Optimization and Design of Energy-Efficient Protocols for Wireless Sensor Networks

WSNs considerably benefit many real-world applications like environmental monitoring, healthcare, and smart cities. Energy constraints, however, have a significant impact on sensor node long-term functioning and reliability. This study offers the design and execution of an energy-efficient protocol for WSNs intended to increase network lifespan, lower power use, and improve communication dependability. To fine-tune protocol parameters in real-time, the proposed model combines dynamic clustering, adaptive routing, sleep scheduling, and optimization methods like PSO and Machine Learning (ML). Simulation findings show that the suggested model much exceeds current LEACH, PEGASIS, and TEEN procedures. The suggested protocol cut energy use by as much as 25%, improved packet delivery rates, lowered communication delay, and extended the network lifespan by around 30%.  Smart optimization techniques worked together to not only reduce unneeded energy use but also enhance general network performance. By providing a comprehensive and scalable strategy for creating next-generation energy-efficient WSNs, our study ensures sustainable deployment in energy-constrained environments.