期刊名称：International Journal of Computer Networks and Applications (IJCNA)
电子版ISSN：2395-0455
出版年度：2021
卷号：8
期号：3
页码：151-187
语种：English
出版社：EverScience Publications
摘要：Since the last decade, wireless sensor network (WSN) and Internet of Things (IoT) has proved itself a versatile technology in many real-time applications. The scalability, cost-effectiveness, and self-configuring nature of WSN make it the fittest technology for many network designs and scenarios. The traditional WSN algorithms are programmed for fixed parameters without any touch of Artificial Intelligence as well as the optimization technique. So, they suffer from a trade-off between various QoS parameters like network lifetime, energy efficiency, and others. To conquer the limitations of traditional WSN algorithms, machine learning has been introduced in wireless technology. But machine learning approaches also cannot solve all the problems in WSN solely. Some of the applications like target tracking, congestion control, and many more, do not give desired results even after applying the machine learning techniques. So, there is a need to introduce optimization in such cases. The paper gives an extensive survey on various optimization methods employed to solve many WSN issues from 2005 till 2020. It also gives a brief description of the usage of various machine learning techniques in WSNs from 2002 till 2020. 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. This paper gives a lucid comparison of many state-of-the-art optimization algorithms and descriptive and statistical analysis for discussed issues and algorithms associated with them. It also elucidates some open issues for WSNs/IoT networks that can be solved using these approaches.
关键词：Wireless Sensor Networks;Internet of Things;Quality of Service;Artificial Intelligence;Optimization;Machine Learning
