Guest Editors
Prof Tianyi Li
Email: tianyi@cs.aau.dk
Affiliation: Department of Computer Science, Aalborg University, Aalborg, 9220, Denmark
Homepage:
Research Interests: Edge Computing, Data Mining, Distributed Systems
Prof Yushuai Li
Email: yusli@cs.aau.dk
Affiliation: Department of Computer Science, Aalborg University, Aalborg, 9220, Denmark
Homepage:
Research Interests: Distributed Systems, Block Chain, Digital Twins
Prof Peiyuan Guan
Email: peiyuang@ieee.org
Affiliation: Department of Informatics, University of Oslo, Oslo, Norway
Homepage:
Research Interests: Edge Computing, Federated Learning, Reinforcement Learning
Prof Jiawen Kang
Email: kavinkang@gdut.edu.cn
Affiliation: Department of Automation, Guangdong University of Technology, Guangzhou, 510006, China
Homepage:
Research Interests: Edge Computing, Block Chain, IoT
Dr Min Zhang
Email: min.zhang@waikato.ac.nz
Affiliation: School of Engineering Teaching and Research, University of Waikato, Hamilton, 3216, New Zealand
Homepage:
Research Interests: Microgrid, P2P energy trading, digital twin
Summary
Scope and Motivation
The integration of edge computing and the Internet of Things (IoT) is shifting intelligence and data processing from the cloud to the network edge. This shift supports real-time applications in areas like smart cities, industrial automation, and healthcare, where low latency and high quality of service (QoS) are essential.
However, the large scale and limited resources of edge IoT devices create challenges in security, sustainability, and reliability. Traditional cloud security approaches are not suitable for the distributed edge, and energy constraints threaten long-term operation. Optimizing service performance, security, and energy use together is a key research goal.Emerging technologies such as AI, machine learning, digital twins, federated learning, and blockchain offer new ways to build secure and sustainable services across the edge-to-cloud continuum.
This Special Issue focuses on applying Artificial Intelligence to balance security, sustainability, and performance in Edge IoT services. We welcome research on intelligent systems and algorithms that enable autonomous, efficient, and resilient service delivery
Topics of Interest
We invite original research and review articles on secure and sustainable Edge IoT services. Topics include, but are not limited to:
· AI and machine learning for dynamic resource management in edge systems
· Intelligent service placement and workload offloading for low-latency applications
· Reinforcement learning for self-healing and adaptive service networks
· Energy-aware scheduling and task allocation across edge and cloud
· Lightweight cryptography and authentication for resource-limited devices
· Blockchain for service integrity and data provenance
· Federated learning and privacy-preserving collaborative intelligence
· AI-driven intrusion and anomaly detection for service security
· Energy-harvesting system design for long-lasting edge services
· Cross-layer security for service data protection
· Secure service collaboration in federated edge environments
Keywords
edge computing, AI, security, energy efficiency
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