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

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.