Clustering Based Hybrid Optimized Model for Effective Data Transmission

The Internet of Things (IoT) is a system of unified gadgets that can conversation data and operate in tandem thanks to the web. When it comes to the longevity of a network, smooth data production is crucial, and wireless sensor networks (WSN) play a key character in the IoT in this regard. Despite the IoT’s usefulness in many areas, it still faces obstacles in the form of security, energy, load balancing, and storage. Clustering and multi-hop routing are two methods used in the architecture of an IoT-assisted WSN to reduce energy consumption. This research therefore provides a novel effective hybrid optimization strategy for choosing cluster heads. In to adjust the white shark optimizer’s (WSO) stochastic behaviour while it seeks out food, the suggested method makes use of the whale optimization approach (WOA). The new HWSO was also tested against a group of contemporary meta-heuristic methods, such as the artificial optimizer (GTO), the coyote optimization algorithm (COA), and the original WSO. Finally, the proposed network is put through its paces by making use of NS-3.26’s extensive simulation features. Improvements in packet delivery ratio (PDR), latency, energy consumption, number of dead nodes, and longevity of the network may be shown in the simulation results.