Guest Editor(s)
Dr. Syed Rizwan Hassan
Email: syed9919@gachon.ac.kr
Affiliation: Gachon University, Gyeonggi-do, Republic of Korea
Homepage:
Research Interests: wireless sensor networks, artificial intelligence, algorithm design, distributed computing, edge intelligence
Ms. Syeda Sitara Waseem
Email: syedasitarawaseem@gmail.com
Affiliation: Department of Computer Science, Government Sadiq College Women University, Bahawalpur, Pakistan
Homepage:
Research Interests: wireless sensor networks, artificial intelligence, edge intelligence
Dr. Muhammad Waqas Arshad
Email: muhammadwaqas.arsha2@unibo.it
Affiliation: Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
Homepage:
Research Interests: wireless sensor networks, artificial intelligence, cyber security, quantum computing
Summary
The proliferation of Wireless Sensor Networks has paved the way for large-scale sensing and intelligent monitoring in smart cities, industrial systems, the environment, transportation, agriculture, energy systems, and IoT applications. However, traditional architectures for WSNs present more challenges in managing dynamic network environments, cyber threats, limited energy resources, a diversity of devices, and vast quantities of distributed sensing data.
Potential uses of Artificial Intelligence for autonomous network management, anomaly and intrusion detection, intelligent routing, adaptive resource allocation, energy optimization, and context-aware decision-making are all promising areas. Additionally, edge intelligence and federated learning can help to facilitate distributed AI processing, minimizing latency, communication overheads, and reliance on centralized cloud systems.
This Special Issue welcomes original research papers, review papers, and innovative applications on theoretical, algorithmic, architectural, and experimental advances in AI-assisted WSNs. Security, resource optimization, lightweight AI, federated intelligence, and edge computing are especially desired.
Topics of Interest
Topics include, but are not limited to:
• Artificial intelligence and machine learning for Wireless Sensor Networks
• Deep learning and lightweight neural networks for WSNs
• Federated and distributed learning in sensor and IoT networks
• Edge intelligence and edge-assisted WSN architectures
• Intrusion and anomaly detection in WSNs
• AI-enabled cybersecurity and privacy preservation
• Lightweight security mechanisms for resource-constrained sensor nodes
• Intelligent routing and topology optimization
• AI-based energy management and energy-efficient communication
• Resource allocation and scheduling in heterogeneous WSNs
• Reinforcement learning for adaptive network management
• Graph neural networks for sensor and IoT systems
• Transformer and state-space models for sensor-data intelligence
• Communication-efficient and computation-efficient distributed learning
• Model compression, pruning, quantization, and knowledge distillation for edge devices
• Privacy-preserving and trustworthy AI for WSNs
• Blockchain-assisted security and distributed trust management
• Collaborative and swarm intelligence for sensor networks
• AI-enabled localization, tracking, and sensing
• Fault detection, resilience, and self-healing sensor networks
• Digital twins for intelligent sensor and IoT networks
• AI-enabled WSNs for smart cities and smart infrastructure
• Industrial IoT and intelligent manufacturing
• WSN applications in smart grids and cyber-physical systems
• UAV-, vehicular-, and mobile-assisted sensor networks
• Integration of WSNs with 5G/6G, IoT, and edge-cloud systems
• Experimental platforms, testbeds, datasets, and real-world implementations
Keywords
wireless sensor networks, artificial intelligence, edge intelligence, federated learning, internet of things, intrusion detection, resource optimization, energy efficiency, intelligent routing
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