IIKI 2026 features seven technology tracks covering a wide range of topics in the Internet of Things, Artificial Intelligence, Health and related fields. Each track is led by distinguished chairs from around the world. Authors are invited to submit original contributions to the track that best fits their research.
The track focuses on artificial intelligence, machine learning, and data analytics techniques for IoT systems. It covers intelligent data processing, predictive modeling, knowledge discovery, and AI-driven decision-making for smart and connected environments.
The rapid growth of IoT systems has led to an unprecedented volume of heterogeneous data generated by interconnected devices and environments. Artificial intelligence and data analytics play a crucial role in transforming this data into actionable insights, enabling intelligent decision-making and autonomous system behavior. Advanced techniques in machine learning, deep learning, and statistical analysis allow IoT systems to identify patterns, predict events, and optimize operations across diverse application domains.
Modern IoT ecosystems require scalable and efficient data processing pipelines capable of handling real-time streams as well as large historical datasets. AI-driven approaches support predictive maintenance, anomaly detection, context-aware services, and adaptive system control, contributing to smarter and more responsive environments. The integration of AI with IoT also raises challenges related to data quality, model interpretability, and deployment in resource-constrained settings.
This track aims to bring together researchers and practitioners working on intelligent data processing and analytics for IoT systems. It emphasizes innovative methodologies, scalable algorithms, and practical applications that leverage data-driven intelligence in connected environments.
Topics of interest include, but are not limited to:
The track addresses computing architectures and platforms that support IoT applications, including cloud computing, edge intelligence, fog systems, and distributed frameworks. Topics include resource management, real-time processing, scalability, and efficient service deployment across heterogeneous IoT infrastructures.
The increasing scale and complexity of IoT systems demand advanced computing infrastructures capable of supporting massive data processing, low-latency communication, and efficient service delivery. Cloud, edge, and distributed computing paradigms provide complementary solutions that enable scalable, flexible, and responsive IoT applications. By distributing computation across centralized cloud platforms and decentralized edge nodes, these architectures support real-time analytics and intelligent services close to data sources.
Edge and fog computing reduce latency and bandwidth usage by processing data near devices, while cloud platforms provide virtually unlimited resources for large-scale analytics and long-term storage. Distributed frameworks further enable coordination across heterogeneous systems, supporting interoperability and resilience in dynamic IoT environments. Efficient resource management, orchestration, and service placement are key challenges in such multi-layered infrastructures.
This track focuses on innovative architectures, platforms, and frameworks that support next-generation IoT applications. It aims to address challenges related to scalability, real-time processing, energy efficiency, and seamless integration across cloud–edge–device continua.
Topics of interest include, but are not limited to:
The track explores the integration of sensing, communication, computation, and control in cyber-physical systems and smart environments. It includes smart homes, smart cities, smart buildings, industrial automation, and intelligent infrastructure enabled by IoT technologies.
Cyber-physical systems (CPS) integrate sensing, computation, communication, and control to enable intelligent interaction between digital systems and the physical world. In IoT-enabled environments, CPS form the backbone of smart applications such as smart cities, buildings, industrial systems, and infrastructure. These systems continuously monitor and respond to real-world conditions, improving efficiency, safety, and sustainability.
Smart environments leverage IoT technologies to create adaptive and context-aware spaces that enhance quality of life and operational performance. Applications range from energy management in smart buildings to traffic optimization in smart cities and automation in industrial settings. The design of CPS requires reliable integration of heterogeneous components, real-time responsiveness, and robust control mechanisms.
This track aims to explore advances in the design, implementation, and deployment of cyber-physical systems and smart environments. It encourages interdisciplinary contributions that address system integration, scalability, and real-world impact.
Topics of interest include, but are not limited to:
The track focuses on secure and trustworthy IoT systems, addressing challenges related to data protection, privacy preservation, authentication, access control, threat detection, and resilient system design. It also welcomes research on trust management and secure communication protocols for IoT ecosystems.
As IoT systems become increasingly pervasive, ensuring their security, privacy, and trustworthiness is a critical challenge. The large number of interconnected devices, often operating in heterogeneous and resource-constrained environments, creates a broad attack surface vulnerable to cyber threats. Protecting sensitive data and ensuring reliable system operation are essential for user trust and widespread adoption.
IoT security encompasses multiple layers, including device authentication, secure communication, data integrity, and system resilience. Privacy-preserving techniques are necessary to protect user data in applications such as healthcare, smart homes, and mobility systems. Trust management frameworks further enable reliable interactions among devices, users, and services in decentralized ecosystems.
This track brings together research on advanced security mechanisms, privacy-enhancing technologies, and trust models for IoT systems. It emphasizes robust, scalable, and lightweight solutions suitable for real-world deployments.
