IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT)

3-5 Dec 2025 · Osaka, Japan.

http://www.ieee-qcnc.org/2025

Track 3: Edge, Cloud, and Fog Computing in IoT

Track Chairs:
PChih-Yu Wang, Academia Sinica, Taiwan, cywang@citi.sinica.edu.tw
Anna Maria Vegni, Roma Tre University, Italy, annamaria.vegni@uniroma3.it
Cristian-Nicolae Buțincu, Gheorghe Asachi Technical University, Romania, cristian-nicolae.butincu@academic.tuiasi.ro

Description

The “Edge, Cloud, and Fog Computing in IoT” track explores the integration and evolution of different computing paradigms in the field of the Internet of Things (IoT). As IoT devices continue to proliferate across industries, from smart homes and healthcare to manufacturing and transportation, the demand for efficient, scalable, and low-latency computing solutions is greater than ever. These computing models are transforming how data is processed, stored, and transmitted in IoT systems, significantly enhancing efficiency, scalability, and responsiveness. Edge computing brings computation closer to the data source, reducing latency and improving real-time processing. Cloud computing offers vast resources for data storage and powerful processing capabilities. Fog computing acts as an intermediary layer between the edge and the cloud, enabling more efficient data flow and reducing network congestion. This track aims to provide a comprehensive look at the role of edge, cloud, and fog computing in driving the future of IoT, offering valuable insights for both researchers and industry professionals. It will highlight cutting-edge research, real-world applications, and emerging trends that cover how these complementary computing models can be used to enhance IoT capabilities, optimize resource management, and enable efficient decision-making processes. It will focus on the challenges and solutions for implementing edge, cloud, and fog computing in IoT environments, along with best practices and frameworks that facilitate their integration.

Track Topics

• Resource management and allocation in Edge-Fog-Cloud for IoT
• Joint scheduling and optimization of networking and distributed computing resources for IoT
• Edge/fog computing and network services architecture for IoT
• IoT Middleware for cloud/fog computing applications
• Resource slicing in the IoT computing continuum
• Autonomic distributed service and network management
• Business models for the IoT computing continuum
• QoS/QoE management for static and mobile IoT applications in Edge-Fog-Cloud
• Machine learning and distributed learning for edge-fog-cloud resource management for IoT
• Distributed learning deployment, management and applications for IoT
• DNN Partitioning and Offloading in Edge-Fog-Cloud for IoT
• AI Models (including LLMs) with edge-fog-cloud computing: inference and training
• IoT-enabled Edge Intelligence
• AI empowered autonomous edge intelligence for IoT
• Edge Computing Architectures and Protocols for IoT
• Cloud and Fog Computing for Scalable IoT Systems
• Fog Computing as an IoT Middleware
• Fog and Edge Networking in IoT
• Security and Privacy for IoT in Edge, Cloud, and Fog Computing
• Latency Optimization and Real-time Processing in IoT
• AI and Machine Learning in Edge, Cloud, and Fog IoT
• IoT Data Management and Distributed Storage
• Energy-Efficient IoT Systems with Edge and Fog Computing
• IoT Integration with 5G and Next-Generation Networks
• IoT in Smart Cities: Leveraging Edge, Cloud, and Fog Computing
• IoT Application Case Studies
• Industrial IoT (IIoT) and Edge, Cloud and Fog Computing
• Interoperability and Standardization in IoT Computing Models
• Next-Generation Computing Models for IoT

Paper Submission and Publication

Details of paper submission and publication can be found here.