Guest Editor(s)
Dr. Salil Bharany
Email: salil.bharany@gmail.com
Affiliation: Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
Homepage:
Research Interests: explainable artificial Intelligence (XAI) for IoT and edge computing, edge intelligence and edge AI, wireless sensor networks (WSNs), internet of things (IoT) and internet of medical things (IoMT), tinyML and resource-constrained AI, federated learning for edge devices
Prof. Rupali Gill
Email: rupali.gill@chitkara.edu.in
Affiliation: Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
Homepage:
Research Interests: federated learning for edge devices, wireless sensor networks (WSNs)
Prof. Ashraf Osman Ibrahim
Email: ashraf@utp.edu.my
Affiliation: Department of Computing, Universiti Teknologi PETRONAS, Seri Iskander, Malaysia
Homepage:
Research Interests: energy-efficient and green IoT systems, intelligent routing and resource optimization, autonomous and self-adaptive sensor networks, edge security, privacy, and trustworthy AI, sensor fusion and context-aware computing
Prof. Durgesh Srivastava
Email: drdkumar.ptu@gmail.com
Affiliation: Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
Homepage:
Research Interests: remote patient monitoring, smart cities and intelligent infrastructure, industrial IoT (IIoT) and industry 5.0, intelligent environmental and agricultural monitoring
Summary
The rapid advancement of Wireless Sensor Networks (WSNs), the Internet of Things (IoT), and edge computing is transforming intelligent systems across healthcare, smart cities, industrial automation, agriculture, and environmental monitoring. As intelligent services increasingly migrate toward the network edge, there is a growing need for explainable, energy-efficient, secure, and trustworthy AI techniques capable of supporting real-time decision-making in resource-constrained environments.
This Special Issue aims to provide a multidisciplinary platform for researchers and practitioners to present cutting-edge advances in Explainable Edge Intelligence for WSNs and IoT. The issue seeks original research addressing novel architectures, intelligent optimization algorithms, lightweight AI models, secure communication frameworks, and scalable edge computing solutions that improve the reliability, transparency, and efficiency of next-generation sensing systems. Emphasis will be placed on innovative methodologies that bridge artificial intelligence, edge intelligence, networking, and intelligent applications while addressing practical challenges such as energy efficiency, latency, security, privacy, and interoperability. Contributions presenting theoretical developments, computational models, real-world implementations, and emerging applications are particularly encouraged.
Suggested themes include:
· Explainable Artificial Intelligence (XAI) for Edge Computing, IoT, and Wireless Sensor Networks
· Edge Intelligence, TinyML, Federated Learning, and Resource-Constrained AI
· Energy-Efficient Routing, Resource Optimization, and Self-Adaptive Wireless Sensor Networks
· Secure, Privacy-Preserving, and Trustworthy Edge AI and IoT Systems
· Intelligent IoT Applications for Smart Healthcare, Smart Cities, Industry 5.0, Agriculture, and Environmental Monitoring
Keywords
explainable artificial intelligence (XAI), edge intelligence, edge computing, wireless sensor networks (WSNs), internet of things (IoT), internet of medical things (IoMT), federated learning, tinyML, energy-efficient computing, intelligent sensing systems
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