through the gateways that are close to BS. This causes faster energy
depletion at gateways closer to BS because of heavy traffic load and causes
energy hole problems around BS. GWO can resolve the energy hole issue by
distributing traffic load with minimum traversal distance and number of hops.
GWO based algorithm performs better than GA, PSO and multi-objective fuzzy
clustering.^{[23]} The
percentage of localized nodes, quick convergence rate and success rate of GWO
algorithm is higher with less computation time and, localization error compared
to PSO, and MBA metaheuristic algorithms.^{[24]} Random
deployment of sensor nodes causes a high degree of aggregation and a low
coverage rate. The simulated annealing method embedded into GWO avoids falling
into local optimum, improves convergence rate, accelerates the convergence
speed, coverage optimization and this leads to higher network stability and
network life cycle in WSNs.^{[25]} GWO is
used to compute the threshold for sensor decision rules from the fusion center
that is independent on initial values and with lesser complexity. The results of
this algorithm demonstrate the reduced buyer’s risk by 15%-20% of fusion
system.^{[26]} The
Virtual Force-Levy technique embedded with the GWO algorithm effectively
enhances the coverage optimization in WSNs in terms of uniformity in nodes
distribution, node’s average moving distance, changes in the number of nodes,
and coverage rate.^{[27]} The
robust deployment of WSNs for industrial applications introduces redundant
nodes and causes overhead. Quantum clone GWO algorithm is proposed to design
and optimize the sensor duty cycle, also avoid falling into a local optimum.
The convergence speed of this algorithm is much faster than GA, SA, and
enhances the network lifetime.^{[28]} The
combination of crow search optimization with the GWO algorithm for optimal CH
selection enhances the network lifetime by reducing delay, energy
stabilization, and distance between nodes.^{[29]}
Providing energy efficiency in heterogeneous WSNs is much more challenging due
to the irrational utilization of energy at nodes. For this kind of network, GWO
provides a solution based on the node’s fitness values. The fitness values are
treated as weights and they are dynamically updated based on the distance
between wolves and their prey. This algorithm ensures the optimal CHs selection
and enhances the network lifetime by 55.7%, 46.3%, 27.0%, and 31.9% over SEP,
modified SEP, fitness value based-GWO, and DEEC respectively.^{[30]} The combination
of GWO and GOA algorithms improves the convergence levels of meta-heuristic
algorithms.^{[31]}
GWO can be used in the implementation of server-less
systems to improve task allocation and minimize runtimes.^{[32]} The
performance of intrusion detection systems in WSNs can be enhanced using binary
GWO with a support vector machine. These techniques can minimize false alarm
rates in the WSN environment thereby detection rate and accuracy of the
intrusion detection system are increased with minimum processing and execution
times.^{[33]} Energy
aware clustering in WSNs based on fuzzy logic improves the formation of
clusters and CH selection. This technique uses a neural network to have an
optimum training set of energy and density values at all sensor nodes to
compute the expected energy for uncertain CHs.^{[34]} “Optimal
Clustering in Circular Networks” is proposed where a single-hop communication
between the sink and CHs is replaced with optimal multi-hops to enhance the
WSNs lifetime by reducing the energy consumption.^{[35]} The precision
of the node positions is a very significant parameter for effective data
transmission and to enhance the network lifetime in WSNs. Geographic routing
based on weighted centroid localization improves the precision of node
positions which uses fuzzy logic to compute the position of unknown nodes. This
technique helps in the efficient selection of next-hop CH for minimizing the
energy dissipation and enhances network lifetime.^{[36]} An
improved version of GWO which is based on the fitness value, ensures optimal
distribution of CHs and enhances the chances of finding the optimal solution in
GWO. To decrease the energy consumption, each sensor node’s communication
distance is recomputed based on the distance between BS and CHs. This feature
improves the WSN in terms of throughput, energy consumption and, stability by
57.8% compared to the LEACH protocol. The optimum method for selecting CHs and
their distribution with balanced cluster structures can be achieved using
fitness value based GWO. This optimal solution ensures that the node in a cluster
located near to BS and having maximum energy becomes CH. To decrease the energy
consumption, the communication range of sensor nodes is recalculated as per the
distances between BS and CHs, it means that when a new CH is elected. This
approach minimizes the average communication distance, reduces the energy
consumption and improves WSN stability by 31.5% and 57.8% compared to SEP,
LEACH protocols respectively.^{[37]} IGWO
algorithm benefits from DLH search strategy which is inherited from individual
wolf’s hunting behavior. DLH uses dimension learning to collect neighbor’s
information at every wolf and this information is shared among other wolves.
This DLH search strategy maintains diversity as well as improves balancing
between global and local search to improve convergence speed. The results of the
engineering experiments and statistical reports prove that the applicability,
optimal performance, effectiveness, solution stability, convergence speed,
robustness, convergence precision, and efficiency of the IGWO algorithm
outperforms the other existing metaheuristic algorithms.^{[38,39]}