Abstract

Systems of wireless sensor networks (WSNs) usually consist of thousands of sensors that are powered by finite amounts of energy. Clustering strategies have been implemented to improve energy efficiency and lengthen the network’s lifespan. Three common goals are considered while analyzing the existing protocols from a quality of service (QoS) standpoint: energy efficiency, dependable communication, and latency awareness. In intelligent systems, it is essential to comprehend user expectations in order to facilitate the capacity to serve a variety of situations. One difficult clustering issue that has to be resolved is user awareness or user-oriented design. Consequently, the possible difficulties in applying clustering techniques to Internet of Things (IoT) devices in network environments. WSNs are not suitable for, or perhaps unable to operate in, highly dynamic Internet of Things systems with a wide variety of user situations, as existing research on the topic is either carried out in homogeneous or low-level heterogeneous networks. Furthermore, the issue will get more complicated than it was in earlier, more basic WSNs when 5G is eventually implemented. However, when WSN expands, there is a significant raises in the amount of data that sensor nodes must collect, analyze, and distribute. The limited energy of the sensors makes it impossible to process and send such a big volume of data. Applying Machine Learning (ML) methods in WSNs is thus necessary. To maximize the performance of WSN, a number of issues pertaining to the application of clustering methods to IoT must be examined in conjunction with machine learning techniques. The goal of this research project was to create an energy-efficient method that would lower energy use and increase network connection longevity.