An energy‐efficient method using PSO clustering and fuzzy routing to reduce energy consumption in IoT

Summary In “Internet of Things” (IoT) technology, all objects can connect to the Internet with a unique Internet address. The sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. These networks are subject to a variety of errors. Errors in wireless communication channels, network dynamics due to the mobility of nodes, and the potential for mistakes in mobile nodes are among the causes of errors in these networks. Despite the above failures, the development of routing algorithms in IoT‐based networks has become a significant challenge for researchers, so it is essential to provide routing algorithms to increase network lifespan and reduce energy consumption. In this study, a two‐dimensional network‐based architecture for the Internet of Things is presented. The proposed algorithm first organizes the nodes into logical cells and selects nodes in each cell as the cluster head. The cluster head is selected based on the binary Particle Swarm Optimization algorithm. Then, each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a fuzzy method to send the data to the base station. The proposed algorithm uses the remaining energy level parameter, cluster density, and location of each cell using fuzzy system capabilities and the particle swarm algorithm to try to route data with less energy consumption and increase network lifespan. The proposed method is simulated in ns2 software, and for a more accurate evaluation, we compare it with LEACH, Ant‐C, PSO, EABC, and SOA‐CH algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.