IIKI 2025 - Internet of Things Conference
Technology Track 1: AI and IoT for Energy
Chairs
- Lin Meng, Ritsumeikan University, Japan
- Zenghui Wang, South Africa University, South Africa
- Eric Maluta, University of Venda, South Africa
- Vhutshilo Nekhubvi, University of Venda, South Africa
- Livhuwani Masevhe, University of Venda, South Africa
- Liston Matindife, National University of Science and Technology, Zimbabwe
- Langa Moyo, National University of Science and Technology, Zimbabwe
- Vusumuzi Ncube, National University of Science and Technology, Zimbabwe
Energy demand is rising, infrastructure is aging, and carbon emissions must be reduced. To address these challenges, the energy sector is increasingly embracing the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT). IoT devices collect real-time data from power grids, buildings, and renewable energy sources, while AI analyzes this data to generate actionable insights and intelligent control strategies. This integration enables the optimization of energy consumption, early detection of equipment failures, and effective balancing of supply and demand. By combining pervasive IoT monitoring with AI-driven decision-making, we can build more adaptive, reliable, and sustainable energy systems. Topics for submissions include but are not limited to the following:
- Data-driven predictive maintenance in energy infrastructure
- Biogas production and utilization
- Cybersecurity and privacy in connected energy systems
- AI-driven energy optimization and intelligent control
- Intelligent demand response and load balancing
- Edge and fog computing for distributed energy systems
Technology Track 2: High-performance AI Systems: Innovations and Applications
Chairs
- Dr. Hengyi Li, Zhongyuan University of Technology, China
- Dr. Zhongkui Wang, Ritsumeikan University, Japan
- Dr. Xuebin Yue, Zhongyuan University of Technology, China
- Dr. Ami Tanaka, Ritsumeikan University, Japan
Track program committee
- Dr. Feng Zeng, Central South University, China
- Dr. Aihui Wang, Zhongyuan University of Technology, China
- Dr. Yifei Ge, Zhongyuan University of Technology, China
- Dr. Chengming Liu, Zhengzhou University, China
- Dr. Qi Li, Ritsumeikan University, Japan
- Dr. Pengcheng Liu, York University, UK
Artificial intelligence is increasingly converging into intelligent systems capable of perceiving, reasoning, and acting in complex real-world environments. As AI continues to transform a broad range of sectors, it is increasingly challenged by the need for high-performance, real-time responsiveness, energy efficiency, trustworthiness, and adaptability to complex environments. This track focuses on cutting-edge research aimed at building high-performance AI systems, spanning advancements from model architecture design to system-level deployment and optimization.
Original technical submissions are invited on, but not limited to, the following topics:
- High-performance computing (HPC) for AI, including parallel/distributed frameworks, as well as heterogeneous architectures
- Advanced model compression strategies for reducing computational overhead and memory footprint
- Lifelong learning and self-adaptive AI for non-stationary data and evolving operational contexts
- Edge and embedded AI for low-latency, energy-efficient intelligence in resource-constrained environments
- AI driven technical innovations in domain-specific applications such as healthcare, agriculture, industrial automation
- AI empowered intelligent robotic systems, including perception, control, motion planning, human–robot interaction, and AI-Robot co-optimization
- AI augmented autonomous drones, focusing on intelligent perception, real-time navigation, path planning, and decision-making in dynamic environments
Technology Track 3: Knowledge Engineering, Big Data, and Cloud computing
Chairs
- Junsheng Zhang, Institute of Scientific and Technical Information of China, China
The Internet of Things (IoT), and of services and people, coupled with social networks means a huge increase in data. Analysing Big Data will become a key focus of research, competition, and innovation in the IoT. Processing of Big Data will in the cloud, and data mining will use background knowledge of societal, cultural, and personal trends. Knowledge engineering for better data mining, new approaches to cloud computing for Big Data, and new paradigms for Big Data processing are key topics. The topics in this track includes but are not limited to:
- Engineering of Big Data applications for smart cities
- Parallel and distributed processing approaches in cloud computing
- New algorithms for Big Data processing, and data mining for Big Data intelligence
- Security and privacy in cloud computing and Big Data processing
- Knowledge engineering for Big Data processing and cloud computing
- Context awareness for mobile cloud/pervasive cloud, and Big Data processing
Technology Track 4: Fog Computing and IoT Services
Chairs
- Dr. ZhangBing Zhou, China University of Geosciences (Beijing), China
- Dr. Teng Long, China University of Geosciences (Beijing), China
With the rapid development and wide adoption of the Internet of Things (IoT), traditional device-centric IoT is moving into a new era where ubiquitous IoT resources are encapsulated and represented in terms of smart IoT services.
In this setting, IoT resources at a certain network region are dynamically integrated through innovative IoT services for the realization of the value of interconnected IoT resources and the satisfaction of (near) real-time, intelligent and local user demands, and consequently, for the promotion of IoT intelligence at the edge of the network.
