Authors:
Esha Rani,Ashwani Kush,DOI NO:
https://doi.org/10.26782/jmcms.2026.06.00010Keywords:
Clustering; Cross-layer routing; Fuzzy-C Means; Optimization; Wireless Sensor Network; Optimization,Abstract
Wireless Sensor Networks have become increasingly significant in the 21st century, enabling a wide spectrum of critical applications. However, energy efficiency remains a fundamental limitation affecting their scalability, reliability, and sustainability. This study introduces a novel framework that integrates Intelligent Dolphin Echolocation Optimization (IDEO) with Fuzzy C-Means clustering for adaptive Cluster Head (CH) selection. The proposed method employs a multi-criteria strategy considering residual energy, transmission distance to the Base Station (BS), and latency minimization, thereby ensuring efficient data aggregation in dynamic environments. Furthermore, an Energy-Conscious Cognitive Smart Routing Protocol (EC2-SRP) is developed to establish both energy-efficient and shortest routing paths, reducing communication overhead and extending network longevity. The novelty of this work lies in (i) the hybrid integration of IDEO with FCM for adaptive clustering, (ii) multi-criteria CH selection for enhanced energy efficiency, (iii) the design of a cognitive routing protocol balancing energy awareness with path optimality, and (iv) a comprehensive QoS-based evaluation. Simulation results demonstrate superiority over existing methods in terms of energy consumption, network lifetime, packet delivery ratio (PDR), End-to-End Delay, routing overhead, and Fault Tolerance, thereby achieving higher energy efficiency, reliability, and sustainability in Wireless Sensor Networks.Refference:
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