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

14-16 Dec 2026 · Kobe, Japan.

http://www.ieee-aiot.org/2026

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

Track Chairs:
Marica Amadeo, University of Messina, Italy, marica.amadeo@unime.it
Bo Yang, Northwestern Polytechnical University, China, yang_bo@nwpu.edu.cn
Roberto Morabito, EURECOM, France, roberto.morabito@eurecom.fr

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. In parallel, the tight coupling of distributed computing and artificial intelligence across the continuum is giving rise to edge intelligence, where AI-empowered edge/fog nodes process and reason over IoT-generated data, enabling autonomous and low-latency decision-making closer to the source.

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
• 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
• Federated learning deployment, management and applications for Edge IoT
• DNN Partitioning and Offloading in Edge-Fog-Cloud for IoT
• AI Models (including LLMs) with Edge-Fog-Cloud computing: inference and training
• Generative AI and Foundation Models at the Edge for IoT applications
• IoT-enabled Edge Intelligence
• Fog and Edge Networking in IoT
• In-network computing and caching strategies for IoT data streams
• Information-Centric Networking (ICN) for Edge-Fog-Cloud IoT systems
• Security and Privacy for IoT in Edge, Cloud, and Fog Computing
• IoT Data Management and Distributed Storage
• Energy-Efficient IoT Systems with Edge and Fog Computing
• IoT in Smart Cities: Leveraging Edge, Cloud, and Fog Computing
• Industrial IoT (IIoT) and Edge, Cloud and Fog Computing
• IoT Application Case Studies
• Interoperability and Standardization in IoT Computing Models

Paper Submission and Publication

Details of paper submission and publication can be found here.