An energy efficient routing scheme in internet of things enabled WSN: neuro-fuzzy approach

The Internet of Things (IoT) has led to the deployment of many battery-powered sensors in various applications to gather, process, and analyze meaningful data. Clusters of sensors provide for more efficient data collection and increased scalability in such contexts. A low-latency, long-lived routing strategy is described for WSNs that can connect to the Internet of Things. In this research, we present a neuro-fuzzy approach to energy-efficient routing (NFEER) for IoT-enabled WSNs. The novelty of the proposed algorithms is the multiple parameters for the routing in IoT-enabled WSN as consideration of CH distance to sink, cluster size, and residual energy of CH. These variables are used to find the most efficient path across the network, which will help mitigate the hotspot issue. During the operation on the condition "consider only those nodes which have energy greater than the pre-defined threshold energy," the NFEER relies on energy thresholds to restrict the set of candidate nodes. Extensive simulations are performed to specify the effectiveness of the NFEER, and it elongates stability period by 27.98%, 13.97%, and 10.91% as compared to existing protocols. The stability duration, residual energy, network lifetime, and throughput are enhanced by the proposed method as compared to PSO-Kmean, BMHGA, and FSO-PSO.