Intelligent Wireless Sensor Networks: Advancing Efficiency and Autonomy with Machine Learning

Dear Colleagues,

The rapid advancements in Wireless Sensor Networks (WSNs) have enabled a wide range of applications, from environmental monitoring and smart cities to industrial automation and healthcare. However, the dynamic and resource-constrained nature of WSNs presents significant challenges in areas such as energy efficiency, data reliability, network optimization, and security. To address these challenges, the integration of Machine Learning (ML) techniques has emerged as a transformative solution, enabling intelligent decision-making, anomaly detection, adaptive resource management, and predictive analytics. This Special Issue aims to explore cutting-edge research and innovative applications that leverage ML to enhance the performance, scalability, and resilience of WSNs, paving the way for next-generation intelligent sensor networks. We invite original research articles and reviews on topics including, but not limited to, ML-driven energy-efficient protocols, federated learning in WSNs, deep learning for sensor data analysis, anomaly detection and security enhancement using AI, reinforcement learning for adaptive network management, and edge intelligence for real-time WSN applications. This Special Issue aims to provide a comprehensive overview of the synergies between ML and WSNs, fostering interdisciplinary discussions and novel contributions that push the boundaries of intelligent wireless sensing systems. Researchers, practitioners, and industry experts are encouraged to contribute their insights and findings, helping to shape the future of smart and autonomous WSNs.

Dr. Elias Dritsas
Guest Editor

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