Abstract

Energy-efficient and intelligent routing strategies have become essential as wireless sensor networks are increasingly incorporated into Internet of Things environments. For IoT-enabled Wireless sensor networks (WSNs), this study suggests an improved internal routing framework with an emphasis on cluster head to sink communication optimization following cluster head selection. Based on a strong cluster formation procedure that employs hybrid fuzzy C-Means and K-Means algorithms, a multi-objective Mother-Inspired Adaptive Optimization method is used to choose the cluster heads. Next, energy- and distance-aware routing paths between cluster heads are dynamically constructed using the Pelican Optimization Algorithm. Key issues in Internet of Things-based deployments, such as limited energy, data latency, and communication reliability, are addressed by the suggested approach. The results show from prior hybrid optimization-based WSN Studies that the hybrid approach is effective in managing large-scale, resource-constrained sensor networks within IoT infrastructures by significantly extending network lifetime, improving packet delivery ratio, and lowering end-to-end delay.

Graphical Abstract

Novelty Statement

A comparative analysis of three key network lifetime metrics; FND, HND, and LND across various protocols: LEACH, GWO, EECHS-ISSADE, Proposed, FMIAO, and FMPOA.