Abstract
The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN concerning Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, from 2010 to 2021. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for for research community in identifying the most adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper by stating the open research issues and new directions for future research.
Highlights
The field of Ad-hoc network technology is experiencing remarkable research attention over the years [1]
We provide a general evaluation and comparison of using different Artificial Intelligence (AI) methods to solve Coverage, Deployment and Localization challenges in Wireless Sensor Network (WSN), which will be a guide for research community in identifying the mostly adapted methods to address these challenges and the benefits of using various AI methods
We primarily present an overview of Coverage, Deployment and Localization challenges in WSNs and the utilized AI techniques
Summary
The field of Ad-hoc network technology is experiencing remarkable research attention over the years [1]. The key focus of this survey is to provide a comprehensive discussion on the recent studies of Coverage, Deployment and Localization challenges that utilized various Artificial Intelligence (AI) methods to meet specific objectives of WSN, during the span of 2010 to 2021. We provide a general evaluation and comparison of using different AI methods to solve Coverage, Deployment and Localization challenges in WSNs, which will be a guide for research community in identifying the mostly adapted methods to address these challenges and the benefits of using various AI methods. There has been related work that discussed or partially surveyed the literature related to AI based protocols and algorithms solving different WSN challenges. 4) We identify promising research directions in applying AI-based solutions to Coverage, Deployment, and Localization challenges in WSN, with the aim to promote and facilitate further research.
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