An energy-efficient cluster head selection in wireless sensor network using g...TELKOMNIKA JOURNAL
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.
AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CON...IJNSA Journal
Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.
Implementation of energy efficient coverage aware routing protocol for wirele...ijfcstjournal
In recent years, wireless sensor network have been used in many application such as disaster reservation,
agriculture, environmental observation and forecasting .Coverage preservation and energy consumption
are two most important issues in wireless sensor networks. To increase the network lifetime, we propose an
energy efficient coverage aware routing protocol for wireless sensor network for randomly deployed sensor
nodes. Some of the routing protocol is based on energy efficiency and some are based on coverage aware.
The proposed routing protocol is based on both the issues i.e. coverage and energy, in which we first find
the k-mean i.e. the degree of coverage, so that we can use this in the selection of cluster heads in wireless
sensor network by using Genetic Algorithm for increasing network lifetime and coverage. For cluster head
selection each node evaluates its k-mean and energy by internal function which used as fitness function in
genetic algorithm. The proposed algorithm “Implementation of energy efficient coverage aware routing
protocol for Wireless Sensor Network” is designed for homogeneous wireless sensor network. Simulations
results show that proposed algorithm increases the network lifetime by reduce the energy consumption and
preserve coverage. Simulation is done with MATLAB and a comparison of algorithm with benchmark
algorithms is also performed.
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.
An energy-efficient cluster head selection in wireless sensor network using g...TELKOMNIKA JOURNAL
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.
AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CON...IJNSA Journal
Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.
Implementation of energy efficient coverage aware routing protocol for wirele...ijfcstjournal
In recent years, wireless sensor network have been used in many application such as disaster reservation,
agriculture, environmental observation and forecasting .Coverage preservation and energy consumption
are two most important issues in wireless sensor networks. To increase the network lifetime, we propose an
energy efficient coverage aware routing protocol for wireless sensor network for randomly deployed sensor
nodes. Some of the routing protocol is based on energy efficiency and some are based on coverage aware.
The proposed routing protocol is based on both the issues i.e. coverage and energy, in which we first find
the k-mean i.e. the degree of coverage, so that we can use this in the selection of cluster heads in wireless
sensor network by using Genetic Algorithm for increasing network lifetime and coverage. For cluster head
selection each node evaluates its k-mean and energy by internal function which used as fitness function in
genetic algorithm. The proposed algorithm “Implementation of energy efficient coverage aware routing
protocol for Wireless Sensor Network” is designed for homogeneous wireless sensor network. Simulations
results show that proposed algorithm increases the network lifetime by reduce the energy consumption and
preserve coverage. Simulation is done with MATLAB and a comparison of algorithm with benchmark
algorithms is also performed.
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.
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.
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.
Energy efficient clustering in heterogeneousIJCNCJournal
Cluster head election is a key technique used to reduce energy consumption and enhancing the throughput
of wireless sensor networks. In this paper, a new energy efficient clustering (E2C) protocol for
heterogeneous wireless sensor networks is proposed. Cluster head is elected based on the predicted
residual energy of sensors, optimal probability of a sensor to become a cluster head, and its degree of
connectivity as the parameters. The probability threshold to compete for the role of cluster head is derived.
The probability threshold has been extended for multi-levels energy heterogeneity in the network. The
proposed E2C protocol is simulated in MATLAB. Results obtained in the simulationshowthat performance
of the proposed E2Cprotocol is betterthan stable election protocol (SEP), and distributed energy efficient
clustering (DEEC) protocol in terms of energy consumption, throughput, and network lifetime.
Chain Based Wireless Sensor Network Routing Using Hybrid Optimization (HBO An...IJEEE
In Wireless Sensor Network, due to the
energy restriction of each nodes, efficient routing is very
important in order to save the energy of the hybrid
optimization technique. The results of new protocol i.e.
hybrid have been compared with EEPB and IEEPB.
Simulation results show that the lifetime of Hybrid is better
as compared to EEPB and IEEPB.
