An Energy-Efficient Clustering Algorithm for WSN Based on Cluster Head Selection Optimization to Prolong Network Lifetime

Khadim, Rania; Maaden, Abdelhakim; Ennaciri, Ansam; Erritali, Mohammed

doi:10.18178/ijfcc.2018.7.3.520

Cited by 12 publications

(8 citation statements)

References 10 publications

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“…Clustering of nodes reduces the energy consumed during the communication of data to the base station. Due to the minimum energy consumption, the lifetime of the IoT networks can be extended [6].…”

Section: Fig1 Iot Based Wireless Sensor Networkmentioning

confidence: 99%

Enhanced Quality of Service Strategy for Improving Network Coverage in IOT Applications

Amalarethinam¹,

Mercy²

2021

JUSST

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The Internet of Things (IoT) is a network that includes physical things capable of aggregating and communicating electronic information. With the advancement in wireless sensor networks, IoT provides highly efficient communication for various real-time applications. IoT networks are large-scale networks where routing can be improved by focusing on the Quality of Service (QoS) Parameter. Network coverage can be enhanced by hierarchical clustering of the nodes which increases the network lifetime. The proposed algorithm Enhanced Fuzzy Based Clustering and Routing Algorithm (EFCRA) performs distance and energy-based cluster head selection to find a new path from source to destination. The algorithm uses Fuzzy c-means clustering to provide optimization in forming cluster centers. The cluster head (CH) is identified based on the minimum distance and maximum energy of the sensor node. The cluster head is updated when its energy is lesser than the threshold value. The distance between sensor nodes and its CH node and then to the destination is computed using Dijkstra’s algorithm. The proposed routing strategy provides improved network coverage and throughput which extends the lifetime of the IoT network.

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Section: Fig1 Iot Based Wireless Sensor Networkmentioning

confidence: 99%

Enhanced Quality of Service Strategy for Improving Network Coverage in IOT Applications

Amalarethinam¹,

Mercy²

2021

JUSST

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“…It is measured in milliseconds. The lower value of end-to-end delay means the better performance of the protocol [15][16][17][18][19]. The Delay is calculated in Eq.…”

Section: ) Average Throughputmentioning

confidence: 99%

An Efficient Cluster-based Routing Protocol for Improvement Delay in Mobile Ad-hoc Networks

Najafi¹,

Aghaei²

2020

Preprint

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Cluster-based routing in Mobile Ad-Hoc Networks is considered one of the convenient methods of routing. The existence of Cluster Head (CH) in a group of nodes for data forwarding improves the performance of routing in terms of routing overhead and power consumption. In this paper, a clustering algorithm using hierarchical routing for mobile ad hoc networks is proposed. In the proposed method, first, clusters are formed by the FLOC clustering method with low overlapping. Then, according to the most important network criteria, including hop count, noise ratio, distance, remaining energy, and link expiration time, head clusters are selected based on the weighting algorithm. To transfer data in the proposed method, two types of communication are used, e.g., inter-cluster communication and intra-cluster communication in which the shortest path to the target cluster is selected. The proposed P-METHOD technique is based on hierarchy and clustering as well as using a weighted algorithm that reduces delay in data transmission on the network. Moreover, P-METHOD is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the MANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), delay, residual energy, and lifetime.

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“…The energy inherent within the sensor nodes needs to be optimized in the network since they are made up of tiny nodes with limited battery power. 2 In this context, the transmission of data between the cluster head and sensor nodes with energy effectiveness is made feasible through the process of clustering. 3 The clustering is considered as the process of partitioning the entire geographical area into small clusters with a single node being designated as the cluster head for each and every separated cluster.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, network lifetime is identified as a major concern in hostile environments as the deployment of sensor nodes with restricted battery capacity introduces challenges into the implementation environment. The energy inherent within the sensor nodes needs to be optimized in the network since they are made up of tiny nodes with limited battery power 2 . In this context, the transmission of data between the cluster head and sensor nodes with energy effectiveness is made feasible through the process of clustering 3 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks

Tamilarasan

Lenin²,

Jayapandian

et al. 2021

Int J Communication

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Wireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography-based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes.

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An Energy-Efficient Clustering Algorithm for WSN Based on Cluster Head Selection Optimization to Prolong Network Lifetime

Cited by 12 publications

References 10 publications

Enhanced Quality of Service Strategy for Improving Network Coverage in IOT Applications

Enhanced Quality of Service Strategy for Improving Network Coverage in IOT Applications

An Efficient Cluster-based Routing Protocol for Improvement Delay in Mobile Ad-hoc Networks

Hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks

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