Title: Modified glowworm swarm optimisation-based cluster head selection and enhanced energy-efficient clustering protocol for IoT-WSN

Authors: T. Kanimozhi; S. Belina V.J. Sara

Addresses: Department of Computer Science, SRM Institute of Science and Technology, Ramapuram Campus, Ramapuram, Chennai, Tamil Nadu, India ' Department of Computer Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chengalpet, Chennai, Tamil Nadu, India

Abstract: The maintenance cost of flat-based wireless sensor network-internet of things (WSN-IoT) is high. Clustering is recommended to reduce message overhead, manage congestion, and simplify topology repairs. A clustering protocol enhances energy efficiency, prolongs network lifespan by grouping nodes into clusters, and reduces transmission distance to the base station (BS). Depending on parameters like quality of service (QoS), energy consumption, and network load, a clustering technique organises nodes into clusters. Each cluster is led by one or more cluster heads (CH) that collect and transmit data to the BS directly or through intermediary nodes. To enhance WSN-based IoT longevity, this study presents an enhanced energy-efficient clustering protocol (EEECP). It establishes an optimal number of clusters, utilises the modified fuzzy C means (MFCM) algorithm to stabilise and reduce sensor node energy consumption, and introduces the modified glowworm swarm optimisation (MGSO) algorithm for CH selection. MGSO incorporates a dynamic threshold for balanced CH longevity within clusters. Performance is evaluated using metrics including first node dies (FND), last node dies (LND), half node dies (HND), weighted first node dies (WFND), energy usage, and network lifetime compared to existing protocols.

Keywords: modified glowworm swarm optimisation; MGSO; modified fuzzy C-means algorithm; MFCM; cluster head; CH; quality of service; QoS; enhanced energy-efficient clustering protocol; EEECP.

DOI: 10.1504/IJCSE.2025.144806

International Journal of Computational Science and Engineering, 2025 Vol.28 No.2, pp.204 - 218

Received: 06 Sep 2023
Accepted: 11 Apr 2024
Published online: 03 Mar 2025 *

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