MACHINE LEARNING TECHNIQUES FOR ROUTING, LOCALIZATION AND QOS FOR FUTURE WIRELESS SENSOR NETWORKS

Abstract

WSNs plays a crucial role in adopting new generation techniques and their use in creating future-ready technologies. The key difficulties with Wireless Sensor Networks (WSNs) are energy-efficient routing, localization strategies and QoS, as these tiny sensor nodes rely solely on battery power to operate in hazardous situations. So there is a need to research and develop efficient, resilient communication techniques and localization mechanisms to address the issues of WSNs and keep the network operating for an extended period. As a result, low complexity machine learning models manage several difficult tasks such as routing, data aggregation, localization, and motion tracking to define system behavior. Machine learning approaches are thought to be useful for developing energy-efficient routing and localization strategies. Furthermore, machine learning techniques inspire various practical ways to optimize resource utilization and hence increase the lifespan of the sensor network. In this article, an effort has been made to present a broad overview of several machine learning approaches that may be utilized to address various challenges in WSNs, with specific emphasis on routing problems and localization strategies and QoS.