TY - JOUR

T1 - Hybrid WGWO

T2 - whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs

AU - Rathore, R.S.

AU - Sangwan, S.

AU - Prakash, S.

AU - Adhikari, K.

AU - Kharel, R.

AU - Cao, Y.

PY - 2020/5/14

Y1 - 2020/5/14

N2 - The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols. © 2020, The Author(s).

AB - The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols. © 2020, The Author(s).

KW - Cluster head (CH)

KW - Clustering

KW - Grey wolf optimization (GWO)

KW - WGWO

KW - Whale optimization algorithm (WOA)

KW - Cluster analysis

KW - Energy efficiency

KW - Energy harvesting

KW - Energy utilization

KW - Heuristic algorithms

KW - Optimization

KW - Power management (telecommunication)

KW - Sensor nodes

KW - Cluster formations

KW - Cluster-head selections

KW - Clustering mechanism

KW - Energy Efficient clustering

KW - Energy-constrained sensor nodes

KW - Exploitation and explorations

KW - Meta heuristic algorithm

KW - Reduce energy consumption

KW - Clustering algorithms

U2 - 10.1186/s13638-020-01721-5

DO - 10.1186/s13638-020-01721-5

M3 - Journal article

VL - 2020

JO - EURASIP Journal on Wireless Communications and Networking

JF - EURASIP Journal on Wireless Communications and Networking

SN - 1687-1472

IS - 1

M1 - 101

ER -