A Novel Approach to Optimized Cluster Head Selection in Heterogeneous WSNs for IoT Deployments

Despite the study proposing the LEACH methodology, its ineffectiveness has been shown. A non-deterministic polynomial (NP) is a difficult issue to optimise for CH. Using an Artificial Immune System (AIS) and Particle Swarm Optimisation (PSO), this study suggests a new routing algorithm. This paper also discusses the best route choice, which may increase network lifetime and reduce energy consumption. We make good use of a lot of metaheuristics techniques, including PSO and AIS. Using principles from neuroscience and social science, the PSO algorithm tackles challenges in a wide range of fields, with a focus on computing and engineering. Particles are creatures that move around the multi-dimensional search distance, where every particle represents a possible solution to the multi-dimensional optimisation issue. The most recent development in AI, AIS takes its cues from the workings of the immune response in living organisms. Immune systems control both the inborn and adapted immunity response's defence mechanisms. Since the latter includes metaphors like variety, self-control, recognition, and memory growth, it is more significant. The AIS outperforms the Heterogeneous Low Energy Adaptive Clustering Hierarchy (LEACH-HPR) and particle swarm optimisation (PSO) in terms of results from experiments. Artificial Immune System (AIS) retains an extensive amount of active nodes while maintaining its efficiency with a 98.5% accuracy rate. AIS is more energy efficient, which means nodes last longer, but it's not much better than PSO and LEACH-HPR in terms of accuracy.