To address this challenge, this research track calls for submissions on the topics including, but not limited to, the following:
- Fog-based IoT and Service Management
- Fog-based IoT and Business Process Management
- Fog-based IoT Infrastructure and Framework
- Fog-based IoT Data Management
- Fog-based IoT Device and Resource Management
- IoT-enabled Edge and Fog Computing
- Fog-based IoT Smart Home
- Fog-cloud Interactions and Protocols
- Security, Privacy and Trust in Fog Environment
- Edge Resource Storage
- Real-time Context -aware Fog Computing
- Network Function Virtualization for Fog Computing in IoT
- Load balancing and Service Selection in Fog Computing for IoT
Technology Track 5: EHealth, Mobile Health, Wellbeing and Sport
Chairs
- Anton Kos, University of Ljubljana, Slovenia
- Anton Umek, University of Ljubljana, Slovenia
“Big data” is endowing the traditional healthcare with mobility, intelligence and convenience, which has given birth to “E-Health & Mobile Health”. In such a mobile health environment, tasks like health monitoring of patients, information exchange between doctors and patients, intelligent diagnosis and information push, etc., can be automatically and rapidly accomplished by analyzing a large number of data collected from various mobile devices. However, such mobile applications face numerous challenges due to the voluminous data and complex procedures. Topics in this track include but are not limited to:
- Advanced data mining technologies for E-Health & Mobile Health
- Machine learning/Deep learning applications in E-Health & Mobile Health
- Smart phone/wearable device based human motion recognition
- Ubiquitous computing in personal health monitoring/architectural structure health monitoring
- Emerging wireless/mobile applications in E-Health & Mobile Health
- Advanced wireless telemedicine and e-health services
Technology Track 6: Artificial Intelligence and Internet of Things
Chairs
- Yu Bai, California State University, Fullerton, USA
- Mingming Xiao, Amazon, USA
Track program committee:
- Dr. Yinjie Huang, Twitter, USA
- Dr. Yu Bai, AMD, USA
- Dr. Yanhui Guo, Shandong Woman University, China
- Dr. Chonglong Li, Shandong Jianzhu University, China
- Dr. Xiaolong Guo, Kansas State University, USA
As The Internet of Things (IoT) continues to be envisioned as the most popular technology, the research of IoT has turned to how to drive value from IoT. While people have enjoyed a treasure trove of big data from IoT, the sheer volume of data being created by the IoT creates a big problem to analyze the deluge of data and information. Recently, the rapid development of artificial intelligence technology encounters great challenges as well as opportunities for IoT.
The proposed track provides the ground for emerging research ideas on how Artificial Intelligence (AI) can make a valuable contribution to solving problems that the Internet of Things. Contributions may come from diverse fields, including artificial intelligence; dependable computing; the Internet of Things; cyber-physical systems; mobile, wearable, and ubiquitous computing; ambient intelligence; architecture.
Original technical submissions on, but not limited to, the following topics are invited:
- Artificial Intelligence and Machine Learning
- Cloud computation for Internet-of-Things (IoT) devices
- Systems for neural computing (including deep neural networks)
- Secure IoT data transfer and storage
- Security, Trust, Privacy and Identity in the IoT
- Bio-inspired IoT and Big Data solutions, including the handling and analysis of data streams
- Interactions between augmented humans and the pervasive computing environment, including intelligent assistants and the IoT (for example, smart cities, homes, and cars)
Technology Track 7: Ubiquitous Sensing and Intelligent Media
Chairs
- Junyu Dong, Ocean University of China, China
- Xiangwei Zheng, Shandong Normal University, China
- Da Yuan, Shandong Normal University, China
Ubiquitous Sensing and Intelligent Media, including environment sensing, Internet of Things (IoT), data or multimedia acquisition, intelligent media processing, harmonious human-computer interaction and pervasive computing have attracted huge interests from researchers. Accordingly, there are a variety of potential application in Ubiquitous Sensing and Intelligent Media. The aim of this track is to survey a state of art of methodologies, algorithms and systems in advanced research into Ubiquitous Sensing and Intelligent Media, which may involve any types of media data such as visual (including 2D, 3D and RGB data), audial, Electroencephalography (EEG)/MRI/CT and touch sensory data etc. Original technical submissions on, but not limited to, the following topics are invited:
- Methods for intelligent information processing/computing: computer vision, graphics, visualization, visual analysis, multimedia storage &editing, multimedia coding and retrieval, object recognition and synthesis of audio and audio, multimedia universal access, etc.
- Methods for harmonious human-computer interaction: intelligent sensing, emotional computing, voice interaction, large-scale surface interaction, brain-computer interface, wear interaction, interaction efficiency and optimization, social network, etc.
- Methods for pervasive computing environment: Internet of Things, pervasive computing mode, active services, embedded systems, situational awareness, smart space, home gateway, etc.
- Systems/applications meeting various practical needs with novel technologies in machine perception and intelligent media.
Technology Track 8: Transforming Healthcare with Artificial Intelligence and Robotics
Chairs
- Xiangbo Kong, Toyama Prefectural University, Japan
- Yuting Geng, Ritsumeikan University, Japan
- Jiaqing Liu, Ritsumeikan University, Japan
With the rapid advancement of Artificial Intelligence (AI) and Robotics, their integration into healthcare is revolutionizing the way we diagnose, treat, and manage diseases. From intelligent diagnostic systems and robotic-assisted surgeries to autonomous rehabilitation and elderly care solutions, these technologies offer advantages such as precision, efficiency, personalization, and scalability. This track focuses on cutting-edge research, practical applications, and emerging trends in the intersection of AI, robotics, and healthcare. We welcome contributions on topics including, but not limited to:
- AI-driven medical imaging
- Wearable health technologies
- Personalized treatment planning
- Image processing for mental health applications
- Image processing for public health and safety
- Signal processing techniques for disease diagnosis
- Motion estimation, registration, and fusion in healthcare