AI-Empowered Smart Sensor Networks: Enabling Dynamic Perception and Data Analytics in IoT

Dear Colleagues,

In recent years, the rapid development of the Internet of Things (IoT) has generated massive volumes of sensor data. Smart sensor networks (SSNs), as a crucial bridge connecting the physical and digital worlds, are becoming increasingly important. However, efficiently processing this heterogeneous, spatio-temporally dynamic data to extract valuable insights for online tracking, situational awareness, and intelligent decision-making remains a significant challenge.

The deep integration of artificial intelligence (AI) with sensing technologies offers unprecedented opportunities to address these issues. This convergence is not only driving the evolution from simple data collection to complex environmental perception and inference but also placing higher demands on the intelligence of sensor circuits, network adaptability, and the depth of data analytics.

This Special Issue, therefore, aims to bring together original research and review articles on the latest advances, innovative technologies, solutions, and new challenges in smart sensor networks, AI-enabled data analytics, and their applications in IoT. We are particularly interested in contributions that integrate advanced AI methods (such as deep learning and reinforcement learning) with sensing technologies to achieve efficient data processing, precise dynamic perception, and intelligent adaptive system design.

Potential topics include, but are not limited to, the following:

• Intelligent Sensor Data Processing: Smart processing, fusion, and analytics for heterogeneous sensor data in IoT.
• AI for Sensor Time Series: AI-based time-series analysis, forecasting, and anomaly detection for sensor data.
• Dynamic Spatio-Temporal Perception: Inference, perception, and modeling of dynamic spatio-temporal data from sensor networks.
• Sparse Sensing and Compressive Sampling: Efficient data acquisition, sparse representation, and reconstruction techniques in large-scale sensor networks.
• Adaptive and Mobile Sensing: Adaptive intelligent environmental sampling strategies and routing for mobile sensors and robotic networks.
• Electromagnetic Environment Awareness: AI-driven situational awareness, spectrum sensing, and analysis of the electromagnetic environment.
• Intelligent and Adaptive Circuits for Sensing: Intelligent adaptive circuit and hardware design for smart sensors and low-power IoT devices.
• AI-Enabled Tracking and Localization: Advanced algorithms for online tracking, positioning, and navigation in IoT-enabled environments.
• AI in Sensor Network Management: AI methods (e.g., reinforcement learning and federated learning) for sensor network optimization, resource allocation, and security.

Prof. Dr. Fan Wu
Prof. Dr. Puning Zhang
Dr. Cong Zhang
Guest Editors

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