An Evolutionary Tri-Hybrid Compressive Sensing Framework for Energy, Memory, and Stability Optimization in Wireless Sensor Networks

To bridge the gap between energy efficiency, memory utilization, and communication stability in dense Wireless Sensor Networks, a unified hybrid optimization framework is proposed. This work introduces an Evolutionary Tri-Hybrid Compressive Sensing Framework for Energy, Memory, and Stability Optimization in Wireless Sensor Networks (ETCS-EMS). This proposed approach integrates Particle Swarm Optimization, Artificial Bee Colony, and Firefly Algorithm with Compressive Sensing to achieve efficient clustering and data reduction simultaneously. Simulation results demonstrate that ETCS-EMS significantly enhances network performance across multiple dimensions. Energy efficiency improves by 61%, while the overall network lifetime extends by 39% compared to the non-CS baseline. Memory usage per node is reduced by 29%, and the framework achieves a compression ratio of 2.4×, leading to more than 67% reduction in raw data transmission volume. In terms of data fidelity, the proposed method records an 18% improvement in PSNR, ensuring better signal reconstruction at the sink. Communication reliability also improves, with the Packet Delivery Ratio increasing by 9.3%, while end-to-end delay reduces by 22%, supporting real-time responsiveness. These results confirm that the combination of hybrid evolutionary clustering with CS not only prolongs WSN lifetime but also reduces memory burden, enhances reconstruction quality, and stabilizes network communication. Thus, ETCS-EMS provides a scalable and energy-conscious framework for next-generation WSN and IoT applications requiring both efficiency and reliability.