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

14-16 Dec 2026 · Kobe, Japan.

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

Call for Papers

IEEE AIoT 2026 aims at bringing together interested parties (universities, research centers, industries and stakeholders) from around the world working in the fields of AI and Internet of Things to exchange opinions, discuss brand-new ideas, developing innovative and emerging solutions, and establishing new collaborations.

Scope and Objectives

Artificial Intelligence (AI) and the Internet of Things (IoT) are two of the most rapidly evolving and interdependent fields of technology. The convergence of these two fields is leading to the creation of an emerging interdiscipline field, dubbed the Artificial Intelligence of Things (AIoT). The AIoT represents the integration of AI technologies into IoT systems and devices, enabling them to interact with their environment in more sophisticated and intelligent ways. By combining IoT with AI, the data collected by distributed nodes can be utilized by applying AI techniques such as machine learning and deep learning. As a result, machine learning capabilities are moved closer to the data source.

The IEEE AIoT Conference aims to explore the integration of artificial intelligence (AI) technologies into the Internet of Things (IoT) systems and devices, enabling them to interact with their environment in more sophisticated and intelligent ways. The conference will focus on the following areas:

• AIoT Architectures, Frameworks, and Algorithms: Developing novel AIoT architectures, frameworks, and algorithms for deploying state-of-the-art machine learning and deep learning algorithms on IoT and edge devices, realising the so-called Edge AI or Edge Intelligence;
• AIoT Applications: Identifying and exploring new AIoT applications, including healthcare, smart homes, industrial automation, transportation, and digital agriculture;
• Standards and Interoperability: Developing standards and protocols for AIoT systems and ensuring interoperability between different platforms and devices; and
• Ethics and Security: Addressing ethical and security concerns related to AIoT, such as privacy, transparency, and accountability.

Authors are cordially invited to submit their original papers within the artificial intelligence of things area. The topics include but are not limited to:

1. Edge Computing and Intelligence in AI and IoT
2. Machine Learning for IoT Applications
3. Mobile deployment of Large Language Models (LLMs)
4. LLMs for AIoT applications
5. Smart Cities: AI and IoT Solutions
6. Security and Privacy in AI-driven IoT Systems
7. 5G and its Impact on AI and IoT
8. Blockchain Technology for Securing IoT Devices
9. Human-Machine Interaction in IoT Environments
10. IoT Sensors and Actuators: Innovations and Advances
11. AI-driven Predictive Maintenance in IoT
12. Energy-Efficient AI Algorithms for IoT Devices
13. IoT in Healthcare: Applications and Challenges
14. Industrial IoT (IIoT) and AI for Manufacturing
15. Smart Agriculture: AI and IoT in Precision Farming
16. Ethical Considerations in AI-powered IoT Systems
17. IoT Standards and Interoperability
18. Robotic Process Automation (RPA) in IoT
19. AI-driven Automation in Supply Chain Management
20. IoT Analytics and Big Data Processing
21. AI in Edge Devices: Challenges and Solutions
22. Wireless Sensor Networks in AI and IoT
23. IoT for Environmental Monitoring and Sustainability
24. AI and IoT in Transportation and Logistics
25. Cross-domain Integration of AI and IoT Technologies

Track 1: Agentic AI and IoT(Track CFP)

