Improving The Performance of WSN Using Imperialist Competitive Algorithm With the Minimum Number Of Energy-Harvesting Nodes

Energy consumption is a significant factor in wireless sensor networks (WSNs) which are consists of so many tiny and battery-based sensors. These sensors are distributed in different environments to perform distinct duties and whenever they consume the whole energy of their batteries, some parts of the necessary data of the network or its efficiency can be missed. Recently, in order to enhance these networks, extra energy extraction nodes which can harvest the energy from the environment, are used between sensors and the base station. Due to the high cost of energy harvesting nodes, minimizing the number of these nodes without affecting the quality of the signals is an important aspect of the wireless sensor network design. Therefore, the challenge is determining the efficient number of energy harvest nodes and their coordinates. In this work, for the first time, the Imperialist Competitive Algorithm (ICA) as one of the most advanced evolutionary algorithms is used to determine the minimum number of the energy harvest node. Based on the ICA’s results, the K-Means algorithm is applied to clustering nodes. The simulation results show that the suggested method can improve the performance of the WSN by increasing its lifetime.