Efficient Clustering scheme in Cognitive Radio Wireless sensor networkaziznitham
This is the Mtech thesis/dissertation on Efficient clustering scheme in cognitive radio wireless sensor network. This work is done by Mohammad Aziz roll no. 14mi544, CSE department of NIT Hamirpur.
Energy Curtailing with Huddling Practices with Fuzzy in Wireless Sensor Networkijsrd.com
Wireless sensor is a mounting field and energy conservation is always being in the peak challenges. Researchers have gone all the way through architectures and topologies that permit energy proficient operation in wireless sensor network. Clustering being stretchy helps to supplely mould the network according to the needs. Cluster head election and cluster formation is previously investigated by numerous researchers. In this paper, a proposed novel scheme the Fuzzy Abiding Cluster Head Formation Protocol (FACFP) that uses Mamdani’s fuzzy inference system in the process during cluster formation. We demonstrate that using multiple parameters in cluster formation can minimize the usage of energy. We will compare our proposed technique with well-known existing protocols to show that using multi parameter FIS enhances network lifetime and conserves energy utilization.
International Journal of Advanced Smart Sensor Network Systems (IJASSN)ijcseit
The placement of base stations in wireless sensor networks affect the energy consumption for
communication between sensor node and base station. In this paper we analyzed the performance of the
zone based clustering protocol [2] under varying position of base stations, different zone sizes and the
effect on network life time with multiple base stations. While evaluating the communication overhead of
various cluster sizes, we observed that the optimal cluster size for a given network is complex, depending
on a range of parameters. Simulation results show that communication overhead decreases as we increase
the number of zone in the network. We show that placing multiple base stations in place of single base
station in zone based routing protocol enhance the network life time.
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.
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.
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.
CA-MWSN: CLUSTERING ALGORITHM FOR MOBILE WIRELESS SENOR NETWORKFransiskeran
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.
Ameliorate the performance using soft computing approaches in wireless networksIJECEIAES
Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.
A Novel Cluster-Based Energy Efficient Routing With Hybrid Protocol in Wirele...IJERA Editor
In wireless sensor network, lifetime of sensor nodes is the most essential parameters. sensor node's lifetime may be extended using LEACH and HEED scheme which is allowing to move the cluster head surrounded by the sensor nodes try to allocate the energy consumption over all nodes in the network. Energy efficiency is depends on the selection of cluster head. In this paper, we proposed the clustering algorithm to minimize the overhead of control packets by using LEACH and HEED and Efficient utilization of node near sink and to implements the hybrid protocol which would be better than the existing protocol.
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.
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.
OPTIMAL CLUSTERING AND ROUTING FOR WIRELESS SENSOR NETWORK BASED ON CUCKOO SE...ijassn
ABSTRACT
In this research work, the egg laying radius of cuckoo search algorithm is used to create a cluster and then search for the optimum node based on multiobjective genetic algorithm with pareto ranking, so that the data can be forwarded to the sink.The primary focus is onthe two performance metrics parameters,one is the maximization of network lifetime and other is the minimization of delay. For maximizing the network
lifetime parameter, the overlapped target sensing by many sensors is wastage of energy by two or more sensors, where the same task can be done by one sensor. To overcome this problem, the sequence set cover methodology is used.For minimization of delay parameter, the sleep-wake scheduling mechanism will be considered, but substantial delays are introduced as transmitting node needs to wait for its next-hop relay node to wake up. These delays can be taken care by developing any cast based packet forwarding schemes
where individual node forwards a packet to the first neighboring node that wakes up among multiple candidate nodes. This any cast forwarding schemes minimizes the expected packet-delivery delays from the sensor nodes to the sink node. The introduced work will perform energy proficient routing with an objective to improve the network life, packet loss ratio and overall network throughput. The proposed algorithm was
simulated in MATLAB and compared with LEACH algorithm. The results show that our proposed algorithm issuperiorfor prolonging the network lifetime, minimizing the packet loss and increasing the
throughput.
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.
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.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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.
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.
