A Fuzzy Logic-Based Extremal Optimization Approach for Enhancing Energy Efficiency in Wireless Sensor Networks

A wireless sensor network (WSN) consists of a collection of sensor nodes that collaboratively perform monitoring and data acquisition tasks. Considering the strict resource limitations of sensor nodes, achieving high energy efficiency is a critical requirement. In WSNs, it is essential to minimize data collection delay to ensure that sensed information remains current, while simultaneously maximizing the number of collected data samples to enhance accuracy and reliability. To address these conflicting objectives, this paper proposes a clustering-based routing protocol that simultaneously maximizes packet delivery, minimizes energy consumption, and reduces end-to-end delay. The proposed protocol integrates extremal optimization with fuzzy logic to dynamically form clusters, selecting cluster heads based on two primary criteria: residual energy and distance to the sink. The elected cluster heads then construct a minimum spanning tree (MST) to serve as an efficient multi-hop communication backbone toward the sink. The proposed method, termed the Extremal Optimization Fuzzy-Based Clustering Algorithm (EOFBCA), was implemented and evaluated using the OPNET ۱۱.۵ simulation platform. Performance is compared against three state-of-the-art protocols: AFSRP, BFOABMS, and NODIC. Simulation results demonstrate that EOFBCA achieves superior performance across multiple metrics, including energy consumption, end-to-end delay, throughput, packet delivery ratio, and signal-to-noise ratio.

کلیدواژه ها:

Wireless sensor networks ، Energy Efficiency ، Clustering ، extremal optimization ، Fuzzy logic ، minimum spanning tree