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Comparative Studies | Computer Engineering | India | Volume 12 Issue 6, June 2023 | Popularity: 4.7 / 10

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Study of Machine Learning Techniques for Coverage in Wireless Sensor Networks

Anvesha Katti

Abstract: Wireless Sensor Networks (WSNs) can be used for a variety of applications such as monitoring of environment and surveillance. One of the fundamental issues in WSNs is the coverage problem, which refers to ensuring that the sensing area is adequately covered by the sensor nodes. Machine learning techniques have been recently proposed for this problem. Our paper presents an inclusive review of the latest machine learning techniques used for coverage in WSNs. The paper discusses the challenges associated with coverage in WSNs and how machine learning techniques can be applied to solve them. Different machine learning techniques such as Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Clustering and Genetic Algorithm are discussed in detail. The paper also highlights the strengths and weaknesses of each algorithm and provides a comparative analysis of their performance. Finally, the paper concludes with some open research challenges and future directions in the field of machine learning for coverage in WSNs.

Keywords: coverage, machine learning techniques, wireless sensor networks

Edition: Volume 12 Issue 6, June 2023

Pages: 2415 - 2418

DOI: https://www.doi.org/10.21275/SR23622201323

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