Guest Editors
Prof. Amir Masoud Rahmani
Email: rahmania@yuntech.edu.tw
Affiliation: Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Taiwan
Homepage:
Research Interests: internet of things (IoT), machine learning, wearable computing, fog/cloud computing, big data
Dr. Seyedeh Yasaman Hosseini Mirmahaleh
Email: yasaman-hosseini@ieee.org
Affiliation: Department of Electrical Engineering, Science and Technology, Lille University, Lille, France
Homepage:
Research Interests: hardware-based deep learning accelerators, network on chip (NoC), audio classification, internet of medical things (IoMT), artificial intelligence (AI), multidisciplinary topics
Dr. Shirin Abbasi
Email: shirin.abbasi@usc.ac.ir
Affiliation: Department of Computer Engineering, University of Science and Culture, Tehran, Iran
Homepage:
Research Interests: internet of things (IoT), internet of vehicles (IoV), artificial intelligence (AI), large language models (LLMs)
Summary
Internet of Things (IoT)–based systems have been widely deployed across diverse domains, including smart cities, healthcare, industry, transportation, and energy. However, the rapid growth in scale, increasing system heterogeneity, limited resource availability, and stringent real-time requirements pose significant challenges to traditional processing and management approaches.
This special issue focuses on reinforcement learning algorithms and related techniques that provide effective solutions for improving the performance of IoT-based systems and applications. The scope includes, but is not limited to, cost reduction, real-time task support, intelligent resource allocation, and optimization of complex IoT operations. The special issue welcomes original research articles and comprehensive survey papers that investigate reinforcement learning, Q-learning, and their variants to address the challenges inherent in IoT environments.
Therefore, this special issue focuses on reinforcement learning algorithms that support IoT systems and enhance their efficiency across various domains. The scope of this special issue is highlighted by the following key subtopics, including but not limited to:
• Reinforcement learning for resource allocation in IoT
• Reinforcement learning for task scheduling and real-time decisions
• Reinforcement learning for data stream processing in IoT
• Reinforcement learning for edge and fog computing in IoT
• Reinforcement learning for adaptive IoT system configuration
• Reinforcement learning for energy- and cost-aware IoT management
• Reinforcement learning for performance optimization of IoT applications
Graphic Abstract
Keywords
internet of things (IoT), IoT-based applications, reinforcement learning, deep learning, optimization, Q-learning
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