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Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches

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Abstract

Sensor node localization is an important feature of many applications, including wireless sensor networks and location-based services. The accurate localization of sensor nodes improves system performance and reliability. This research emphasizes the benefits of using hybrid machine learning and optimization strategies for sensor node localization. Machine Learning (ML) algorithms, such as neural networks and support vector machines, are used to simulate complex correlations between sensor readings and related locations. These models enable precise prediction of node placements based on received signal strength, time of arrival, or other sensory inputs. The survey conducted in this study aims to uncover the latest advancements in localization strategies within Wireless Sensor Networks through the utilization of ML and Optimization Techniques. By thoroughly examining the existing literature, research gaps have been identified when localization techniques are solely employed. To provide a comprehensive understanding, this survey offers a detailed classification of localization algorithms, covering various aspects. Furthermore, the paper elaborates on the implementation of Optimization and Machine Learning approaches, exploring potential combinations with localization techniques. Through the use of analytical tables, the survey presents a comprehensive overview of sensor node localization using ML and optimized approaches. Additionally, the study addresses the challenges encountered and identifies potential future directions for the integration of these techniques.

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Data Availability

Data sharing is not applicable to this article as this is a survey paper and no datasets were generated or analysed during the current study.

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Acknowledgements

I am currently pursuing my doctoral studies at IIT Roorkee in India, under the QIP fellowship program sponsored by M.J.P. Rohilkhand University, Bareilly. The research presented in this paper is an integral part of my doctoral work. I would like to express my sincere gratitude to my supervisor, Prof. Subhash Chander Sharma, from IIT Roorkee, for his invaluable support and guidance. Throughout my academic journey, Prof. Sharma’s unwavering enthusiasm, extensive knowledge, and meticulous attention to detail have been a constant source of inspiration. From my initial exploration of numerous books to the finalization of this research paper, his mentorship has played a pivotal role in shaping the current form of this work.

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    Preeti Yadav & S. C. Sharma

  2. Department of CSIT, M.J.P. Rohilkhand University, Bareilly, India

    Preeti Yadav

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Yadav, P., Sharma, S.C. Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches. Wireless Pers Commun 132, 2293–2362 (2023). https://doi.org/10.1007/s11277-023-10630-x

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