Clustering is considered as one of the most prominent solutions to preserve theenergy in the wireless sensor networks. However, for optimal clustering, anenergy efficient cluster head selection is quite important. Improper selectionofcluster heads(CHs) consumes high energy compared to other sensor nodesdue to the transmission of data packets between the cluster members and thesink node. Thereby, it reduces the network lifetime and performance of thenetwork. In order to overcome the issues, we propose a novelcluster headselection approach usinggrey wolf optimization algorithm(GWO) namelyGWO-CH which considers the residual energy, intra-cluster and sink distance.In addition to that, we formulated an objective function and weight parametersfor anefficient cluster head selection and cluster formation. The proposedalgorithm is tested in different wireless sensor network scenarios by varyingthe number of sensor nodes and cluster heads. The observed results conveythat the proposed algorithm outperforms in terms of achieving better networkperformance compare to other algorithms.
AN OPTIMIZED WEIGHT BASED CLUSTERING ALGORITHM IN HETEROGENEOUS WIRELESS SENS...cscpconf
The last few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in various fields like disastermanagementbattle field surveillance, and border security surveillance. In such applications, a large number of sensor nodes are deployed, which are often unattended and work autonomously. The process of dividing the network into interconnected substructures is called clustering and the interconnected substructures are called clusters. The cluster head (CH) of each cluster act as a coordinator within the substructure. Each CH acts as a temporary base station within its zone or cluster. It also communicates with other CHs. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. It can also increase network scalability. Researchers in all fields of wireless sensor network believe that nodes are homogeneous, but
some nodes may be of different characteristics to prolong the lifetime of a WSN and its reliability. We have proposed an algorithm for better cluster head selection based on weights for different parameter that influence on energy consumption which includes distance from base station as a new parameter to reduce number of transmissions and reduce energy consumption by sensor nodes. Finally proposed algorithm compared with the WCA, IWCA algorithm in terms of number of clusters and energy consumption.
Cluster Head Selection Techniques for Energy Efficient Wireless Sensor Networ...ijsrd.com
Wireless sensor networks are widely considered as one of the most important technologies. The Wireless Sensor Network (WSN) is a wireless network consisting of ten to thousand small nodes with sensing, computing and wireless communication capabilities. They have been applied to numerous fields such as healthcare, monitoring system, military, and so forth. Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Energy efficiency is thus a primary issue in maintaining the network. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks. This paper elaborates several techniques like LEACH, HEED, LEACH-B, PEACH, EEUC of cluster head selection for energy efficient in wireless sensor networks.
Sierpinski carpet fractal monopole antenna for ultra-wideband applications IJECEIAES
Microstrip antenna is broadly used in the modern communication system due to its significant features such as light weight, inexpensive, low profile, and ease of integration with radio frequency devices. The fractal shape is applied in antenna geometry to obtain the ultra-wideband antennas. In this paper, the sierpinski carpet fractal monopole antenna (SCFMA) is developed for base case, first iteration and second iteration to obtain the wideband based on its space filling and self-similar characteristics. The dimension of the monopole patch size is optimized to minimize the overall dimension of the fractal antenna. Moreover, the optimized planar structure is proposed using the microstrip line feed. The monopole antenna is mounted on the FR4 substrate with the thickness of 1.6 mm with loss tangent of 0.02 and relative permittivity of 4.4. The performance of this SCFMA is analyzed in terms of area, bandwidth, return loss, voltage standing wave ratio, radiation pattern and gain. The proposed fractal antenna achieves three different bandwidth ranges such as 2.6-4.0 GHz, 2.5-4.3 GHz and 2.4-4.4 GHz for base case, first and second iteration respectively. The proposed SCFMA is compared with existing fractal antennas to prove the efficiency of the SCFMA design. The area of the SCFMA is 25×20 푚푚 2 , which is less when compared to the existing fractal antennas.
Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.
Load Balancing for Achieving the Network Lifetime in WSN-A SurveyAM Publications
a wireless sensor network is network form of sense compute, and communication elements which helps to
observe, events in a specified environment. Sensor nodes in wireless sensor network are depends on battery power they
have limited transmission range that’s why energy efficiency plays a vital role to minimize the overhead through which
the Network Lifetime can be achieved. The lifetime of network, depends on number of nodes, strength, range of area
and connectivity of nodes in the network. In this paper we are over viewing techniques which are used in wireless sensor
network for load balancing. Wireless sensor network having different nodes with different kind of energy which can be
improve the lifetime of the network and its dependability. This paper will provide the person who reads with the
groundwork for research in load balancing techniques for wireless sensor networks.
An Ant colony optimization algorithm to solve the broken link problem in wire...IJERA Editor
Aco is a well –known metahuristic in which a colony of artificial ants cooperates in explain Good solution to a combinational optimization problem. Wireless sensor consisting of nodes with limited power is deployed to gather useful information From the field. In wireless sensor network it is critical to collect the information in an energy efficient Manner.ant colony optimization, a swarm intelligence based optimization technique, is widely used In network routing. A novel routing approach using an ant colony optimization algorithm is proposed for wireless sensor Network consisting of stable nodes illustrative example details description and cooperative performance test result the proposed approach are included. The approach is also implementing to a small sized hardware component as a router chip simulation result show that proposed algorithm Provides promising solution allowing node designers to efficiency operate routing tasks.
Ca mwsn clustering algorithm for mobile wireless senor network [graphhoc
This paper proposes a centralized algorithm for cluster-head-selection in a mobile wireless sensor network.
Before execution of algorithm in each round, Base station runs centralized localization algorithm whereby
sensors update their locations to base station and accordingly Base station performs dynamic clustering.
Afterwards Base station runs CA-MWSN for cluster-head-selection. The proposed algorithm uses three
fuzzy inputs Residual energy, Expected Residual Energy and Mobility to find Chance of nodes to be elected
as Cluster-head. The node with highest Chance is declared as a Cluster-head for that particular cluster.
Dynamic clustering provides uniform and significant distribution of energy in a non-uniform distribution of
sensors. CA-MWSN guarantees completion of the round.
AN OPTIMIZED WEIGHT BASED CLUSTERING ALGORITHM IN HETEROGENEOUS WIRELESS SENS...cscpconf
The last few years have seen an increased interest in the potential use of wireless sensor networks (WSNs) in various fields like disastermanagementbattle field surveillance, and border security surveillance. In such applications, a large number of sensor nodes are deployed, which are often unattended and work autonomously. The process of dividing the network into interconnected substructures is called clustering and the interconnected substructures are called clusters. The cluster head (CH) of each cluster act as a coordinator within the substructure. Each CH acts as a temporary base station within its zone or cluster. It also communicates with other CHs. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. It can also increase network scalability. Researchers in all fields of wireless sensor network believe that nodes are homogeneous, but
some nodes may be of different characteristics to prolong the lifetime of a WSN and its reliability. We have proposed an algorithm for better cluster head selection based on weights for different parameter that influence on energy consumption which includes distance from base station as a new parameter to reduce number of transmissions and reduce energy consumption by sensor nodes. Finally proposed algorithm compared with the WCA, IWCA algorithm in terms of number of clusters and energy consumption.
Cluster Head Selection Techniques for Energy Efficient Wireless Sensor Networ...ijsrd.com
Wireless sensor networks are widely considered as one of the most important technologies. The Wireless Sensor Network (WSN) is a wireless network consisting of ten to thousand small nodes with sensing, computing and wireless communication capabilities. They have been applied to numerous fields such as healthcare, monitoring system, military, and so forth. Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Energy efficiency is thus a primary issue in maintaining the network. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks. This paper elaborates several techniques like LEACH, HEED, LEACH-B, PEACH, EEUC of cluster head selection for energy efficient in wireless sensor networks.
Sierpinski carpet fractal monopole antenna for ultra-wideband applications IJECEIAES
Microstrip antenna is broadly used in the modern communication system due to its significant features such as light weight, inexpensive, low profile, and ease of integration with radio frequency devices. The fractal shape is applied in antenna geometry to obtain the ultra-wideband antennas. In this paper, the sierpinski carpet fractal monopole antenna (SCFMA) is developed for base case, first iteration and second iteration to obtain the wideband based on its space filling and self-similar characteristics. The dimension of the monopole patch size is optimized to minimize the overall dimension of the fractal antenna. Moreover, the optimized planar structure is proposed using the microstrip line feed. The monopole antenna is mounted on the FR4 substrate with the thickness of 1.6 mm with loss tangent of 0.02 and relative permittivity of 4.4. The performance of this SCFMA is analyzed in terms of area, bandwidth, return loss, voltage standing wave ratio, radiation pattern and gain. The proposed fractal antenna achieves three different bandwidth ranges such as 2.6-4.0 GHz, 2.5-4.3 GHz and 2.4-4.4 GHz for base case, first and second iteration respectively. The proposed SCFMA is compared with existing fractal antennas to prove the efficiency of the SCFMA design. The area of the SCFMA is 25×20 푚푚 2 , which is less when compared to the existing fractal antennas.
Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms. This paper gives a brief overview on clustering process in wireless sensor networks. A research on the well evaluated distributed clustering algorithm Low Energy Adaptive Clustering Hierarchy (LEACH) and its followers are portrayed artistically. To overcome the drawbacks of these existing algorithms a hybrid distributed clustering model has been proposed for attaining energy efficiency to a larger scale.
Load Balancing for Achieving the Network Lifetime in WSN-A SurveyAM Publications
a wireless sensor network is network form of sense compute, and communication elements which helps to
observe, events in a specified environment. Sensor nodes in wireless sensor network are depends on battery power they
have limited transmission range that’s why energy efficiency plays a vital role to minimize the overhead through which
the Network Lifetime can be achieved. The lifetime of network, depends on number of nodes, strength, range of area
and connectivity of nodes in the network. In this paper we are over viewing techniques which are used in wireless sensor
network for load balancing. Wireless sensor network having different nodes with different kind of energy which can be
improve the lifetime of the network and its dependability. This paper will provide the person who reads with the
groundwork for research in load balancing techniques for wireless sensor networks.
An Ant colony optimization algorithm to solve the broken link problem in wire...IJERA Editor
Aco is a well –known metahuristic in which a colony of artificial ants cooperates in explain Good solution to a combinational optimization problem. Wireless sensor consisting of nodes with limited power is deployed to gather useful information From the field. In wireless sensor network it is critical to collect the information in an energy efficient Manner.ant colony optimization, a swarm intelligence based optimization technique, is widely used In network routing. A novel routing approach using an ant colony optimization algorithm is proposed for wireless sensor Network consisting of stable nodes illustrative example details description and cooperative performance test result the proposed approach are included. The approach is also implementing to a small sized hardware component as a router chip simulation result show that proposed algorithm Provides promising solution allowing node designers to efficiency operate routing tasks.
Ca mwsn clustering algorithm for mobile wireless senor network [graphhoc
This paper proposes a centralized algorithm for cluster-head-selection in a mobile wireless sensor network.
Before execution of algorithm in each round, Base station runs centralized localization algorithm whereby
sensors update their locations to base station and accordingly Base station performs dynamic clustering.
Afterwards Base station runs CA-MWSN for cluster-head-selection. The proposed algorithm uses three
fuzzy inputs Residual energy, Expected Residual Energy and Mobility to find Chance of nodes to be elected
as Cluster-head. The node with highest Chance is declared as a Cluster-head for that particular cluster.
Dynamic clustering provides uniform and significant distribution of energy in a non-uniform distribution of
sensors. CA-MWSN guarantees completion of the round.
Genetic-fuzzy based load balanced protocol for WSNsIJECEIAES
Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AN ENHANCED HYBRID ROUTING AND CLUSTERING TECHNIQUE FOR WIRELESS SENSOR NETWORKijwmn
Wireless Sensor Networks (WSN) have extensively deployed in a wide range of applications. However, WSN still faces several limitations in processing capabilities, memory, and power supply of sensor nodes. It is required to extend the lifetime of WSN. Mainly this is achieved by routing protocols choosing the best transmission path in-network with desired power conservation.This cause is developing a generic protocol framework for WSNa big challenge. This work proposed a new routing technique, described as Hybrid Routing-Clustering (HRC) model. This new approach takes advantage of clustering and routing procedures defined in K-Mean clustering and AODV routing, which constituted of three phases. This development aims to achieve enhanced power conservation rate in consequence network lifetime. An extensive evaluation methodology utilized to measure the performance of the proposed model in simulated scenarios.The results categorized in terms of the average amount of packet received and power conservation rate. The Hybrid Routing-Clustering (HRC) model was determined, showed enhanced results regarding both parameters. In the end, they are comparing these results with well-known routing and well-known clustering algorithms.
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A DYNAMIC ROUTE DISCOVERY SCHEME FOR HETEROGENEOUS WIRELESS SENSOR NETWORKS B...csandit
With the development of new networking paradigms and wireless protocols, nodes with different capabilities are used to form a heterogeneous network. The performance of this kind of networks is seriously deteriorated because of the bottlenecks inside the network. In addition, because of the application requirements, different routing schemes are required toward one particular application. This needs a tool to design protocols to avoid the bottlenecked nodes and adaptable to application requirement. Polychromatic sets theory has the ability to do so. This paper demonstrates the applications of polychromatic sets theory in route discovery and protocols design for heterogeneous networks. From extensive simulations, it shows the nodes with high priority are selected for routing, which greatly increases the performance of the network. This demonstrates that a new type of graph theory could be applied to solve problems of complex networks.
Wireless sensor networks, clustering, Energy efficient protocols, Particles S...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality.
The most important issue in this type of networks is energy constraints. In this area several researches have
been done from which clustering is one of the most effective solutions. The goal of clustering is to divide
network into sections each of which has a cluster head (CH). The task of cluster heads collection, data
aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach
for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal
fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual
energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation
results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF,
PSO-MV) in terms of network lifetime and energy consumption.
NEW APPROACH TO IMPROVING LIFETIME IN HETEROGENEOUS WIRELESS SENSOR NETWORKS ...chokrio
The major challenge for wireless sensor networks is energy consumption minimization. Wireless transmission consumes much more of energy. In the clustered network, a few nodes become cluster heads which causes the energetic heterogeneity. Therefore the behavior of the sensor network becomes very unstable. Hence, the need to apply the balancing of energy consumption across all nodes of the heterogeneous network is very important to prevent the death of those nodes and thereafter increase the
lifetime of the network. DEEC (Distributed Energy Efficient Clustering) is one of routing protocols
designed to extend the stability time of the network by reducing energy consumption. A disadvantage of
DEEC, which doesn’t takes into account the cluster size and the density of nodes in this cluster to elect the
cluster heads. When multiple cluster heads are randomly selected within a small area, a big extra energy
loss occurs. The amount of lost energy is approximately proportional to the number of cluster heads in this
area. In this paper, we propose to improve DEEC by a modified energy efficient algorithm for choosing
cluster heads that exclude a number of low energy levels nodes due to their distribution density and their
dimensions area. We show by simulation in MATLAB that the proposed approach increases the number of
received messages and prolong the lifetime of the network compared to DEEC. We conclude by studying
the parameters of heterogeneity that proposed technique provides a longer stability period which increases
by increasing the number of nodes which are excluded from the cluster head selection.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IMPROVEMENTS IN ROUTING ALGORITHMS TO ENHANCE LIFETIME OF WIRELESS SENSOR NET...IJCNCJournal
Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited
computation, communication, memory, and energy resources that are being used fora huge range of
applications. Clustering in WSNs is an effective way to minimize the energy consumption of sensor nodes.
In this paper improvements in various parameters are compared for three different routing algorithms.
First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which is a famed clustering
mechanism that elects a CH based on the probability model. Then, work describes a Fuzzy logic system
initiated CH selection algorithm for LEACH. Then Artificial Bee Colony (ABC)which is an optimisation
protocol owes its inspiration to the exploration behaviour of honey bees. In this study ABC optimization
algorithm is proposed for fuzzy rule selection. Then, the results of the three routing algorithms are
compared with respect to various parameters
COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NE...IJCSIT Journal
In recent years, Wireless Sensor Networks have gained growing attention from both the research community and actual users. As sensor nodes are generally battery-energized devices, so the network lifetime can be widespread to sensible times.
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
Novel approach for hybrid MAC scheme for balanced energy and transmission in ...IJECEIAES
Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
Energy aware clustering protocol (eacp)IJCNCJournal
Energy saving to prolong the network life is an important design issue while developing a new routing
protocol for wireless sensor network. Clustering is a key technique for this and helps in maximizing the
network lifetime and scalability. Most of the routing and data dissemination protocols of WSN assume a
homogeneous network architecture, in which all sensors have the same capabilities in terms of battery
power, communication, sensing, storage, and processing. Recently, there has been an interest in
heterogeneous sensor networks, especially for real deployments. This research paper has proposed a new
energy aware clustering protocol (EACP) for heterogeneous wireless sensor networks. Heterogeneity is
introduced in EACP by using two types of nodes: normal and advanced. In EACP cluster heads for normal
nodes are elected with the help of a probability scheme based on residual and average energy of the
normal nodes. This will ensure that only the high residual normal nodes can become the cluster head in a
round. Advanced nodes use a separate probability based scheme for cluster head election and they will
further act as a gateway for normal cluster heads and transmit their data load to base station when they
are not doing the duty of a cluster head. Finally a sleep state is suggested for some sensor nodes during
cluster formation phase to save network energy. The performance of EACP is compared with SEP and
simulation result shows the better result for stability period, network life and energy saving than SEP.
