Guest Editor(s)
Prof. Chun-Wei Tsai
Email: cwtsai@mail.cse.nsysu.edu.tw
Affiliation: Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan
Homepage:
Research Interests: computational intelligence, data mining, cloud computing, internet of things
Prof. Gábor Szűcs
Email: szucs@tmit.bme.hu
Affiliation: Department of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, Budapest, Hungary
Homepage:
Research Interests: artificial intelligent, deep learning, multimedia, data mining, content technologies, explainable artificial intelligence (XAI)
Prof. Rafael Kaliski
Email: rkaliski@cse.nsysu.edu.tw
Affiliation: Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan
Homepage:
Research Interests: internet of things, artificial intelligent, game theory, wireless communications, cybersecurity, multimedia
Dr. Rokas Gipiškis
Email: rokas.gipiskis@mif.vu.lt
Affiliation: Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania
Homepage:
Research Interests: explainable AI, model interpretability, AI safety, risk management
Summary
With recent advances in artificial intelligence (AI), an increasing number of intelligent mechanisms, algorithms, and systems have been developed to deliver better services for the Internet of Things (IoT). Among these, Agentic AI has emerged as a promising research direction across various domains. Unlike conventional AI models that passively respond to queries, Agentic AI systems can autonomously perceive, reason, plan, and act to accomplish complex goals with minimal human intervention, making them particularly suitable for dynamic and distributed Artificial Intelligence of Things (AIoT) environments. However, three key research challenges remain in realizing Agentic AI for AIoT: (1) how to determine which AI technologies should be integrated into agentic systems and how to assess the impacts of such integration; (2) how to reduce the computational costs and model sizes of AI agents, given that most current AI models exceed the resource constraints of typical IoT devices; and (3) how to leverage Agentic AI to enhance the performance of existing information systems or to enable novel applications. This Special Issue aims to advance Agentic AI and related technologies for the IoT. It welcomes original contributions on AI Agents, Agentic AI, multi-agent systems, explainable AI, tiny machine learning, and large language models, with an emphasis on agent-based approaches that enhance the performance of IoT systems and applications.
Topics of interest include, but are not limited to:
· Architectures and Frameworks of Agentic AI for AIoT
· Autonomous AI Agents on Resource-Constrained IoT Devices
· Multi-Agent Systems and Agent Collaboration for AIoT
· Agent Migration and Negotiation Mechanisms on IoT Devices
· AI Model Refinement and Optimization for AIoT
· Lightweight and Energy-Efficient AI Agents for IoT Devices
· Large Language Models for Agentic AI in AIoT
· Explainable AI for Agentic AI in AIoT
· Security and Privacy of Agentic AI in AIoT
· Agentic AI for Edge and Fog Computing
· Evaluation and Benchmarking of Agentic AI in AIoT
· Applications of Agentic AI in AIoT
Keywords
agentic AI; AI agents; multi-agent systems; artificial intelligence of things; large language models; tiny machine learning; explainable AI; security; privacy; benchmarking; edge computing; fog computing
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