An energy-aware cluster-based routing in the Internet of things using particle swarm optimization algorithm and fuzzy clustering.

The effectiveness and longevity of IoT infrastructures heavily depend on the limitations posed by communication, multi-hop data transfers, and the inherent difficulties of wireless links. In dealing with these challenges, routing, and data transmission procedures are critical. Among the fundamental concerns are the attainment of energy efficiency and an ideal distribution of loads among sensing devices, given the restricted energy resources at the disposal of IoT devices. To meet these challenges, the present research suggests a novel hybrid energy-aware IoT routing approach that mixes the Particle Swarm Optimization (PSO) algorithm and fuzzy clustering. The approach begins with a fuzzy clustering algorithm to initially group sensor nodes by their geographical location and assign them to clusters determined by a certain probability. The proposed method includes a fitness function considering energy consumption and distance factors. This feature guides the optimization process and aims to balance energy efficiency and data transmission distance. The hierarchical topology uses the advanced PSO algorithm to identify the cluster head nodes. The MATLAB simulator shows that our method outperforms previous approaches. Various metrics have demonstrated significant improvements over DEEC and LEACH. The method reduces energy consumption by 52% and 16%, improves throughput by 112% and 10%, increases packet delivery rates by 83% and 15%, and extends the network lifespan by 48% and 27%, respectively, compared to DEEC and LEACH approaches. [ABSTRACT FROM AUTHOR]