• Foundations and architectures of agentic AI for IoT systems
• LLM-based agents and multimodal foundation models for IoT
• Multi-agent systems, coordination, negotiation and collaboration in IoT environments
• Tool use, function calling and agent orchestration for IoT devices and services
• Planning, reasoning, and decision-making for embodied AIoT agents
• Perception-reasoning-action loop in agentic AIoT systems
• Memory, reflection, and long-horizon autonomy in IoT agents
• On-device and edge deployment of agentic AI
• Communication protocols and semantic communication for agent-to-agent (A2A) interaction in IoT
• Agentic AI for integrated sensing, communication, and control
• Agentic AI for network management, self-organization, and self-healing in IoT
• Digital twins and agentic AI for real-time IoT orchestration
• Human-agent collaboration and human-in-the-loop AIoT systems
• Safety, alignment, and guardrails for autonomous agents acting on the physical world
• Trust, accountability, and explainability of agentic AIoT systems
• Security and privacy threats specific to agentic AI in IoT
• Verification, validation, and benchmarking of agentic AIoT systems
• Energy-efficient and sustainable agentic AI for IoT
• Federated and continual learning for distributed IoT agents
• Agentic AI for autonomous vehicles, drones, and robotics in IoT contexts
• Agentic AI for smart cities, smart manufacturing (Industry 5.0), and smart healthcare
• Standards, governance, and regulatory considerations for agentic AIoT
• Datasets, simulators, and testbeds for agentic AIoT research
• Case studies and real-world deployments of agentic AI in IoT

Track 2: AI for IoT Communications and Networking (Track CFP)

• AI-native communication architectures for IoT and 6G
• AI-driven network slicing and orchestration
• Quantum-enabled Digital Twin frameworks for future IoT systems
• Real-time Digital Twin architectures for IoT and 6G networks
• Semantic communications for intelligent IoT systems
• Integrated sensing and communication (ISAC) for IoT
• AI-assisted mobility, handover, and topology management
• Intelligent access selection and connectivity management
• Multi-agent Digital Twin intelligence and coordination
• AI-enabled ultra-reliable and low-latency communications (URLLC)
• AI for heterogeneous and interoperable IoT networks
• Edge AI and distributed intelligence for IoT
• Federated and collaborative learning for IoT networking
• TinyML for resource-constrained IoT systems
• AI-assisted cloud-edge-IoT resource orchestration
• Distributed AI for large-scale IoT environments
• AI-enabled data offloading and task scheduling
• Reinforcement learning for autonomous network optimization
• AI-driven routing and congestion control
• AI-enabled self-organizing and self-healing networks
• Generative AI and LLMs for network automation
• Multi-agent AI for IoT communications
• AI-based QoS/QoE optimization
• AI-assisted adaptive MAC and radio resource management
• AI for cybersecurity and cyber resilience in IoT networks
• Trustworthy, explainable, and privacy-preserving AI
• AI-enabled intrusion and anomaly detection
• AI-based energy-efficient and green IoT networking
• Secure federated learning and distributed AI systems
• AI-enabled industrial IoT and private 5G/6G systems
• AI for vehicular IoT and V2X communications
• AI-enabled UAV and aerial IoT networking
• AI-driven smart city communication systems
• Digital twins for IoT communications and networking
• Experimental testbeds, real-world deployments, and industrial experiences

Track 3: Edge, Cloud, and Fog Computing in IoT (Track CFP)

• 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

Track 4: Security, Trust, Privacy in AI and IoT(Track CFP)

• Agentic AI Governance and Security for Autonomous IoT Systems
• AI Supply Chain Security and Model Provenance in Intelligent IoT
• Post-Quantum Secure AI Architectures for IoT Networks
• Secure AI Agents and Multi-Agent Collaboration in IoT Environments
• AI-powered Deepfake, Synthetic Media, and Identity Attack Detection for IoT
• TinyML Security and Trustworthy On-Device Learning for Edge IoT
• AI-enhanced Authentication and Access Control for IoT
• AI for Truth Verification and Management in IoT
• AI for Data Privacy in IoT Devices and Services
• Federated Learning in IoT Security
• Edge-Deployed AI Security in IoT
• Incentive Strategies for AI Interaction in IoT
• AI Applications for Smart City IoT Security
• Data Security for AI-powered IoT
• Privacy-enhancing Technologies in Intelligent IoT Systems
• AI-enabled Attacks and Defenses for IoT Devices and Services
• AI for Communication Security of IoT Devices
• Malware Analysis for Intelligent IoT
• Vulnerability Analysis for AI-integrated IoT Devices
• Intelligent Forensics Tools, Techniques, and Procedures for IoT
• Emerging Data Bias Security Issues in Intelligent IoT Systems
• Lightweight Hardware Verification in Intelligent IoT Systems
• Side-Channel Attacks and Defense in Intelligent IoT Systems
• The implications of machine unlearning for security, trust, and privacy in AI and IoT

