Wireless Sensor Networks for Real-Time Environmental Data Collection and Analysis With AI

Abstract

The growing urgency of climate change, pollution, and biodiversity loss has driven the development of advanced environmental monitoring systems capable of generating timely, accurate, and actionable insights. Wireless Sensor Networks (WSNs) represent a transformative approach to real-time environmental monitoring, enabling distributed sensing, data acquisition, and communication across heterogeneous ecosystems. However, WSNs face persistent challenges, including limited energy resources, scalability, and data management complexity. The integration of Artificial Intelligence (AI) into WSNs provides powerful solutions to these limitations by enabling intelligent data analysis, predictive modeling, anomaly detection, and autonomous system optimization. This review explores the convergence of WSNs and AI for environmental data collection and analysis, examining WSN architectures, communication protocols, and sensor technologies alongside AI-driven approaches such as machine learning, deep learning, and edge intelligence. Key applications in climate monitoring, pollution detection, precision agriculture, and disaster management are highlighted. Challenges related to energy efficiency, security, interoperability, and deployment costs are critically assessed. Finally, the review outlines future research directions, emphasizing the potential of AI-enabled WSNs in building sustainable, adaptive, and resilient environmental monitoring systems.