Survey on Energy Optimisation in Wireless Sensor Network
Vishwajit K. Barbudhe, Shruti K. Dixit · 2024
Numerous energy-constrained sensors are often used in sensor network wireless technology. Clustering techniques have been employed to reduce energy consumption and extend the lifespan of the entire network. Current methods are examined from a quality of service (QoS) standpoint, with several basic criteria in mind: energy efficiency, secure interaction and latency monitoring. Intelligent gadgets must take user preferences into account so as to manage a variety of circumstances. One lingering difficult topic in clusters is client of users or customer-oriented architecture. The possible difficulties of applying clustered methods to Internet of Things (IoT) technologies in SG infrastructures. According to recent research, because WSNs operate across both homogenous and a small amount of diverse relationships, they aren’t suitable for use because they aren’t capable of operating within extremely IoT platforms with an extensive variety of user situations. Furthermore, once SG is realised, the issue will grow harder than with conventional, simple WSNs. However, as WSN expands, so does the amount of information that nodes with sensors must collect, manage and distribute. Given these sensors’ energy requirements, analysing and delivering such a significant volume of information is unfeasible. As a result, there is a requirement for ML (machine learning) methods to be used in WSNs. Numerous issues associated with deploying clustering methods in the IoT, as well as machine learning approaches, must be investigated in order to optimise WSN efficiency.