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:
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:
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:
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:
“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:
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:
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:
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:
As ChatGPT ignited the big model craze, the industry entered the era of generative artificial intelligence. As the scale of AI model parameters becomes larger and larger, the demand for computing power of intelligent computing data centers has doubled. Computing power is not only the "engine" of the intelligent era, but also the most valuable resource in the intelligent computing era. First, with the doubling of computing power demand, larger-scale network support is required, which poses new challenges to the network capacity, networking architecture, construction cost, and maintenance cost of data center network construction. Secondly, with the high training cost of AI large models, it is necessary to optimize the underlying issues such as communication, topology, model parallelism, and pipeline parallelism between servers as a whole. Thirdly, with the long training cycle of AI large models, challenges have been posed for enhancing the reliability deployment of intelligent computing networks, improving the accuracy of pre-calculation fault detection, and enhancing the fault detection, demarcation, and visualization capabilities of intelligent computing networks. This track focuses on cutting-edge research, practical applications, and emerging trends in the fields of intelligent computing networks, computing power scheduling, and high-performance computing. We welcome contributions on topics including, but not limited to: