Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs

Rajkumar Singh Rathore, Suman Sangwan, Shiv Prakash, Kabita Adhikari, Rupak Kharel, Yue Cao

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)


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.

Original languageEnglish
Article number101
Number of pages28
JournalEurasip Journal on Wireless Communications and Networking
Issue number1
Early online date14 May 2020
Publication statusPublished - 1 Dec 2020
Externally publishedYes

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