Energy efficient clustering in heterogeneousIJCNCJournal
Cluster head election is a key technique used to reduce energy consumption and enhancing the throughput
of wireless sensor networks. In this paper, a new energy efficient clustering (E2C) protocol for
heterogeneous wireless sensor networks is proposed. Cluster head is elected based on the predicted
residual energy of sensors, optimal probability of a sensor to become a cluster head, and its degree of
connectivity as the parameters. The probability threshold to compete for the role of cluster head is derived.
The probability threshold has been extended for multi-levels energy heterogeneity in the network. The
proposed E2C protocol is simulated in MATLAB. Results obtained in the simulationshowthat performance
of the proposed E2Cprotocol is betterthan stable election protocol (SEP), and distributed energy efficient
clustering (DEEC) protocol in terms of energy consumption, throughput, and network lifetime.
Chain Based Wireless Sensor Network Routing Using Hybrid Optimization (HBO An...IJEEE
In Wireless Sensor Network, due to the
energy restriction of each nodes, efficient routing is very
important in order to save the energy of the hybrid
optimization technique. The results of new protocol i.e.
hybrid have been compared with EEPB and IEEPB.
Simulation results show that the lifetime of Hybrid is better
as compared to EEPB and IEEPB.
Efficient Clustering scheme in Cognitive Radio Wireless sensor networkaziznitham
This is the Mtech thesis/dissertation on Efficient clustering scheme in cognitive radio wireless sensor network. This work is done by Mohammad Aziz roll no. 14mi544, CSE department of NIT Hamirpur.
Energy Curtailing with Huddling Practices with Fuzzy in Wireless Sensor Networkijsrd.com
Wireless sensor is a mounting field and energy conservation is always being in the peak challenges. Researchers have gone all the way through architectures and topologies that permit energy proficient operation in wireless sensor network. Clustering being stretchy helps to supplely mould the network according to the needs. Cluster head election and cluster formation is previously investigated by numerous researchers. In this paper, a proposed novel scheme the Fuzzy Abiding Cluster Head Formation Protocol (FACFP) that uses Mamdani’s fuzzy inference system in the process during cluster formation. We demonstrate that using multiple parameters in cluster formation can minimize the usage of energy. We will compare our proposed technique with well-known existing protocols to show that using multi parameter FIS enhances network lifetime and conserves energy utilization.
International Journal of Advanced Smart Sensor Network Systems (IJASSN)ijcseit
The placement of base stations in wireless sensor networks affect the energy consumption for
communication between sensor node and base station. In this paper we analyzed the performance of the
zone based clustering protocol [2] under varying position of base stations, different zone sizes and the
effect on network life time with multiple base stations. While evaluating the communication overhead of
various cluster sizes, we observed that the optimal cluster size for a given network is complex, depending
on a range of parameters. Simulation results show that communication overhead decreases as we increase
the number of zone in the network. We show that placing multiple base stations in place of single base
station in zone based routing protocol enhance the network life time.
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.
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.
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.
CA-MWSN: CLUSTERING ALGORITHM FOR MOBILE WIRELESS SENOR NETWORKFransiskeran
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.
Ameliorate the performance using soft computing approaches in wireless networksIJECEIAES
Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.
A Novel Cluster-Based Energy Efficient Routing With Hybrid Protocol in Wirele...IJERA Editor
In wireless sensor network, lifetime of sensor nodes is the most essential parameters. sensor node's lifetime may be extended using LEACH and HEED scheme which is allowing to move the cluster head surrounded by the sensor nodes try to allocate the energy consumption over all nodes in the network. Energy efficiency is depends on the selection of cluster head. In this paper, we proposed the clustering algorithm to minimize the overhead of control packets by using LEACH and HEED and Efficient utilization of node near sink and to implements the hybrid protocol which would be better than the existing protocol.
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.
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.
OPTIMAL CLUSTERING AND ROUTING FOR WIRELESS SENSOR NETWORK BASED ON CUCKOO SE...ijassn
ABSTRACT
In this research work, the egg laying radius of cuckoo search algorithm is used to create a cluster and then search for the optimum node based on multiobjective genetic algorithm with pareto ranking, so that the data can be forwarded to the sink.The primary focus is onthe two performance metrics parameters,one is the maximization of network lifetime and other is the minimization of delay. For maximizing the network
lifetime parameter, the overlapped target sensing by many sensors is wastage of energy by two or more sensors, where the same task can be done by one sensor. To overcome this problem, the sequence set cover methodology is used.For minimization of delay parameter, the sleep-wake scheduling mechanism will be considered, but substantial delays are introduced as transmitting node needs to wait for its next-hop relay node to wake up. These delays can be taken care by developing any cast based packet forwarding schemes
where individual node forwards a packet to the first neighboring node that wakes up among multiple candidate nodes. This any cast forwarding schemes minimizes the expected packet-delivery delays from the sensor nodes to the sink node. The introduced work will perform energy proficient routing with an objective to improve the network life, packet loss ratio and overall network throughput. The proposed algorithm was
simulated in MATLAB and compared with LEACH algorithm. The results show that our proposed algorithm issuperiorfor prolonging the network lifetime, minimizing the packet loss and increasing the
throughput.
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.
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protocols such as LEACH in the simulation results.
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An Energy efficient cluster head selection in wireless sensor networks using improved Grey Wolf optimization
1. An energy-efficient cluster head selection in
wireless sensor network using Improved
Grey wolf optimization algorithm
Project Supervisor Name : Dr. Ritu Garg
Submitted By : Nikhil kumar
Roll No : 32013114
Semester : 3rd
MTech, Computer Engineering
National Institute Of Technology , Kurukshetra
Dissertation Topic
1
3. INTRODUCTION
1. Sensors in WSN are small, inexpensive, low-power, intelligent and disposable. The sensor
nodes are self-configuring and contain one or more sensors, integrated with wireless
communication devices and data processing components and a limited energy source.
2. Due to the large number of nodes and the possibly hazardous environment in which these nodes
are deployed, their batteries are often assumed to be nonreplaced.
3. Network lifetime is, therefore, dependent on the lifetime of individual nodes. This raises the
issue of energy-efficient design of the network.
.
3
4. Working flow of cluster head and base station (BS) in wireless sensor network (WSN)
4
5. Problem Description
Wireless sensor networks comprise a large number of small sensor nodes scattered across
limited geographical areas.
The nodes in such networks carry sources of limited and mainly unchangeable energy
clustering is the most prominent solution to preserve the energy in wireless sensor Network
Improper cluster head selection can lead to high energy
Thats why for optimal clustering, an energy efficient cluster head selection is quite important
This research effort focuses on the design of an energy-efficient cluster head selection algorithm
for WSNs
5
6. Vast research has been done in the area of wireless sensor networks in order to increase the
lifetime of the network.
Algorithms devised for increasing the longevity of the network can be broadly categorized into two
1. Heuristic-based clustering algorithm :
• low-energy adaptive clustering (LEACH) is of the predominant clustering algorithm which
elects the cluster head with some feasibility .
• reducing the unwanted traffic and energy consumption of nodes
• increasing the longevity of the network.
• However, it does not provide any adequate information about the number of cluster heads in a
network
Related Work
6
7. 2. MetaHeuristic-based clustering algorithm :
• 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.
• Algorithms. Some of the approaches are ant colony optimization (ACO), fish colony optimization
(FCO), bird flocking behaviour, particle swarm optimization (PSO), firefly algorithm (FA) ,bat
algorithm (BA), cuckoo search (CS), artificial bee colony optimization (ABC), fish swarm
optimization Grey wolf optimizer (GWO)
7
8. • Recently,in 2020 cluster head selection in WSN using Grey Wolf Optimization is implemeted and
The observed results convey that the proposed algorithm outperforms better compared to E-
LEACH, GA, CS, PSO-C, and FFOA algorithms in terms of energy consumption, network lifetime
and packet received by the BS .
