An Intelligent Recurrent Backpropagation Neural System for Energy Optimized Wireless Sensor Based Vehicle Communication

Wireless sensor networks (WSN) have emerged in all digital and smart applications for providing the finest communication range. However, the main problem that often occurs in the WSN is the wide range of energy consumption. So, the present study has focused on developing a novel chimp-based back propagation recurrent neural model for optimizing energy usage. Here, the application that has been considered in this study is multi-input multi-output (MIMO) vehicle communication. Moreover, the planned procedure is executed in the NS2 environment and the performance has been validated in the dual phases that are before applying the chimp fitness and after applying the chimp optimal solution. Finally, the gained metrics have been compared with other conventional models and have earned the finest packet delivery rate and less energy consumption. This finest performance has proved the need for the designed model in the MIMO vehicle application for managing the energy resources by proper cluster-head selection and shortest route finding.