An Improved Deterministic Energy Efficient Clustering Protocol for Wireless S...IJERA Editor
In recent development, achieving the deployment of nodes, lifetime, fault tolerance, latency, energy efficiency in brief robustness and high reliability have become the prime research goals of wireless sensor network. In recent years many clustering protocols have been suggested on clustering structure based on heterogeneity. We propose improved deterministic energy-efficient clustering protocol for four types of nodes which extend the stability and lifetime of the network in team of first node get dead. Hence, it increases the heterogeneity and energy level of the network. I-DEC performs better than E-SEP, SEP and DEC with more stability and effective messages shows in simulation results.
Genetic-fuzzy based load balanced protocol for WSNsIJECEIAES
Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AN ENHANCED HYBRID ROUTING AND CLUSTERING TECHNIQUE FOR WIRELESS SENSOR NETWORKijwmn
Wireless Sensor Networks (WSN) have extensively deployed in a wide range of applications. However, WSN still faces several limitations in processing capabilities, memory, and power supply of sensor nodes. It is required to extend the lifetime of WSN. Mainly this is achieved by routing protocols choosing the best transmission path in-network with desired power conservation.This cause is developing a generic protocol framework for WSNa big challenge. This work proposed a new routing technique, described as Hybrid Routing-Clustering (HRC) model. This new approach takes advantage of clustering and routing procedures defined in K-Mean clustering and AODV routing, which constituted of three phases. This development aims to achieve enhanced power conservation rate in consequence network lifetime. An extensive evaluation methodology utilized to measure the performance of the proposed model in simulated scenarios.The results categorized in terms of the average amount of packet received and power conservation rate. The Hybrid Routing-Clustering (HRC) model was determined, showed enhanced results regarding both parameters. In the end, they are comparing these results with well-known routing and well-known clustering algorithms.
ENERGY EFFICIENT AGGREGATION WITH DIVERGENT SINK PLACEMENT FOR WIRELESS SENSO...ijasuc
In WSN the data aggregation is a means for condensing the energy requirement by reducing number of
transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A DYNAMIC ROUTE DISCOVERY SCHEME FOR HETEROGENEOUS WIRELESS SENSOR NETWORKS B...csandit
With the development of new networking paradigms and wireless protocols, nodes with different capabilities are used to form a heterogeneous network. The performance of this kind of networks is seriously deteriorated because of the bottlenecks inside the network. In addition, because of the application requirements, different routing schemes are required toward one particular application. This needs a tool to design protocols to avoid the bottlenecked nodes and adaptable to application requirement. Polychromatic sets theory has the ability to do so. This paper demonstrates the applications of polychromatic sets theory in route discovery and protocols design for heterogeneous networks. From extensive simulations, it shows the nodes with high priority are selected for routing, which greatly increases the performance of the network. This demonstrates that a new type of graph theory could be applied to solve problems of complex networks.
Wireless sensor networks, clustering, Energy efficient protocols, Particles S...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality.
The most important issue in this type of networks is energy constraints. In this area several researches have
been done from which clustering is one of the most effective solutions. The goal of clustering is to divide
network into sections each of which has a cluster head (CH). The task of cluster heads collection, data
aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach
for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal
fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual
energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation
results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF,
PSO-MV) in terms of network lifetime and energy consumption.
NEW APPROACH TO IMPROVING LIFETIME IN HETEROGENEOUS WIRELESS SENSOR NETWORKS ...chokrio
The major challenge for wireless sensor networks is energy consumption minimization. Wireless transmission consumes much more of energy. In the clustered network, a few nodes become cluster heads which causes the energetic heterogeneity. Therefore the behavior of the sensor network becomes very unstable. Hence, the need to apply the balancing of energy consumption across all nodes of the heterogeneous network is very important to prevent the death of those nodes and thereafter increase the
lifetime of the network. DEEC (Distributed Energy Efficient Clustering) is one of routing protocols
designed to extend the stability time of the network by reducing energy consumption. A disadvantage of
DEEC, which doesn’t takes into account the cluster size and the density of nodes in this cluster to elect the
cluster heads. When multiple cluster heads are randomly selected within a small area, a big extra energy
loss occurs. The amount of lost energy is approximately proportional to the number of cluster heads in this
area. In this paper, we propose to improve DEEC by a modified energy efficient algorithm for choosing
cluster heads that exclude a number of low energy levels nodes due to their distribution density and their
dimensions area. We show by simulation in MATLAB that the proposed approach increases the number of
received messages and prolong the lifetime of the network compared to DEEC. We conclude by studying
the parameters of heterogeneity that proposed technique provides a longer stability period which increases
by increasing the number of nodes which are excluded from the cluster head selection.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IMPROVEMENTS IN ROUTING ALGORITHMS TO ENHANCE LIFETIME OF WIRELESS SENSOR NET...IJCNCJournal
Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited
computation, communication, memory, and energy resources that are being used fora huge range of
applications. Clustering in WSNs is an effective way to minimize the energy consumption of sensor nodes.
In this paper improvements in various parameters are compared for three different routing algorithms.
First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which is a famed clustering
mechanism that elects a CH based on the probability model. Then, work describes a Fuzzy logic system
initiated CH selection algorithm for LEACH. Then Artificial Bee Colony (ABC)which is an optimisation
protocol owes its inspiration to the exploration behaviour of honey bees. In this study ABC optimization
algorithm is proposed for fuzzy rule selection. Then, the results of the three routing algorithms are
compared with respect to various parameters
COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NE...IJCSIT Journal
In recent years, Wireless Sensor Networks have gained growing attention from both the research community and actual users. As sensor nodes are generally battery-energized devices, so the network lifetime can be widespread to sensible times.
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
Novel approach for hybrid MAC scheme for balanced energy and transmission in ...IJECEIAES
Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
Energy aware clustering protocol (eacp)IJCNCJournal
Energy saving to prolong the network life is an important design issue while developing a new routing
protocol for wireless sensor network. Clustering is a key technique for this and helps in maximizing the
network lifetime and scalability. Most of the routing and data dissemination protocols of WSN assume a
homogeneous network architecture, in which all sensors have the same capabilities in terms of battery
power, communication, sensing, storage, and processing. Recently, there has been an interest in
heterogeneous sensor networks, especially for real deployments. This research paper has proposed a new
energy aware clustering protocol (EACP) for heterogeneous wireless sensor networks. Heterogeneity is
introduced in EACP by using two types of nodes: normal and advanced. In EACP cluster heads for normal
nodes are elected with the help of a probability scheme based on residual and average energy of the
normal nodes. This will ensure that only the high residual normal nodes can become the cluster head in a
round. Advanced nodes use a separate probability based scheme for cluster head election and they will
further act as a gateway for normal cluster heads and transmit their data load to base station when they
are not doing the duty of a cluster head. Finally a sleep state is suggested for some sensor nodes during
cluster formation phase to save network energy. The performance of EACP is compared with SEP and
simulation result shows the better result for stability period, network life and energy saving than SEP.
An Improved Deterministic Energy Efficient Clustering Protocol for Wireless S...IJERA Editor
In recent development, achieving the deployment of nodes, lifetime, fault tolerance, latency, energy efficiency in brief robustness and high reliability have become the prime research goals of wireless sensor network. In recent years many clustering protocols have been suggested on clustering structure based on heterogeneity. We propose improved deterministic energy-efficient clustering protocol for four types of nodes which extend the stability and lifetime of the network in team of first node get dead. Hence, it increases the heterogeneity and energy level of the network. I-DEC performs better than E-SEP, SEP and DEC with more stability and effective messages shows in simulation results.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers reduces the energy consumption and increases the throughput of the wireless sensor networks.
CBHRP: A Cluster Based Routing Protocol for Wireless Sensor NetworkCSEIJJournal
A new two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol
(CBHRP) is proposed in this paper. It is an extension of LEACH routing protocol. We introduce cluster
head-set idea for cluster-based routing where several clusters are formed with the deployed sensors to
collect information from target field. On rotation basis, a head-set member receives data from the neighbor
nodes and transmits the aggregated results to the distance base station. This protocol reduces energy
consumption quite significantly and prolongs the life time of sensor network. It is found that CBHRP
performs better than other well accepted hierarchical routing protocols like LEACH in term of energy
consumption and time requirement.
ENERGY EFFICIENT DATA COMMUNICATION APPROACH IN WIRELESS SENSOR NETWORKSijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor node creates challenges for routing in WSNs. The research work presents the organized and detailed description of energy conservation method for WSNs. In the proposed method reclustering and multihop data transmission processes are utilized for data reporting to base station by sensor node. The accurate use of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for longer times inside the cluster. The result shows that the proposed clustering approach has higher stable region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy Adaptive Clustering Hierarchy (LEACH) for WSNs.
Designing an Energy Efficient Clustering in Heterogeneous Wireless Sensor Net...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical issue that degrades the network performance. Recharging and providing security to the sensor devices is very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an important and suitable approach to increase energy efficiency and transmitting secured data which in turn enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC) works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all the cluster heads are formed at a time and selected on rotation based on considering the highest energy of the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access (CSMA), a contention window based protocol is used at the MAC layer for collision detection and to provide channel access prioritization to HWSN of different traffic classes with reduction in End to End delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the cluster head for transmission without depleting the energy. Simulation parameters of the proposed system such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing system.
DESIGNING AN ENERGY EFFICIENT CLUSTERING IN HETEROGENEOUS WIRELESS SENSOR NET...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical
issue that degrades the network performance. Recharging and providing security to the sensor devices is
very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an
important and suitable approach to increase energy efficiency and transmitting secured data which in turn
enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC)
works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the
rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the
network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on
the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all
the cluster heads are formed at a time and selected on rotation based on considering the highest energy of
the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum
flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access
(CSMA), a contention window based protocol is used at the MAC layer for collision detection and to
provide channel access prioritization to HWSN of different traffic classes with reduction in End to End
delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the
cluster head for transmission without depleting the energy. Simulation parameters of the proposed system
such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing
system.
A New Method for Reducing Energy Consumption in Wireless Sensor Networks usin...Editor IJCATR
Nowadays, wireless sensor networks, clustering protocol based on the neighboring nodes into separate clusters and fault
tolerance for each cluster exists for sensors to send information to the base station, to gain the best performance in terms of increased
longevity and maintain tolerance than with other routing methods. However, most clustering protocols proposed so far, only
geographical proximity (neighboring) cluster formation is considered as a parameter. In this study, a new clustering protocol and fault
tolerance based on the fuzzy algorithms are able to clustering nodes in sensor networks based on fuzzy logic and fault tolerance. This
protocol uses clustering sensor nodes and fault tolerance exist in the network to reduce energy consumption, so that faulty sensors
from neighboring nodes are used to cover the errors, work based on the most criteria overlay neighbor sensors with defective sensors,
distance neighbor sensors from fault sensor and distance neighbor sensors from central station is done. Superior performance of the
protocol can be seen in terms of increasing the network lifetime and maintain the best network tolerance in comparison with previous
protocols such as LEACH in the simulation results.
Mobile Agents based Energy Efficient Routing for Wireless Sensor NetworksEswar Publications
Energy Efficiency and prolonged network lifetime are few of the major concern areas. Energy consumption rated of sensor nodes can be reduced in various ways. Data aggregation, result sharing and filtration of aggregated data among sensor nodes deployed in the unattended regions have been few of the most researched areas in the field of wireless sensor networks. While data aggregation is concerned with minimizing the information transfer from source to sink to reduce network traffic and removing congestion in network, result sharing focuses on sharing of information among agents pertinent to the tasks at hand and filtration of aggregated data so as to remove redundant information. There exist various algorithms for data aggregation and filtration using different mobile agents. In this proposed work same mobile agent is used to perform both tasks data aggregation and data filtration. This approach advocates the sharing of resources and reducing the energy consumption level of sensor nodes.
An Integrated Distributed Clustering Algorithm for Large Scale WSN...................................................1
S. R. Boselin Prabhu, S. Sophia, S. Arthi and K. Vetriselvi
An Efficient Connection between Statistical Software and Database Management System ................... 1
Sunghae Jun
Pragmatic Approach to Component Based Software Metrics Based on Static Methods ......................... 1
S. Sagayaraj and M. Poovizhi
SDI System with Scalable Filtering of XML Documents for Mobile Clients ............................................... 1
Yi Yi Myint and Hninn Aye Thant
An Easy yet Effective Method for Detecting Spatial Domain LSB Steganography .................................... 1
Minati Mishra and Flt. Lt. Dr. M. C. Adhikary
Minimizing the Time of Detection of Large (Probably) Prime Numbers ................................................... 1
Dragan Vidakovic, Dusko Parezanovic and Zoran Vucetic
Design of ATL Rules for TransformingUML 2 Sequence Diagrams into Petri Nets..................................... 1
Elkamel Merah, Nabil Messaoudi, Dalal Bardou and Allaoua Chaoui
Energy Consumption Reduction in Wireless Sensor Network Based on ClusteringIJCNCJournal
ABSTRACT
One of the important issues in the routing protocol design in Wireless Sensor Networks (WSNs) is minimizing energy consumption and maximizing network lift time. Nowadays networks and information systems are one of the main parts of modern life that without them, people cannot live. On the hand, the impairment of these networks leads to great and incalculable costs. In this paper, a new method based on clustering has presented that problem of energy consumption is solved. The proposed algorithm is that energy-based clustering can create clusters of the same energy level and distribute energy efficiency across the WNS nodes. This proposed clustering protocol classify network nodes based on energy and neighbourhood criteria and attempts to better balance energy in clusters and ultimately increase network lifetime and maintain network coverage. Results are shown that the proposed algorithm is on average 40% better than LEACH algorithm and 14% better than IBLEACH algorithm.
KEYWORDS
Wireless Sensor Network, Clustering, LEACH Algorithm, IBLEACH Algorithm
Abstract Link : http://aircconline.com/abstract/ijcnc/v11n2/11219cnc03.html
Full Details : http://aircconline.com/ijcnc/V11N2/11219cnc03.pdf
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
In this paper, snake optimization algorithm (SOA) is used to find the optimal gains of an enhanced controller for controlling congestion problem in computer networks. M-file and Simulink platform is adopted to evaluate the response of the active queue management (AQM) system, a comparison with two classical controllers is done, all tuned gains of controllers are obtained using SOA method and the fitness function chose to monitor the system performance is the integral time absolute error (ITAE). Transient analysis and robust analysis is used to show the proposed controller performance, two robustness tests are applied to the AQM system, one is done by varying the size of queue value in different period and the other test is done by changing the number of transmission control protocol (TCP) sessions with a value of ± 20% from its original value. The simulation results reflect a stable and robust behavior and best performance is appeared clearly to achieve the desired queue size without any noise or any transmission problems.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
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An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm
1. TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 6, December 2020, pp. 2822~2833
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i6.15199 2822
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
An energy-efficient cluster head selection in wireless sensor
network using grey wolf optimization algorithm
Kaushik Sekaran1
, R. Rajakumar2
, K. Dinesh3
, Y. Rajkumar4
, T. P. Latchoumi5
,
Seifedine Kadry6
, Sangsoon Lim7
1
Department of Computer Science and Engineering, Vignan Institute of Technology & Science, India
2,4,5
Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology & Research, India
3
School of Computing and Science and Engineering, Vellore Institute of Technology, India
6
Department of mathematics and computer science, Faculty of Science, Beirut Arab University, Lebanon
7
Department of Computer Engineering, Sungkyul University, South Korea
Article Info ABSTRACT
Article history:
Received Jan 7, 2020
Revised Mar 22, 2020
Accepted Jun 25, 2020
Clustering is considered as one of the most prominent solutions to preserve the
energy in the wireless sensor networks. However, for optimal clustering, an
energy efficient cluster head selection is quite important. Improper selection
of cluster heads (CHs) consumes high energy compared to other sensor nodes
due to the transmission of data packets between the cluster members and the
sink node. Thereby, it reduces the network lifetime and performance of the
network. In order to overcome the issues, we propose a novel cluster head
selection approach using grey wolf optimization algorithm (GWO) namely
GWO-CH which considers the residual energy, intra-cluster and sink distance.