Topics of interest include, but are not limited to:
The track covers intelligent sensing technologies and human-centered IoT applications that enhance interaction between people and connected systems. Topics include multimodal sensing, context awareness, wearable devices, assistive technologies, and user-adaptive intelligent environments.
The evolution of IoT technologies increasingly focuses on human-centered applications that enhance interaction between people and intelligent systems. Advanced sensing technologies enable the capture of physical, physiological, and contextual information, allowing systems to better understand human behavior, preferences, and needs. These capabilities support the development of adaptive and personalized environments.
Multimodal sensing, including vision, audio, and wearable sensors, enables rich data collection for applications such as health monitoring, assistive technologies, and smart living environments. Human-centric IoT systems emphasize usability, accessibility, and user experience, ensuring that technology seamlessly integrates into everyday life while respecting user privacy and autonomy.
This track aims to explore innovative sensing technologies and human-centered IoT applications. It encourages research that bridges sensing, data processing, and human–machine interaction to create intelligent and responsive environments.
Topics of interest include, but are not limited to:
The track welcomes research on IoT-enabled healthcare, mobile health systems, digital wellbeing, and sports technologies. It includes remote monitoring, rehabilitation systems, wearable health devices, personalized health analytics, and intelligent platforms for fitness and human performance.
Digital health technologies are transforming healthcare delivery, wellness management, and athletic performance through the integration of IoT, mobile systems, and data analytics. E-health and mobile health (mHealth) solutions enable continuous monitoring, remote diagnostics, and personalized interventions, improving accessibility and efficiency of healthcare services.
Wearable devices and sensor-based systems provide real-time insights into physiological parameters, supporting preventive care, rehabilitation, and performance optimization. In sports and wellbeing applications, data-driven approaches help track physical activity, enhance training strategies, and promote healthy lifestyles. These technologies also raise important challenges related to data privacy, reliability, and user engagement.
This track focuses on innovative technologies and applications that leverage IoT for healthcare, wellbeing, and sports. It aims to bring together interdisciplinary research addressing both technical and human-centered aspects of digital health systems.
Topics of interest include, but are not limited to:
The track focuses on IoT-related technologies for connected and automated driving, including sensing systems, vehicle connectivity, human-machine interaction, and driver/passenger-centered design. It covers intelligent transportation applications that improve safety, efficiency, and user experience.
Connected and automated driving technologies are transforming the transportation ecosystem by enhancing safety, traffic efficiency, and accessibility, while enabling new mobility services. Advances in sensing technologies, artificial intelligence, and ICT infrastructures allow vehicles to perceive their environment, interpret complex traffic situations, and support or fully automate driving tasks. These developments are expected to significantly reduce accidents caused by human error, improve traffic flow, lower emissions, and provide mobility for broader populations, including elderly and impaired users.
Modern automated vehicles rely on a combination of external perception sensors – such as radar, lidar, cameras, and localization systems – and internal monitoring technologies that observe driver and passenger states. Multimodal in-cabin sensing systems play a key role in monitoring driver attention, fatigue, stress, and readiness to take over control in conditional automation scenarios. Simultaneously, ICT and IoT technologies enable connected vehicles, intelligent infrastructure, and collaborative perception systems that support safer and more efficient traffic environments.
This special track aims to bring together researchers and practitioners from academia and industry to address emerging challenges at the intersection of sensing technologies, vehicle connectivity, and human–vehicle interaction. Particular emphasis will be placed on technologies and methodologies that improve perception robustness, driver monitoring, human–machine interaction, and the safe integration of automated and connected vehicles into real-world traffic.
Topics of interest include, but are not limited to:
The track focuses on theoretical foundations, methodological advances, and applications of big data and artificial intelligence. It covers statistical learning, modeling, optimization, and scalable algorithms for complex data, emphasizing their integration into real-world systems across domains such as finance, healthcare, engineering, and smart infrastructure.
The rapid development of big data and artificial intelligence has fundamentally reshaped the landscape of modern science, engineering, finance, and decision-making. From theoretical foundations to large-scale computational methods and real-world deployment, data-driven intelligence plays an increasingly critical role in understanding complex systems and solving high-dimensional problems across disciplines.
The Shandong Big Data Research Association (SDBDRA) is an academic organization dedicated to advancing research in big data, artificial intelligence, and related interdisciplinary fields. It promotes collaboration among academia, industry, and government, with a focus on bridging theoretical innovation, methodological development, and practical impact.
This special session aims to provide a comprehensive forum for researchers and practitioners to present recent advances in theory, methods, and applications of big data and artificial intelligence. We particularly encourage contributions that emphasize fundamental principles, scalable algorithms, and their integration into complex real-world systems.
Topics of interest include, but are not limited to: