A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

R Muthukkumar; Lalit Garg; K Maharajan; M Jayalakshmi; Nz Jhanjhi; S Parthiban; G Saritha

doi:10.7717/peerj-cs.1029

A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

PeerJ Comput Sci. 2022 Aug 15:8:e1029. doi: 10.7717/peerj-cs.1029. eCollection 2022.

Authors

R Muthukkumar¹, Lalit Garg², K Maharajan³, M Jayalakshmi³, Nz Jhanjhi⁴, S Parthiban⁵, G Saritha⁶

Affiliations

¹ Department of Information Technology, National Engineering College, Kovilpatti, Thoothukudi, Tamil Nadu, India.
² Department of Computer information Systems, Faculty of Information and Communication Technology, University of Malta, Msida, Malta, Malta.
³ Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, India.
⁴ School of Computer Science, Taylor's University, Subang Jaya, Selangor, Malaysia.
⁵ Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India.
⁶ Sri Sairam Institute of Technology,, Chennai, Tamilnadu, India.

Abstract

Background: The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge.

Methods: This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node.

Results: Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.

Keywords: Clustering; End-to-end delay; Genetic algorithm; Heterogeneous wireless sensor networks; Multi-hop routing; Network lifetime; Throughput.

Grants and funding

The work was funded by NICE-Healthcare, Research Excellence Funds by The University of Malta. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.