In addition to that, we formulated an objective function and weight parameters
for an efficient cluster head selection and cluster formation. The proposed
algorithm is tested in different wireless sensor network scenarios by varying
the number of sensor nodes and cluster heads. The observed results convey
that the proposed algorithm outperforms in terms of achieving better network
performance compare to other algorithms.
Keywords:
Cluster head
Grey wolf optimization
Network lifetime
Residual energy
Wireless sensor network
This is an open access article under the CC BY-SA license.
Corresponding Author:
Sangsoon Lim,
Department of Computer Engineering,
Sungkyul University,
Anyang, South Korea.
Email: lssgood80@gmail.com
1. INTRODUCTION
Wireless sensor networks can be expounded as a collation of crammed dissipation of ad-hoc sensor
nodes that acts as a watchdog which provides contiguous information about its surroundings which are coagulated
in a central processing node called sink. Due to its compactness and low value, it has been predominantly used
across different kinds of monopoly such as military, health, education, design and engineering sectors. It has
grabbed its attention because of the applications that cater to diverse variants have been discovered. In a packed
environment of nodes, routing poses a hefty concern. It is obvious since nodes are optimal in size, energy is also
an argument where lots of research has been concerted. Since the nodes are battery-powered devices which are
deployed in a downtrodden area, it is not possible to reconstitute back which poses a limitation in case of a vast
set up of wireless sensor networks [1-3]. One of the most poignant stipulations in the deployment of nodes in
wireless sensor network (WSN) is to exercise the energy that is stored in the nodes. Countering to this need, many
2. TELKOMNIKA Telecommun Comput El Control
An energy-efficient cluster head selection in wireless sensor network using… (Kaushik Sekaran)
2823
protocols and schemes have been evolved. Since routing mainly confides on battery power, clustering captures
its attention among the researches due to its efficiency during information exchange. Clustering can be defined as
a grouping of nodes based on parameters such as proximity, range, power, and location, [4-6]. Cluster-based
sensors aids to utilize the resources efficiently in wireless sensor networks. Clustering facilitates the cluster
members to transmit data only to cluster heads (CHs) and then the CHs transmits the collected data to the base
station and thereby reducing the energy consumption and minimizing the routing overhead as shown in Figure 1.
However, the communication cost of data is higher than the processing; therefore, clustering the sensors will be
beneficial. The central processing unit is mainly responsible for the intimating the common mob about the
happenings that have been captured from the down-trodden environment. Clustering provides many leverages
which include; a) ease of deployment; b) wide area coverage; c) fault tolerance; and d) energy conservation.
During the dissipation of information from one node to the other, several nodes may contain the same redundant
information resulting in huge energy consumption. However, the selection of cluster heads poses a problem
against the lifetime of the network [7, 8].
Figure 1. Working flow of cluster head and base station (BS) in wireless sensor network (WSN)
Grey wolf optimization algorithm is family of the swarm intelligence techniques which is inspired by
the behaviour of grey wolf (i.e. leadership and hunting strategies). This algorithm has been utilized by different
domains researchers to solve their domain related problems due to its simplicity and ease of implementations.
Grey wolf optimization (GWO) algorithm has few parameters to solve the non-deterministic polynomial
(NP)-hard problems within the course of iterations. This algorithm is used to solve different domain problems
such as Localization in WSN [9], economic load dispatch problem [10], feature selection [11], engineering
problems [12], unit commitment problems [13] and so on. Clustering in WSN is considered an NP-hard problem
which can be solved using an efficient optimization algorithm. In this paper, we proposed an optimal cluster head
selection mechanism based on grey wolf optimization algorithm namely GWO-CH. This algorithm considers
the residual energy, intra-cluster and Sink distance to select the optimal cluster heads. In addition to that, we
introduced an objective function which includes the essential parameters to select the optimum. In GWO
algorithm, we incorporated the efficient search agent representation scheme to represent the energy efficient
cluster head selection. On the other hand, we proposed a weight parameter for cluster formation. This parameter
guides the sensor nodes to join their respective cluster head groups. The sensor node with high weight will be
moved to the corresponding clusters. Thereby, that sensor will act as cluster members under the CHs and transmits
their information to the base station through the CHs. The experimentation of the proposed algorithm is tested in
the different scenarios of sensor nodes by varying the number of sensor nodes and the CHs. To analyze the efficacy
of the proposed work is compared with the other algorithms namely end-to-end secure low energy adaptive
clustering hierarchy (E-LEACH) [14], genetic algorithms (GA) [15, 16], cuckoo search (CS) [17], particle swarm
3. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 6, December 2020: 2822 - 2833
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optimization-C (PSO-C) [18], and fruit fly optimization algorithm (FFOA) [19]. Our contributions in this paper
are described as follows:
− Proposed cluster head selection using grey wolf optimization with energy efficient parameters.
− Proposed an objective function and weight parameters to select the optimal CHs and efficient cluster formation.
− Tested proposed work with various WSN scenarios and efficacy compared with other algorithms.
The rest of the paper formulated as follows. Section 2 deals with the literature study of the existing
mechanism to select the cluster heads. Section 3 discussed the preliminaries of GWO algorithm and energy
consumption models. The proposed methodologies with its formulated objective function and weight parameter
for cluster formation presented in section 4. The experimentation results are discussed in section 5 and finally,
conclusion and future works are provided in section 6.
2. LITTERATURE REVIEW
Vast research has been plunged in the area of wireless sensor networks in order to perpetuate the lifetime
of the network. Since the selection of cluster heads is an NP-hard problem each algorithm has its own flaws as well.
Algorithms devised for increasing the longevity of the network can be broadly categorized into 1) heuristic and
2) meta-heuristic approaches. Elaborations of these approaches are as follows:
2.1. Heuristic-based clustering algorithm
Since diverse algorithms catering to different needs are there, low-energy adaptive clustering
(LEACH) [20] is of the predominant clustering algorithm which elects the cluster head with some feasibility.
It provides aggregation of the crammed data thus reducing the unwanted traffic and energy consumption of
the network [21], thereby increasing the longevity of the network. However, it does not provide any adequate
information about the number of cluster heads in a network. Sometimes it may opt a node with low energy as
a cluster head thereby shortening the lifetime of the network. Other most popular algorithms include
power-efficient gathering in sensor information systems (PEGASIS) and hybrid energy-efficient distributed
(HEED). PEGASIS [22] is an addendum to that of LEACH protocol. It is more advantageous in the sense
because it aggregates all the data and sends it to the central processing unit. However, it introduces an additional
lag if nodes are distant. It is unsuitable for large scale WSNs which involves multi-hop communication. HEED
[23] is also an extension of the LEACH; it suffers from serious communication overhead between a cluster
head and a base station. In the case of E-LEACH [14], the cluster head communication between different
clusters is highly efficient, but in the case of larger networks, it fails to select the nodes with low energy.
TL-LEACH [24] increases the lifetime of the network, but it wastes the energy while performing
communication between cluster heads and the other nodes. M-LEACH carries an advantage by considering
mobility in a routing protocol. It assumes that all the nodes are congruent, and it does not care about the
formation of the cluster while clustering. B-LEACH [25] is another extension where the communication is
entirely depending upon the position of the cluster heads which needs no information about all the other nodes
inside the cluster. Therefore, the residual energy of the CHs gets drained which further reduces the lifetime of
the network. LEACH-C [26, 27] outperforms LEACH-A, LEACH-B, and MTE because the central processing
unit takes care of the location and the energy of all the nodes in the network, hence cluster formation and cluster
maintenance will not get affected. The only disadvantage is that it is not vigorous. E-LEACH is much energy
efficient in case of multi-hop communication. It enhances the cluster head selection process by considering the
higher residual energy available at a particular time within a cluster. Though V-LEACH [28] has been proposed
as an alternative to LEACH, it elects additional CHs to that of main CHs in order to mitigate the failure of the
main CHs. Hence whenever the main CHs, fails the additional CHs selected takes care of its position and
perform the flooding. The algorithm suffers from deprivation that it does not bother about the cluster formation
process.
2.2. Meta-heuristic approaches
Meta-Heuristic algorithms act as the most promising approach for NP-hard combinatorial problems.
Since they mimic from nature, it concentrates mainly on the aspirant which has a high survival rate.
