Machine Learning-Based Approaches for Predictive Node Placement in Wireless Sensor Networks

Node placement in Wireless Sensor Networks (WSNs) is a critical factor affecting network performance, including coverage, connectivity, energy efficiency, and scalability. Traditional methods, such as grid-based, random deployment, and deterministic approaches, have provided foundational strategies but often fall short in adaptability and optimization. Recent advancements in machine learning (ML) provide promising enhancements by introducing data-driven models like reinforcement learning (RL), genetic algorithms (GAs), and deep learning (DL). This paper provides a comprehensive comparison of traditional and ML-based node placement methods, evaluating their performance across several key features. The analysis highlights that ML-based methods generally outperform traditional approaches in coverage, adaptability, and energy efficiency, while also providing improved scalability. The results underscore the potential of integrating advanced ML techniques to optimize node placement strategies, addressing the limitations of conventional methods and paving the way for more efficient and adaptive WSN deployments. Future research directions include exploring advanced ML models, hybrid approaches, and real-world applications to further enhance the efficacy and robustness of node placement strategies in WSNs.