Track 5: Ubiquitous IoT: Space, Air, Ground, and Sea (Track CFP)

• AI-Enabled Ubiquitous IoT Architectures across Space-Air-Ground-Sea Networks
• Edge AI and Distributed Intelligence for IoT and OT Systems
• Federated Learning and Collaborative AI for Ubiquitous IoT
• Secure and Resilient AI-Driven IoT Infrastructures
• Cybersecurity, Trust, and Zero-Trust Architectures for IoT/OT
• AI for Autonomous Unmanned Systems and Robotics
• Large Language Models (LLMs) and Foundation Models for Ubiquitous IoT and OT Systems
• Integrated Sensing, Communication, and Computation (ISAC) for AI-Enabled Ubiquitous IoT Systems
• Digital Twins and Intelligent Cyber-Physical Systems
• Low-Latency and Ultra-Reliable Communications for AIoT
• Space-Based IoT, Satellite Networks, and Non-Terrestrial Communications
• UAV-, Maritime-, and Vehicular-Assisted IoT Networks
• AI-Driven Resource Management and Network Optimization
• Semantic Communications and Intelligent Data Processing for IoT
• Energy-Efficient and Sustainable AIoT Systems
• Privacy Preservation and Trustworthy AI in IoT/OT Environments
• Multi-Modal Sensing, Data Fusion, and Context-Aware Intelligence
• AI for Critical Infrastructure Protection and Smart Industry
• Interoperability, Standardization, and Integration of Legacy OT Systems
• Emerging Applications of AI-Enabled Ubiquitous IoT in Smart Cities, Transportation, Defence, Healthcare, and Logistics

Track 6: Big Data Analytics and IoT Applications (Track CFP)

• Scalable data analytics for IoT ecosystems
• Real-time data processing and stream analytics
• Smart healthcare applications with IoT and big data
• Advanced data mining techniques for massive data
• Generative AI and Large Language Models (LLMs) for IoT analytics
• Federated learning and distributed intelligence in IoT environments
• Digital twins for smart infrastructure and industrial systems
• TinyML and resource-efficient AI for edge IoT devices
• Autonomous systems and intelligent robotics using IoT analytics
• Sustainable and green IoT architectures
• Energy-efficient big data processing frameworks
• Explainable AI (XAI) for IoT decision-making systems
• Quantum computing approaches for big data analytics
• Semantic web, knowledge graphs, and intelligent IoT interoperability
• Multi-modal data fusion for heterogeneous IoT data sources
• AIoT (Artificial Intelligence of Things) applications and frameworks
• 5G/6G-enabled IoT communication and analytics
• Cloud-native architectures for scalable IoT platforms
• Human-centered IoT systems and user behavior analytics
• Trustworthy, ethical, and responsible AI in IoT systems

Submission Procedures

Submitted manuscripts must be prepared according to IEEE Computer Society Proceedings Format (double column, 10pt font, letter paper) and submitted in the PDF format. The manuscript submitted for review should be no longer than 8 pages. After the manuscript is accepted, the camera-ready paper may have up to 10 pages, subject to an additional fee per extra page. Manuscripts should be submitted to one of the research tracks. Submitted manuscripts must not contain previously published material or be under consideration for publication in another conference or journal at the time of submission. The accepted papers will be included in IEEE Xplore.

Paper Submission and Publication

Details of paper submission and publication can be found here.

Organization Committee

Details of organization committee can be found here.

Important Dates

• August 1, 2026

  Paper Submission Due

• August 24, 2026

  Paper Submission Due

• October 16, 2026

  Notification of Acceptance

• November 16, 2026

  Final Manuscript (Camera Ready)