Meta-heuristic algorithms are broadly categorized into two types namely evolutionary and swarm intelligence
approaches [29]. Genetic algorithm [15, 30] and simulated annealing are the most popular evolutionary
algorithms. Some of the swarm intelligence approaches are ant colony optimization (ACO), fish colony
optimization (FCO), bird flocking behaviour, particle swarm optimization (PSO), firefly algorithm (FA) [31],
bat algorithm (BA), cuckoo search (CS), artificial bee colony optimization (ABC), fish swarm optimization
(FSO), glow-worm swarm optimization (GSO), grey wolf optimizer (GWO), fruit fly optimization algorithm
(FFOA). Sweta Potthuri et al. [32] proposed a hybrid differential evolution and simulated annealing (DESA)
algorithm which aims to increase the liveliness of the network by selecting the cluster heads which has optimal
4. TELKOMNIKA Telecommun Comput El Control
An energy-efficient cluster head selection in wireless sensor network using… (Kaushik Sekaran)
2825
energy, thereby preventing the energy loss. The author in [15] proposed an energy efficient clustering algorithm
in order to extend the lifetime of the network. It uses a genetic algorithm (GA), where the cluster heads are
elected by using appropriate fitness function until the information is propagated through to the central
processing unit i.e. base station. Osama Helmy et al. proposed an algorithm that provides energy consumption
thereby increasing the longevity of the network. The different approaches such as preying, and swarming are
employed in order to achieve the selection of the optimal cluster head. The method offers a wide range of
coverage leveraging a better lifetime for the nodes as well as the network and it proves its efficiency even after
increasing the number of clusters compared with LEACH and PSO approach [8].
Sariga et al. [33] proposed a meta-heuristic ACO based unequal clustering (MHACO-UC) algorithm that
concentrates mainly on preserving the lifetime of the CHs, by using a distance estimation function. It also keeps
knowledge about the nearness of the nodes present in the clusters and in the entire network thereby propagating
the information to the central processing unit and this increases the longevity of the lifetime of the network. Tauseef
Ahmad et al. [34] proposed an algorithm that concentrates mainly on selecting the cluster head that has the optimum
energy using bee colony optimization algorithm. The author provides a significant contribution in identifying
the proximities of the nodes inside the cluster and between the cluster heads using an optimized fitness function.
Amit Sarkar et al. [35] utilized the firefly algorithm for increasing the lifetime of the network and the liveliness of
the nodes by electing optimal cluster heads. Cyclic randomization is employed which outperforms the traditional
cluster head selection algorithms respectively. Srinivasa Rao et al. [8] came up with a solution based on particle
swarm optimization approach to address the issues such as energy and clustering. It employs a geometric method to
elect a cluster head and as flooding occurs, the higher energy nodes are only used and the nodes with lower energy
are preserved from propagating the information to the central processing unit thereby preserving the lifetime of
the network. Kia et al. [26] a new hybrid protocol based on cuckoo search optimization have been proposed in order
to conserve energy while flooding the information inside the clusters by selecting the optimal cluster heads.
It employs an energy conservator in order to increase the longevity of the network. Dattatraya et al. [19] proposed
a hybrid algorithm by combining glom worm swarm optimization (GSO) and fruit fly optimization (FFOA). GSO
suffers from low computational speed and low searching capacity. Fruit fly optimization algorithm (FFOA) has its
own merging rate. Hence hybridizing both yields perfect results thereby outperforming the traditional cluster head
selection algorithms.
3. METHODS
3.1. Grey wolf optimization
Grey wolf optimization [28] is a recently proposed swarm intelligence algorithm which mimics
the intelligent behavior of grey wolves which includes leadership and hunting characteristics of the grey wolf.
Grey wolf works in a pack of 5-12 members which follows a very strict social hierarchy. Grey wolf pack
consists of four level hierarchy namely alpha, beta, delta, and omega. Alpha is the first level in the hierarchy
which is considered as the first leader of the pack. It is responsible for all the decision making a process like
hunting the prey, approaching the prey and instructing the wolves in the entire pack. The second level in
the hierarchy is beta, which guides the alpha in decision making and also acts as alpha whenever the alpha is
passed away. In most cases, beta is also called as subordinate wolves. Delta is the third level in the hierarchy
which also known as caretaker and finally. Omega is the last level in the hierarchy which obeys the decision
of the three above leaders and also maintains the safety and integrity in the wolf pack. GWO working process
is mathematically modelled as follows:
3.1.1. Encircling process
All the grey wolves in the pack start encircling the prey before it starts the hunting process.
The encircling process is mathematically formulated and it is given in the (1) and (2).
𝐷⃗⃗ = |𝐶. 𝑋 𝑝
⃗⃗⃗⃗ (𝑘) − 𝑋(𝑘)| (1)
𝑋(𝑘 + 1) = |𝑋 𝑝
⃗⃗⃗⃗ (𝑘) − 𝐴. 𝐷⃗⃗ | (2)
where, 𝐷⃗⃗ represents the distance between the prey and wolf, 𝑋 determines the current position of the wolf in 𝑘
iterations and 𝑋 𝑝
⃗⃗⃗⃗ is the position of the prey. The constant parameters 𝐴 and 𝐶 are measured using the (3) and (4).
𝐴 = 2𝑎. 𝑟𝑎𝑛𝑑1
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ − 𝑎 (3)
𝐶 = 2. 𝑟𝑎𝑛𝑑2
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ (4)
5. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 6, December 2020: 2822 - 2833
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where, 𝑟𝑎𝑛𝑑1
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ and 𝑟𝑎𝑛𝑑1
⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ determines the arbitrary vectors generated between the range of [0,1]. These values
aids to adjust the position of the grey wolf randomly at any position towards the prey. Parameter 𝑎 is aids to
control the movement of the algorithm which linearly reduces from 2 to 0 over certain generations.
3.1.2. Hunting process
In the hunting process, all the dominant wolves 𝜔 adjust their positions using non-dominant wolves
𝛼, 𝛽, and 𝛿. The position update using these non-dominant wolves have mathematically modelled and it is
given in (5-7).
𝐷 𝛼
⃗⃗⃗⃗⃗ = |𝐶1
⃗⃗⃗⃗ . 𝑋 𝛼
⃗⃗⃗⃗ − 𝑋|, 𝐷𝛽
⃗⃗⃗⃗ = |𝐶2
⃗⃗⃗⃗ . 𝑋𝛽
⃗⃗⃗⃗ − 𝑋|, 𝐷𝛿
⃗⃗⃗⃗ = |𝐶3
⃗⃗⃗⃗ . 𝑋 𝛿
⃗⃗⃗⃗ − 𝑋| (5)
𝑋1
⃗⃗⃗⃗ = |𝑋 𝛼
⃗⃗⃗⃗ − 𝐴1
⃗⃗⃗⃗ . 𝐷 𝛼
⃗⃗⃗⃗⃗ |, 𝑋2
⃗⃗⃗⃗ = |𝑋𝛽
⃗⃗⃗⃗ − 𝐴2
⃗⃗⃗⃗ . 𝐷𝛽
⃗⃗⃗⃗ |, 𝑋3
⃗⃗⃗⃗ = |𝑋𝛽
⃗⃗⃗⃗ − 𝐴3
⃗⃗⃗⃗ . 𝐷𝛽
⃗⃗⃗⃗ | (6)
Using in (5) and (6) are used to update the position of the grey wolf and it is shown in (7).
𝑋(𝑘 + 1) = 0.33 ∗ ∑ 𝑋𝑖
⃗⃗⃗3
𝑖=1 (7)
The position update using alpha, beta and delta are graphically represented in Figure 2.
Figure 2. Position update in GWO
3.1.3. Seeking and attacking the prey
The parameter 𝐴 is a random vector which is used to explore and exploit the search position of
the grey wolves. Every course of iterations, this parameter has been adjusted in the range of[−𝑎, 𝑎], where
the value 𝑎 linearly decreases from 2 to 0. GWO algorithm exploits the prey if |𝐴| < 1 otherwise it seeks for
new prey if |𝐴| > 1. In addition to that, parameter 𝐶 lies in the range of [0, 2] which aids the algorithm to avoid
the local optima stagnation by providing some random weight to the position update. However, tuning
the parameter 𝑎 provides better results compare to the generic GWO algorithm. In this proposed work, we
tuned the parameter 𝑎 for better results. The working flow of the GWO algorithm is mathematically modelled
and it is shown in algorithm 1.
Algorithm 1: Grey wolf optimization algorithm
Input – Initialize the population size of the wolves 𝑋𝑖 = (1,2,3, … 𝑛) and parameters A, C
Step 1: Randomly generate solutions 𝑋𝑖 within the boundary regions
Step 2: Evaluate the fitness of the wolves 𝑓𝑖
Step 3: Select the first best solution as Alpha, second best as beta, third best as Delta
and rest as Omega.
