A Survey on Distributed Rage Free Localization WSN Deployment Using Machine Learning Techniques for Air Pollution Monitoring.

Wireless Sensor Networks (WSNs) have emerged as a critical technology for ubiquitous environmental monitoring, particularly in tracking air pollution. However, the efficacy of WSN deployments is fundamentally limited by energy constraints, the accuracy of node localization, and the optimality of sensor placement. This survey paper provides a comprehensive review of the state-of-the-art literature addressing these challenges, focusing specifically on three integrated objectives central to designing a robust air pollution monitoring system: efficient clustering for identifying pollution zones, developing enhanced distributed and robust localization algorithms, and utilizing Machine Learning (ML) for optimal WSN deployment. We analyze existing solutions, highlight the current gaps, and demonstrate how the fusion of WSN architecture with advanced ML techniques, such as Reinforcement Learning and robust estimation theory, offers a path toward a truly "rage-free," scalable, and efficient environmental monitoring framework.