ABSTRACT Modern wireless sensor networks (WSN) and the internet of things (IoT) have received a lot of attention lately due to the proliferation of smart mobile devices and connected devices. An autonomous sensor‐equipped device is the primary component of the WSN‐based IoT architecture due to its disruptive nature, enabling it to grow into an even greater range of dynamic and sophisticated applications that face even greater difficulties. WSN performance is significantly impacted by the energy resources of nodes. To solve this issue, the suggested study used a fuzzy‐based hybrid optimization model for clustering and increased routing optimization. Cluster heads (CHs) are chosen for routing when WSNs deployed in an IoT context are initially clustered using Fuzzy Logic–Based Enhanced Gray Wolf Optimization (FLₑGWFO) techniques. In the second phase, packets from the IoT sensor nodes are received by a CH, forwarding them to the base station. The intercluster uses Levy Flight Based Improved Bald Eagle Search Optimization (LfIBESO) algorithms to find the best route from sources to the destination during routing. The goal function for choosing CH considers Euclidean distances, error rate, energy consumption, and packet delivery ratio (PDR). Factors such as the remaining energy and the separation between the base station (BS) and CH are assessed to determine the best route. The proposed model attained the end‐to‐end delay of 0. 0796 ms, energy consumption of 4. 294 j, network overhead of 1. 71%, PDR of 0. 98412%, and throughput of 158. 6 kbps, respectively. The proposed model attained the best results by comparing with existing models.
Menaka et al. (Mon,) studied this question.