A hybrid cluster head selection model for Internet of Things

Internet of Things (IoT) is one of the rising networking standards that gap between the physical world and the cyber. Energy conservation of IoT devices becomes a fundamental challenge for extending the life time of the network. As a solution to this challenge, cluster head selection can be used. This paper intends to adopt a hybrid model with both Moth Flame Optimization and Ant Lion Optimization (ALO) to improve the performance of cluster head selection among IoT devices in WSN–IoT network. The particular simulation approach not only preserves energy of the sensor node by maintaining distance and delay but also balances the temperature and load of IoT devices for attaining the optimal cluster head selection in WSN–IoT network. Further, it compares the performance of the proposed hybrid model over the traditional models like Artificial Bee Colony, Genetic Algorithm, Particle Swarm Optimization, Gravitational Search Algorithm, ALO, MFO and Adaptive GSA. The simulation analysis considers the convergence, sustainability of alive nodes, normalized energy, load, and temperature. Thus the proposed simulation results are more efficient for prolonging the life time of the network.