Intelligent Control of Cyber-Physical Networks: Complex Sensing Networks, Neuromorphic Sensing, and Machine Learning
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 15
Special Issue Editors
Interests: memristor and memristive systems; neuromorphic computing systems; machine learning; intelligent signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: stability and synchronization of chaotic systems and complex networks with applications
Interests: Wi-Fi/5G/6G HetNets; MEC; IoT/IoV; resource allocation; machine learning; WSN; CPS
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cyber-physical networks represent a paradigm shift in integrating computational intelligence with physical dynamics, enabling autonomous sensing, decision-making, and control across critical infrastructures such as smart grids, autonomous vehicles, and industrial automation systems. The convergence of complex sensing networks, neuromorphic sensing, and machine learning (ML) has unlocked unprecedented capabilities for adaptive control, real-time optimization, and resilience against dynamic uncertainties. However, challenges persist in scalability, energy efficiency, security, and the seamless integration of heterogeneous hardware and software components
This Special Issue aims to assemble original research and comprehensive reviews on cutting-edge advances, analytical frameworks, technological solutions, sensing-oriented applications, and emerging challenges in the theory and implementation of cyber-physical networks.
Potential topics of interest include, but are not limited to:
- Adaptive and robust control for sensor networks.
- Energy-efficient control for multilayer/multiplex networks.
- Machine learning for network control.
- Optimal control for higher-order dynamical networks.
- Time synchronization protocols for sensor
- Control for chaotic memristive neural circuits for neuromorphic sensing.
- Controllability for data-driven autonomous vehicle networks.
- Controllability robustness under attacks.
- Machine learning for sensor node cooperation.
- Control for quantum and optical networks.
Prof. Dr. Xiaofang Hu
Dr. Jie Wu
Dr. Bochun Wu
Guest Editors
Jiening Wu
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- sensor networks
- cyber-physical networks
- sensor nodes
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