Scope

For over a decade, internet of things (IoT) and Artificial Intelligence (AI) technologies are boosting the competitiveness of organizations in sectors like manufacturing, energy, healthcare, and smart cities. IoT technologies enable organizations to interact with the physical world through cyber-physical systems and internet connected objects towards improving the automation and efficiency of their business processes. At the same time, AI technologies like machine learning and industrial robots facilitate organizations to derive insights from large volumes of structured and unstructured datasets, which helps them to optimize business workflows and to improve the quality of their decisions. Also, recent advances in IoT and AI systems ease the process of collecting and processing information from a large variety of distributed data sources, including sensors, automation devices, smart objects and enterprises databases. Moreover, they ensure secure and scalable information management, as well as the execution of advanced analytics based on high-performance AI techniques like deep learning.

While the above-listed developments provide a solid foundation for developing, deploying and operating Industry 4.0 applications, there have still limitations when it comes to support the new wave of human-centred AI applications of the Industry 5.0. In particular, the development of human centred IoT and AI applications requires an extra layer of trustworthiness that boosts their security, safety and transparency, while at the same time ensuring that these applications are acceptable by humans. In this direction, research in approaches that safeguard and promote trustworthiness at multiple levels is required i.e., from ensure the trustworthiness and integrity of the data, to explaining the operation of AI systems to humans, and to developing AI approaches that foster trusted human-machine collaboration. This novel layer of trustworthiness can nowadays benefit from recent developments in the cloud/edge/IoT continuum (e.g., edge AI approaches that reduce the attack surface of industrial data), from the security and anti-tampering properties of blockchain technologies, as well as from trusted human-centric AI paradigms like explainable AI and neurosymbolic learning.

One of the key challenges that is associated with the development of such systems lies in the development of trusted applications in highly decentralized IoT/AI environment, which use, combine and orchestrate data-driven services from multiple providers. To address this challenge, there is a need for federated architectures that foster trusted and secure information sharing from different platforms, including platforms from different administrative domains that provide diverse security mechanisms and levels of trusted. Moreover, there is a need for novel methodologies for designing IoT and AI systems with the human in-mind i.e., designing applications that adapt to the human interaction loop rather than expecting humans to adapt to the operation of IoT and AI systems.

Topics

In this context, the aim of the 3rd Workshop on Next Generation IoT and AI systems for Trusted, Human-Centered Intelligence (TI-2026), is to present research results on technologies, tools and methods that support the development, deployment, and operation of trustworthy and human-centered IoT/AI systems for different industries such as manufacturing, smart cities, precision farming, and healthcare. The main topics of interest for this workshop include:

• Architectures, tools and techniques for trusted and reliability industrial data.
• Federated Architectures and Data Spaces for Trustworthy data sharing.
• Distributed data management for trusted AI/IoT applications in the cloud/edge continuum.
• Decentralized machine learning techniques for trusted and privacy friendly IoT/AI applications such as Federated Machine Learning and Swarm Intelligence.
• Security, safety and data protection of highly distributed IoT/AI systems.
• Distributed Ledger Technologies (DLT) for trusted human machine interactions.
• Decentralized AI paradigms that foster Human-AI collaboration such as active learning, neuro-symbolic learning and human robot collaborations.
• Novel techniques for Explainable Artificial Intelligence (XAI) and Interpretable Artificial Intelligence (IAI).
• Methods and tools for designing and deploying trusted IoT/AI systems.
• Human-Centered enhancements to industrial IoT/AI technologies (e.g., digital twins, machine learning, systems).
• Trustworthy IoT/AI applications in sectors with clear market relevant such as manufacturing, smart cities, precision farming, energy, and healthcare.

For Authors

Prospective authors are invited to submit high-quality original technical papers reporting original research of theoretical or applied nature for presentation at the workshop and publication in the TI-2026 Proceedings. All papers will be reviewed and evaluated by independent experts and selected based on their originality, merit, and relevance to the workshop. Accepted papers will be published as part of the IEEE DCOSS-IoT 2026 conference proceedings and submitted to IEEE Xplore.

The authors of accepted papers will be required to prepare a presentation in PDF file format and provide it along with the camera-ready manuscript. All presentations will be made publicly available.

The manuscripts must be prepared in English, following the IEEE two-column Manuscript Templates for Conference Proceedings (available here) with a maximum length of eight (8) printed pages including text, figures, and references. Authors may add at most two (2) pages, but only for an appendix, i.e. these two pages contain supplementary material only. The additional two pages will incur overlength charges at $100/page.

Submissions will be made using the EasyChair system. The workshop submission link will be provided shortly