AI-Powered Low-Power Clustering Algorithms to Enhance Heterogeneous Wireless Sensor Network

Wireless Sensor Networks (WSNs) have become an essential technology in a variety of fields, including industrial automation, healthcare, and environmental monitoring. Energy efficiency is still a major problem, though, particularly in heterogeneous WSNs where sensor nodes vary in processing power, energy capacity, and sensing range. In order to improve energy efficiency and extend the network lifetime of heterogeneous WSNs, this research suggests an AIdriven grouping approach. The suggested approach makes use of machine learning techniques, particularly adaptive decision-making and reinforcement learning, to dynamically optimize cluster head selection and routing patterns. Our method allows for more intelligent and balanced energy consumption by adaptively taking into account node residual energy, communication cost, and network topology, in contrast to conventional clustering protocols like LEACH and HEED. When compared to traditional methods, simulation results show a significant improvement in network longevity, energy consumption, and data transmission dependability. The development of intelligent, self-adaptive WSNs for next-generation Internet of Things (IoT) applications is aided by this work.