Abstract

In traditional Wireless Sensor Networks (WSNs), routing protocols often suffer from high energy consumption due to redundant data collection at fixed intervals. To tackle this issue and conserve energy, this paper introduces a novel approach using prediction-based data fusion methods. Firstly, it proposes the Low Energy Adaptive Clustering Hierarchy-Energy-Kopt-N (LEACH-E-Kopt-N) algorithm, specifically optimizing the cluster-head election phase of the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Secondly, it presents a data collection model utilizing the Grey Data Prediction Model, which predicts future sensor data to minimize redundant transmissions. These innovations culminate in a new data fusion algorithm called Grey-Clusters-Leach (GCL), which integrates data prediction techniques. Simulation results demonstrate that GCL reduces energy consumption by 18%, 35%, 21.5%, and 20%, and extends the network operational lifespan by 3%, 35%, 22%, and 5% compared to EQDC LEACH, LEACH-E, and SEP algorithms, respectively. GCL efficiently manages cluster size and number, leading to a 20% reduction in packet transmissions, thereby enhancing overall WSN performance.