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

14-16 Dec 2026 · Kobe, Japan.

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

Track 6: Big Data Analytics and IoT Applications

Track Chairs:
Qing Yang, University of North Texas, USA, Qing.Yang@unt.edu
Guobin Xu, Morgan State University, USA, guobin.xu@morgan.edu
Weixian Liao, Towson University, USA, wliao@towson.edu

Description

This track focuses on the integration of Big Data Analytics and Internet of Things (IoT) technologies to address real-world challenges, drive innovation, and create actionable insights from connected systems. With the rapid development and growth of IoT devices, sensors, applications, and systems, massive volumes of heterogeneous, real-time, and distributed data are being generated and collected across many domains. Effective big data analytics methods are needed to transform these IoT data into useful information, which can support intelligent decision-making, predictive modeling, automation, service optimization, and system performance improvement. This track aims to explore cutting-edge research, practical applications, and emerging trends in scalable IoT analytics, real-time and stream data processing, AIoT, federated learning, TinyML, digital twins, generative AI and large language models, explainable and trustworthy AI, multi-modal data fusion, semantic interoperability, cloud-native IoT platforms, 5G/6G-enabled analytics, sustainable IoT architectures, and intelligent applications in healthcare, transportation, manufacturing, energy, agriculture, robotics, and human-centered IoT systems.

Track Topics

• 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

Paper Submission and Publication

Details of paper submission and publication can be found here.