Amirmasoud Soltanzadeh and Zbigniew Dziong, Ecole de technologie superieure (ETS), Canada

The increasing use of sensor-based wireless communication systems, such as WSNs, provides numerous benefits; however, some issues arise, most notably regarding energy efficiency. In the past, multiple approaches have addressed energy issues in WSNs; however, there is a need to overcome the remaining issues. This study leverages advanced artificial intelligence algorithms, clustering, and a routing process to enhance the performance of WSNs intelligently. The clustering algorithm used is PSO-mutation, which is employed to select CHs. Golden eagle optimization is used for route optimization within the CHs to minimize energy expenditure and enhance the WSNs lifetime. Matlab tool to simulate and evaluate with AI techniques based on a genetic and predictive coding theory algorithm, as well as the traditional Leach-CE-based routing protocol for WSNs. Performance metrics include energy consumption, the number of dead nodes, throughput, and delay. The proposed model demonstrates significant improvements over the Leach-CR model, thereby justifying its validity.

Golden Eagle Optimization (Geo), Particle Swarm Optimization-Mutation (Pso-Mutation), Energy Efficiency Routing Protocol (Eerp). Wireless Sensor Networks (Wsns).