Neuro-Fuzzy Clustering and Genetic Optimization Algorithm to Enhance the Quality of Services in IoT-enabled Wireless Sensor Networks

Wireless sensor networks, sometimes referred to as WSNs, are made up of a group of sensor nodes that are dispersed across a geographical area and operate in a decentralized fashion throughout the environment in which they are deployed. Their purpose is to detect natural events. To reach the base station, the data that has been sensed are communicated by the nodes through a process known as multi-hop communication. As a result of the fact that the gadget has limited resources and communicates in an open-source format, The provision of energy optimization in conjunction with the provision of secure connectivity is a significant challenge in this destructive climate. The proposed methodology neuro fuzzy genetic optimization algorithm (NFGOA) to improve the network quality of service. Through the utilization of the neuro-fuzzy clustering algorithm, the suggested protocol can effectively cluster the nodes, and a genetic optimization method is responsible for the routing process. Utilizing the network simulator-3 allows for the implementation of the suggested protocol. When compared to other protocols like neuro-fuzzy clustering along with sparrow search optimization algorithm(NFSSOA),sham-share(SS), and common security-defence policy (CSDP), that are already in use, The results of the simulation indicate that the suggested protocol is superior in quality of services in terms of throughput, energy usage analysis, network delay, network lifetime, and packet delivery ratio are all important aspects to consider.