A NOVEL SWARM INTELLIGENCE-BASED COMPRESSIVE SLEEPING APPROACH TO IMPROVE THE LIFETIME OF WIRELESS SENSOR NETWORKS

Abstract

Wireless Sensor Networks face serious energy limitations that directly constrain their operational lifetime. To address this, we propose a hybrid optimization framework that integrates compressive sensing, genetic algorithm, and ant colony optimization to decrease energy consumption while preserving data accuracy. The proposed method considers both spatial and temporal correlations but unlike existing approaches that rely on random node activation or single-hop routing, our method centrally selects active nodes using genetic algorithm and constructs a multi-hop data aggregation tree using ant colony optimization for energy-efficient transmission. This design offers a significant improvement over compared methods by jointly optimizing signal reconstruction and routing. Simulation results demonstrate that our method reduces signal reconstruction error by over 48% and extends network lifetime by more than 18% compared to leading baselines.