2026 6th International Conference on Internet of Things and Machine Learning (IoTML 2026) provides a premier forum for the presentation of new advances and research results in the fields of Internet of Things and Machine Learning. The conference will bring together leading researchers, social workers and scientists in the domain of interest from around the world.
The topics of interest for submission include, but are not limited to:
◕ Connectivity, Security, and Privacy for IoT
IoT architecture with 5G and beyond networks
Multiple access IoT access networks and network backhaul with 5G and beyond
Cooperative communication techniques for IoT
Software defined networking solutions for IoT
Efficient resource allocation schemes, QoS, and QoE in IoT
Energy efficiency and wirelessly powered IoT
Massive connectivity in IoT
Critical and URLLC IoT
oT short range communications
Blockchain solutions for IoT
Cryptography, key management, authentication and authorization for IoT
IoT privacy and security concerns tests, certification, and labelling
IoT security of smart sustainable cities
6G-enabled IoTl
◕ Internet of Things Applications and Services
IoT Big Data
IoT Open Platform
IoT Wearable Devices
IoT Artificial Intelligence
IoT-based Smart City Solutions
IoT-based Smart Homes
IoT-based Robots
IoT Standards and Application Scenarios
IoT-based Autonomous Driving
IoT-based Smart Manufacturing
IoT Artificial Intelligence
Industrial IoT Applications
IoT Data Mining Platforms
Agricultural IoT Applications
Open Service Platforms
Environmental Monitoring
IoT Semantic Services
Intelligent Transportation
Future IoT Open Topics
IoT Testing Platforms and Deployment
◕ Machine Learning and the Internet of Things
Artificial Intelligence
Machine Learning and Analytics
Neural Networks
Communications, Connectivity, and Networks
Computing—From Edge to Cloud
Cybersecurity, Security, and Privacy
Infrastructure, Devices, and Components
Information Processing from Multimedia and Heterogeneous Sources
System Engineering, Integration Approaches, and Operational Techniques
Theoretical Foundations, Design Methods, and Architectures
Network Pattern Recognition and Classification
Machine Learning for Network Slicing Optimization
Machine Learning for 5G Systems
Machine Learning for User Behavior Prediction
Novel Innovator Learning Approaches
Optimizing Machine Learning Methods
Performance Analysis of Machine Learning Algorithms
Experimental Evaluation of Machine Learning
Data Mining in Heterogeneous Networks
Machine Learning for Multimedia
Machine Learning for the Internet of Things
Security and Protection of Machine Learning
Distributed and Decentralized Machine Learning Algorithms
Data Analysis and Mining
Pattern Recognition
Other ...