Simulation-Driven Energy-Efficient Routing Protocols for Wireless Sensor Networks Using VLSI-Based Techniques

Wireless Sensor Networks (WSNs) are integral to critical applications in industrial automation, healthcare, and environmental monitoring. Despite their broad utility, the limited energy capacity of sensor nodes continues to hinder long-term network operation. This study presents a simulation-driven analysis of energy-efficient routing protocols, integrating Machine Learning (ML)-based prediction, VLSI/FPGA hardware acceleration, and software-defined networking. Using NS-3 and MATLAB-based simulations, the proposed ML-HKM_PSO hybrid routing framework demonstrates significant performance gains over traditional protocols such as LEACH and PEGASIS. Results show a 25.7 % reduction in total energy consumption, an increase in network lifetime by 32 %, and a packet delivery ratio (PDR) improvement to 96.8 %. Additionally, average end-to-end delay decreased by 18.8 %, and residual energy levels at 1000 seconds were notably higher, indicating improved sustainability. The study also explores FPGA-based architectures for power-aware computation and discusses the integration of deep learning and reinforcement learning in adaptive routing strategies. A unified hybrid framework is proposed to bridge software-driven intelligence with low-power hardware optimization. The results validate the effectiveness of the combined simulation–hardware–ML approach and point toward future directions in AI-driven routing, secure topologies, and wireless energy harvesting for scalable WSN deployments.