The paper discusses the advantages, limitations, effects of these methods on various WSN techniques like topology, coverage, localization, network and node connectivity, routing, clustering, cluster head selection, cross-layer issues, intrusion detection, etc. It also gives a brief description of the usage of various machine learning techniques in WSNs from 2002 till 2020. The paper gives an extensive survey on various optimization methods employed to solve many WSN issues from 2005 till 2020. So, there is a need to introduce optimization in such cases. Some of the applications like target tracking, congestion control, and many more, do not give desired results even after applying the machine learning techniques. But machine learning approaches also cannot solve all the problems in WSN solely. To conquer the limitations of traditional WSN algorithms, machine learning has been introduced in wireless technology. So, they suffer from a trade-off between various QoS parameters like network lifetime, energy efficiency, and others. The traditional WSN algorithms are programmed for fixed parameters without any touch of Artificial Intelligence as well as the optimization technique. The scalability, costeffectiveness, and self-configuring nature of WSN make it the fittest technology for many network designs and scenarios. Since the last decade, wireless sensor network (WSN) and Internet of Things (IoT) has proved itself a versatile technology in many real-time applications.
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