Abstract
Primary goal of wireless sensor network (WSN) to deal with real-world issues creates such network which is feasible and efficient to implement the applications such as monitoring, surveillance of man, machine, structures and natural phenomenon. Topological changes are inevitable due to dynamic nature of WSN. As network dynamics changes, all functional and non-functional operations of wireless sensor network are affected. Traditional approaches used in other networks are incapable of responding and learning dynamically. In current scenario, WSN is integrated with recent technologies like Internet of things and cyberphysical systems to facilitate scalability for providing common services. It is imperative that wireless sensor networks are energy-efficient, self-configurable and can operate independently with minimum human intervention. In order to instil these properties, recently a lot of work has been done to explore machine learning algorithms to tackle with issues and challenges of WSN. In this paper, a basic introduction to machine learning algorithms and their application to various domains of wireless sensor networks are covered.
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Sudha, Singh, Y., Sehrawat, H., Jaglan, V. (2022). Approach of Machine Learning Algorithms to Deal with Challenges in Wireless Sensor Network. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_31
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