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Machine Learning Techniques for Underwater Wireless Sensor Networks: A Comprehensive Study
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Author(s): Deepti Rani (Chandigarh University, India), Anju Sangwan (Guru Jambheshwar University of Science and Technology, India), Anupma Sangwan (Guru Jambheshwar University of Science and Technology, India)and Tajinder Singh (University of Information Science and Technology, Macedonia)
Copyright: 2021
Pages: 18
Source title:
Energy-Efficient Underwater Wireless Communications and Networking
Source Author(s)/Editor(s): Nitin Goyal (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India), Luxmi Sapra (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India)and Jasminder Kaur Sandhu (Chitkara University Institute of Engineering and Technology, Chitkara University, India)
DOI: 10.4018/978-1-7998-3640-7.ch013
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Abstract
With the enormous growth of sensor networks, information seeking from such networks has become an invaluable source of knowledge for various organizations to enhance the comprehension of people interests. Not only wireless sensor networks (WSNs) but its various classes also remain the hot topics of research. In this chapter, the primary focus is to understand the concept of sensor network in underwater scenario. Various mechanisms are used to recognize the activities underwater using sensor which examines the real-time events. With these features, a few challenges are also associated with sensor networks, which are addressed here. Machine learning (ML) techniques are the perfect key of success to resolve such issues due to their feasibility and adaption in complex problem environment. Therefore, various ML techniques have been explained to enhance the operational performance of WSNs, especially in underwater WSNs (UWSNs). The main objective of this chapter is to understand the concepts of UWSNs and role of ML to address the performance issues of UWSNs.
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