Intelligent Water Drop Based Wireless Network Energy Optimization Model

Bio-inspired metaheuristics can be useful for the optimization of complex systems. Wireless sensor networks (WSNs) are massively distributed cyber-physical systems whose efficient operation requires appropriate design and control strategies. This work proposes an IoT network optimization model that performs node clustering and efficiently routes data packets from sensing IoT devices to the base station. In the proposed approach, clustering is carried out using the Intelligent Water Drops (IWD) algorithm, which intelligently identifies suitable cluster centers based on the network conditions. The algorithm can operate effectively in dynamic WSN environments without requiring prior knowledge of node configurations. Furthermore, an optimized routing strategy is employed to reduce energy consumption during data transmission. Extensive experiments were conducted using different node distributions and network regions. The results demonstrate that the proposed IWD-based clustering and routing approach significantly improves energy efficiency and extends the overall lifetime of the IoT network.