• Grey wolf albha ,beta ,delta lead omega wolf toward the area of search space that are promising
the optimal solution and this behaviour may lead to entrapment in locally optimal solution
,means still we can more improve for the cluster head selection .
• we required to implement a algorithm which should be the improved version of grey wolf
optimization so that cluster head selection in wireless sensor network can also be improved .
8
9. Side-effect of GWO is the reduction of the diversity of the population and cause GWO to fall
into the local optimum which can be improved and due to this cluster head selection can also be
improved .
To overcome these issues, recently an improved grey wolf optimizer (IGWO) algorithm is
Introduced.
The IGWO algorithm benefits from a new movement strategy named dimension learning-based
hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in
nature. DLH uses a different approach to construct a neighborhood for each wolf in which the
neighboring information can be shared between wolves. This dimension learning used in the
DLH search strategy can enhance the balance between local and global search and maintains
diversity.
Current status of work
9
11. The IGWO algorithm Can be implemented for the clusted head selection in wireless sensor
network which 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.
I-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 use
the weight function which involves the intra-cluster distance information, residual energy, and
neighborhood ratio of CHs respectively
…
11
12. The objective function of Cluster Head selection
we use the objective function which utilizes the intra-cluster distance among the sensors and the
distance from the target node.
The objective function 𝑓1, 𝑓2 is mathematically represented as;
we use I-GWO algorithm to select the optimal CH to linearly decrease the function. The combined
objective function is mathematically represented as
𝐹=𝜇×𝑓1+(1−𝜇)𝑓2, 0< 𝜇<1
I
CH
m
i
E
f
1
1
2
min
)
,
(
)
,
(T
(
1
1
min
i
n
i
j
1
BS
CH
CH
n
f i
i
m
i
i
12
13. Experimental setup
Network Configuration
13
Parameter Value
Network Field (300,300)
Base station Position (150,150)
Sensor Node 400
Initial Energy 2J
Number of Cluster Head 20
E(elec) 50 nJ/bit
Packet Size,message size 4000bits,5000bits
IGWO parameter
Parameter Value
No of search agents 50
C (2-0)
a (0-1.5)
µ 0.27
Dimension of search agent 20
No of iteration 5000
14. Performance analysis of Algorithm
The performance of the proposed algorithm will be measured using three metrics namely total
energy consumption (TEC), network lifetime (NL) and packet received by BS (PR-BS).
These three performance metrics will be used to analyze the performance of the proposed
algorithm with other algorithms
In order to measure the performance of energy consumption, firstly we will executed the
algorithms by varying the number of sensor nodes from 400 to 700 and the number of cluster
heads from 20 to 50.
14
15. Conclusion and Future Work
we presented a novel cluster head selection algorithm based on IGWO using efficient search agent
representation and novel objective function. For the energy efficiency, 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
we have tested the proposed algorithm in the homogeneous network. In the future, the same can be
tested on heterogeneous networks
15
16. [1] M. C. M. Thein, and T. Thein, “An energy efficient cluster-head selection for wireless sensor
networks,” 2010 International Conference on Intelligent Systems, Modelling, and Simulation, IEEE,
pp. 287-291, 2010.
[2] Kaushik Sekaran, R. Rajakumar, K. Dinesh , “An energy-efficient cluster head selection in wireless
sensor network using grey wolf optimization algorithm ,” TELKOMNIKA Telecommunication,
Computing, Electronics and Control Vol. 18, No. 6, December 2020, pp. 2822~2833
[3] M. M. Afsar and M. H. Tayarani-N, “Clustering in sensor networks: A literature survey,” Journal of
Network and Computer Applications, vol. 46, pp. 198-226, 2014.
[4] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in engineering software,
vol. 69, pp. 46-61, 2014
[5] Nadimi-Shahraki, M.H., Taghian, S., Mirjalili, S., An Improved Grey Wolf Optimizer
for Solving Engineering Problems, Expert Systems with Applications (2020),
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16