Step 4: Update the position of the grey wolves and its parameters
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Step 5: Evaluate the new fitness of all wolves
Step 6: Update the alpha, beta, and delta
Step 7: Repeat step 5 to 7 until condition satisfies
Output – visualize the Alpha wolf
3.2. Energy consumption model
In this paper, the energy consumption model is used based on the suggestions of the author in [36].
In this model, the energy has been utilized by the transmitter and receiver for transmitting and receiving their
signals and to operate the radio amplifiers. The energy consumption of a sensor for transmitting (𝐸 𝑇𝑋) n-bit of
information is mathematically represented in (8).
𝐸 𝑇𝑋(𝑛, 𝜃) = {
𝑛 × 𝐸𝑒𝑙𝑒𝑐 + 𝑛 × 𝜀𝑓𝑠 × 𝜃2
𝑛 × 𝐸𝑒𝑙𝑒𝑐 + 𝑛 × 𝜀 𝑚𝑝 × 𝜃4
𝑖𝑓 𝜃 < 𝜑
𝑖𝑓 𝜃 ≥ 𝜑
(8)
where, 𝐸𝑒𝑙𝑒𝑐 represented as energy utilized per bit to operate the transmitter or receiver. 𝜀𝑓𝑠 and 𝜀 𝑚𝑝determined
as the free space model and multipath of amplification power. 𝜑 and 𝜃 denoted as the threshold and distance
for transmitting the information from one sensor location to other sensors.
At the same time, energy consumed by the receiver for receiving n-bit of information (𝐸 𝑅𝑋(𝑛)) is
computed as follows;
𝐸 𝑅𝑋(𝑛) = 𝑛 × 𝐸𝑒𝑙𝑒𝑐 (9)
The total energy consumption (𝐸𝑡𝑜𝑡𝑎𝑙) of a sensor node for transmitting and receiving the 𝑛-bit information is
mathematically calculated as follows;
𝐸 𝑇𝑜𝑡𝑎𝑙 = 𝐸 𝑇𝑋(𝑛, 𝜃) + 𝐸 𝑅𝑋(𝑛) (10)
A sensor node lifetime is computed based on the initial energy of the node and the remaining energy of the
node after transmitting and receiving the 𝑛-bit information. It is expressed as follows;
𝐿 =
𝐸 𝑖𝑛𝑡𝑖𝑎𝑙
𝐸 𝑡𝑜𝑡𝑎𝑙
(11)
where, 𝐸𝑖𝑛𝑡𝑖𝑎𝑙 represents initial energy of the sensor node (i.e. 2J in our work) and 𝐸𝑡𝑜𝑡𝑎𝑙 represented as
the total consumed the energy of the sensor node. In our work, the network lifetime considered based on
the number of iterations until the last node of death.
4. PROPOSED ALGORITHM
The proposed algorithm mainly contributes to selecting the cluster heads by considering the residual
energy and distance measurement of the sensor nodes. Initially, all the sensor nodes send their information
(node_id, residual energy, location) to the base station. Our proposed GWO algorithm executed at the base
station to select the optimal CH (i.e. by sensor node information) and to form the optimal clusters. In order to
process the cluster formation, we have formulated the weight function which involves the intra-cluster distance
information, residual energy, and neighborhood ratio of CHs respectively.
4.1. The objective function for CH selection
In this work, we derived the objective function which utilizes the intra-cluster distance among
the sensors and the distance from the target node. Let us assume 𝑓1 be a function of mean intra-cluster and
the target distance of the CHs. In order to select the optimal CHs, the 𝑓1 to be minimized. Let us assume 𝑓2 be
the function which is inverse of total current energy of all the selected CHs. In order to provide better results
both the objective function is to be minimized and it to be within (𝑓1, 𝑓2) ∈ [0,1].
The objective function 𝑓1 is represented as;
min 𝑓1 = ∑
1
𝑛 𝑖
𝑚
𝑖=1 (∑ 𝜃(𝑇𝑗, 𝐶𝐻𝑖) + 𝜃(𝐶𝐻𝑖, 𝐵𝑆)
𝑛 𝑖
𝑖=1 ) (12)
where, 𝜃(𝑇𝑗, 𝐶𝐻𝑖) represented as the distance between the target node 𝑗 to the cluster head 𝑖. 𝜃(𝐶𝐻𝑖, 𝐵𝑆)
denoted as the distance between the cluster head 𝑖 to the base station. 𝑛 and 𝑚 denoted as the number of target
sensor nodes and cluster heads.
The objective function 𝑓2 is mathematically represented as;
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min 𝑓2 =
1
∑ 𝐸 𝐶𝐻 𝑖
𝑚
𝑖=1
(13)
where, 𝐸 𝐶𝐻 𝑖
denoted as the residual energy of the cluster head 𝑖. In order to minimize both the objective
function, we use GWO algorithm to select the optimal CH to linearly decrease the function. The combined
objective function is mathematically represented in (14).
𝐹 = 𝜇 × 𝑓1 + (1 − 𝜇)𝑓2, 0 < 𝜇 < 1 (14)
where 𝜇 is the weight parameter in the range of [0,1]. The search agent with minimal objective value is
considered as the CH.
4.2. Cluster formation
In WSN, selecting the optimal CHs will lead to a proper cluster formation and it aids to prolong
the network lifetime. In this work, we select the CHs using the residual energy, neighborhood ratio and distance
from BS. To create an optimal cluster formation, we formulate weight function which guides the sensor node
to join in their respective CHs. The derived weight function is mathematically represented in the (15).
𝐶𝐻 𝑤(𝑇𝑖, 𝐶𝐻𝑗) = 𝐾 ∗
𝐸 𝑅(𝐶𝐻 𝑗)
𝜃(𝑇 𝑖,𝐶𝐻 𝑗)×𝜃(𝐶𝐻 𝑗,𝐵𝑆)×𝐷𝑁(𝐶𝐻 𝑗)
(15)
where 𝐾 is the constant parameter value (i.e. 𝐾 = 1). 𝐸 𝑅(𝐶𝐻𝑗) represented as the residual energy of the 𝑗 𝑡ℎ
cluster head. 𝜃(𝑇𝑖, 𝐶𝐻𝑗) denoted as the distance between the ith target sensor node (i.e. normal sensor node)
and jth cluster head. 𝜃(𝐶𝐻𝑗, 𝐵𝑆) represented as the distance between 𝑗 𝑡ℎ
cluster head and the base station.
𝐷𝑁(𝐶𝐻𝑗) denoted as the neighborhood ratio of the 𝑗 𝑡ℎ
CH. The 𝑖 𝑡ℎ
sensor node with high weight value can
able to join in a 𝑗 𝑡ℎ
cluster head.
4.3. GWO algorithm for CH selection
In the proposed GWO algorithm, the search agent represented as m dimensional cluster heads with its
position (x-axis, y-axis) and sensor id as shown in Figure 3. Initially, the algorithm selects the random cluster
head with their appropriate locations and it computes the objective value for those cluster heads. Next, it selects
the first best search agent 𝛼, second best search agent 𝛽 and third best search agent 𝛿 and rest of the search
agent as 𝜔. With the aid of three best solutions, the remaining search agents update its position and the new
position represented as the new cluster heads which satisfies the objective function. Later, identify the weight
function to determine the appropriate cluster members to join in their respective CHs. The working flow of
the proposed GWO is presented in Algorithm 2.
