Guest Editor(s)
Assoc. Prof. Joongjin Kook
Email: kook@smu.ac.kr
Affiliation: Deptartment of Information Security Engineering, Sangmyung University, Cheonan, South Korea
Homepage:
Research Interests: on-device AI, physical AI, cyber security, embedded system, wireless sensor network
Assoc. Prof. Hyungbae Park
Email: hpark@ung.edu
Affiliation: University of North Georgia, Oakwood, United States
Homepage:
Research Interests: artificial intelligence (AI), machine learning, software engineering methodology, data mining, wireless sensor networking
Dr. Jaehoon Ahn
Email: corehun@keti.re.kr
Affiliation: Convergence System Research Division, Intelligent IDC Project Office, Korea Electronics Technology Institute, Seongnam-si, Republic of Korea
Homepage:
Research Interests: artificial intelligence (AI), machine learning, on-device AI, physical AI, cloud native, embedded system
Summary
Wireless sensor networks (WSNs) are fundamental components of Internet of Things, smart city, industrial automation, environmental monitoring, healthcare, and intelligent transportation applications. However, their performance is constrained by limited energy, computation, communication bandwidth, and dynamic network conditions, making artificial intelligence an increasingly important tool for improving network efficiency, reliability, adaptability, and longevity.
This Special Issue aims to present recent advances in artificial intelligence-based techniques for improving the performance of wireless sensor networks. It welcomes original research and review articles on machine learning, deep learning, reinforcement learning, federated learning, and lightweight AI models for energy management, routing, clustering, resource allocation, anomaly detection, localization, and network optimization. Particular attention is given to practical, energy-efficient, secure, and adaptive AI solutions that address resource constraints, dynamic environments, scalability, and real-time operation in WSN and IoT systems.
Suggested themes
· AI-Based Energy-Efficient Routing and Clustering in WSNs
· Machine Learning and Deep Learning for Network Performance Optimization
· Reinforcement Learning-Based Resource Allocation and Adaptive Communication
· Lightweight and On-Device AI for Resource-Constrained Sensor Nodes
· Federated and Distributed Learning in Wireless Sensor and IoT Networks
· AI-Enabled Security, Intrusion Detection, and Fault Diagnosis in WSNs
· Intelligent WSN Applications for Smart Cities, Industry, Agriculture, and Environmental Monitoring
Graphic Abstract
Keywords
wireless sensor networks, artificial intelligence, energy efficiency, intelligent routing, network optimization
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