Abstract

Wireless Sensor Network (WSN) is made up of sensor and actuator nodes that are widely dispersed within a certain geographic area. Sensing, gathering, processing, and wirelessly transmitting data have successfully been carried out on WSNs. Distributed computing, sensors, computer networks, communication, embedded systems, and other techniques and technologies are all applied in the WSN. Limited communication capacity caused by low energy resources is one of the major challenges facing WSN. However, longer network lifetimes have been made possible by incorporating solar energy with wireless sensor networks (SE-WSN), even if WSN applications are severely limited by battery capacity. Detecting and guiding SE-WSN against sensor nodes from attack is one of the major challenges in SE-WSN. Therefore, this study developed an intrusion detection model for SE-WSN using support vector machine and random weight grasshopper optimization algorithm (SVM-RWGOA). MATLAB environment was used to simulate the developed model. SVM-RWGOA technique showed an improvement in the performance of SE-WSN when it was compared with the existing approach of honeybee optimization algorithm (HBOA) and ensemble particle swarm optimization (ESPSO) using detection accuracy, packet delivery ratio and energy consumption as performance metrics.