Figure 3. Representation of search agent in GWO
Algorithm 2: GWO algorithm for CH selection
Input: Number of sensors 𝑆 = {𝑆1, 𝑆2, … , 𝑆 𝑛}, Population size = 𝑁𝑃
Step 1: Randomly initialize the search agent 𝑋𝑖 ∀𝑖,𝑗, 1 ≤ 𝑖 ≤ 𝑁𝑃, 1 ≤ 𝑗 ≤ 𝐷
𝑃𝑖𝑗(0) = (𝑃𝑋𝑖𝑗(0), 𝑃𝑌𝑖𝑗(0))
Step 2: Calculate the fitness 𝑓(𝑋𝑖)
Step 3: Select 𝛼 = min 𝑓(𝑋𝑖), 𝛽 = min 𝑓(𝑋𝑖−1), 𝛿 = min 𝑓(𝑋𝑖−2)
/* 𝛼- first best solution, 𝛽 second best search agent, 𝛿 – third best search agent */
Step 4: while (𝑡 < 𝑡 𝑚𝑎𝑥) /* 𝑡 𝑚𝑎𝑥 – the maximum number of iterations */
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for 𝑖 = 1: 𝑁𝑝
Update the position of search agent 𝑋𝑖
𝑡
Calculate the fitness 𝑓(𝑋𝑖
𝑡
)
Update 𝛼, 𝛽 and 𝛿
end for
for 𝑖 = 1: 𝑛
calculate 𝜃(𝑃𝑖𝑗(𝑡 + 1), 𝑆 𝑘)
𝑃𝑖𝑗(𝑡 + 1) → {𝑆 𝑘| min(𝜃(𝑃𝑖𝑗(𝑡 + 1), 𝑆 𝑘)), ∀𝑖, 1 ≤ 𝑗 ≤ 𝑁𝑃
end for
end while
Step 5: Repeat Step 4 until it reaches the maximum number of iterations
Output: Visualize the best cluster heads 𝐶𝐻 = {𝐶𝐻1, 𝐶𝐻2,… , 𝐶𝐻 𝑚}
5. EXPERIMENTAL SETUP
5.1. Simulation setup
In this paper, the algorithms were implemented in MATLAB (version 8.5) with configurations of Intel
Core i5 Processor with 8GB RAM in a Windows 10 platform. The parameter settings of the proposed system
are given in Table 1. To analyze the performance of the proposed system, the state-of-art other algorithms such
as E-LEACH, GA, CS, PSO-C, and FFOA algorithms are used respectively. In our work, we considered
the network region as 300x300 m2
, with a varying number of sensors from 400 to 700 and the number of
clusters from 20 to 40. The detailed information about the network considerations is given in Table 1.
Table 1. Network configurations
Parameter Value
Network Field (300, 300) m2
Base Station Position (150-400, 150-400)
Sensor Nodes 400-700
Initial Energy 2J
Number of Cluster Heads 20-40
𝐸𝑒𝑙𝑒𝑐, 𝜀𝑓𝑠, 𝜀 𝑚𝑝 50 nJ/bit, 10 pJ/bit/m2
, 0.0013 pJ/bit/m4
𝑑 𝑚𝑎𝑥, 𝜑 100 m, 30 m
Packet Size, Message Size 4000 bits, 500 bits
To measure the performance of the algorithms, we considered three different cases in WSN with
the varying number of sensors and CHs. Firstly, case#1 deals with the 400 sensor nodes with 20 CHs. Next, case#2
deals with 500 sensor nodes with 30 CHs, case#3 consists of 600 sensor nodes with 30 CHs and finally, case#4 holds
700 sensor nodes with 40 CHs. In addition to that, we have placed the Base station in three different locations namely
mid of the network region (150, 150), corner of the network region (300, 300) and outside of the network region
(400, 400). Owing to the Placement of BS in different locations are used to analyze the performance of packet
delivery information and the network lifetime. Every algorithm has been executed repeatedly for 30 times
and average values of that execution are measured and plotted in the figures. The proposed algorithm has been
tested with different population size and based on the experimentation analysis we fixed the population size as 50.
At the same time, the weighted sum of 𝜇 value is fixed as 0.27 based on the experimentation analysis. This value
provides better performance compared to values from 0 to 1. The detailed parameters information of the GWO
algorithm is given in Table 2.
Table 2. GWO parameters
Parameters Value
No. of Search agents 50
C (2 – 0)
a (0 – 1.5)
𝜇 0.27
Dimension of search agents 20-40 (CHs)
Number of Iterations 100
5.2. Performance analysis
The performance of the proposed algorithm has been measured using three metrics namely total
energy consumption (TEC), network lifetime (NL) and packet received by BS (PR-BS). These three-
performance metrics are used to analyze the performance of the proposed algorithm with other algorithms.
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5.2.1. Performance analysis of TEC
In order to measure the performance of energy consumption, firstly we executed the algorithms by
varying the number of sensor nodes from 400 to 700 and the number of cluster heads from 20 to 50.
The performance measures of E-LEACH, GA, CS, PSO-C, FFOA, and GWO-CH are shown in Tables 3 and
4 and Figures 4-8 in terms of total energy utilization in all the different cases. In the first case, the BS location
was considered as mid of the network region (150, 150). The observed results notify that the proposed
GWO-CH algorithm outperforms better than E-LEACH, GA, CS, PSO-C, FFOA in terms of total energy
consumption respectively. In addition to that, we have noticed that if the sensors are nearest to the CHs,
the energy consumption for transferring packets from one sensor to other is decreased. Because of the proposed
fitness function which concentrates on the energy consumption of the normal nodes by minimizing the distance
between the sensor and CHs.
Table 3. Total energy consumption for 20CHs in case#1 (5000 iterations)
Sensors Nodes = 400 BS (150,150) BS (300,300) BS (400,400)
E-LEACH 800.00 800.00 800.00
GA 786.54 794.74 800.00
CS 782.92 784.96 788.82
PSO-C 764.64 774.82 784.68
FFOA 710.28 724.65 746.87
GWO 646.54 680.38 702.49
Table 4. Total energy consumption for 30CHs in case#2 (5000 iterations)
Sensors Nodes = 500 BS (150,150) BS (300,300) BS (400,400)
E-LEACH 1000.00 1000.00 1000.00
GA 954.87 968.75 986.78
CS 914.15 932.87 954.54
PSO-C 880.54 917.54 942.87
FFOA 864.54 886.40 902.14
GWO 804.51 835.21 856.12
On the other hand, we have noticed that when the network size increases then the performance of
the existing algorithm decreases, which was in Figures 4-8. Initially, the performance of the proposed algorithm is
not that much satisfactory compared to PSO-C and FFOA. As the number of iterations increases, the residual energy
of the sensors is decreasing due to the improper cluster head selections. In this case, our proposed algorithms provide
a better solution in case of selecting the proper cluster heads by our derived fitness function. In order to measure
the energy consumption performance, we executed our algorithm by varying the number of sensors from 400 to 700
and the cluster heads from 20 to 50. For efficient performance analysis, the algorithms are executed for 5000
iterations. The overall energy consumption was measured at the final iterations 5000. Figures 4-8 displays that
the proposed algorithm provides better performance compared to other state-of-art algorithms. The efficacy of
the proposed algorithm has been achieved by the novel derived fitness function which handles in selecting
the appropriate cluster heads by minimizing the distance between the sensors and CHs. Finally, the performance of
the algorithm in terms of energy consumption with varying number of sensors from 400 to 700 and cluster heads
from 20 to 50 with 5000 iterations shown in Tables 3 and 4.
Figure 4. Total energy consumption in case 2
with 30 CHs
Figure 5. Total energy consumption in case 3
with 40 CHs
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(a) (b)
(c)
Figure 6. Total energy consumption by placing BS in different locations; (a) case 1 (b) case 2 (c) case 3
(a) (b)
Figure 7. Comparison of packet received by BS by placing the BS in different locations; (a) case 1 (b) case 2
(a) (b)
Figure 8. Comparison of packet received by BS by placing the BS in different locations; (a) case 3 (b) case 4
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Finally, we specify that the proposed algorithm utilizes the minimum energy consumption and
maximizes the network lifetime achieves better performance in delivering the maximum number of packets.
It is also observed that the proposed algorithm achieves the maximum number of packets received when
compared to other algorithms E-LEACH, GA, CS, PSO-C, and FFOA. In the existing algorithm, when the BS
location is at out of the network region then the number of packets received is less but the proposed algorithm
maximizes the number of packets received in terms of selecting the efficient cluster head using the derived
fitness function.
6. CONCLUSION
In this paper, we introduced a novel cluster head selection algorithm based on GWO using efficient
search agent representation and novel objective function. For the energy efficacy, we have considered
intra-cluster distance, sink distance and the residual energy of sensors respectively. In addition to that, we have
formulated the weighted function for the efficient cluster formation. The experimental results with its
comparison of existing algorithms E-LEACH, GA, CS, PSO-C, and FFOA has been presented. The algorithm
has been executed in the different test cases with a varying number of sensors and CHs. The observed results
convey that the proposed algorithm outperforms better compared to other algorithms in terms of energy
consumption, network lifetime and packet received by the BS. Further, this work can be extended by
formulating novel routing algorithm in the proposed algorithm. Still, we can consider the various issues viz.,
load balancing and fault tolerance in WSN. In this work, we have tested the proposed algorithm in
the homogeneous network. In the future, the same can be tested on heterogeneous networks.
ACKNOWLEDGEMENTS
This work was supported by the National Research Foundation of Korea (NRF) grant funded by
the Korea government (MSIT) (No. NRF-2018R1C1B5038818).
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