Special Sessions

1. Smart Sensor Networks and Internet of Things

Organizers

• Tie Qiu, Tianjin University, qiutie@ieee.org
• Wenbing Zhao, Cleveland State University, w.zhao1@csuohio.edu

Description

With the rapid development of information technology, Internet of Things (IoT) and Intelligent sensing technologies have become hot topic. In the large scale Sensor Networks, the massive sensing data with complex in structures, high dimensional, distributed, and heterogamous are growing rapidly. It brings new opportunities to data owner and challenges to data researchers. These challenges are distinguished and require new computational paradigm. How to build an efficient IoT architecture and get the large scale sensing data has become the emerging research focuses.

This Special Session seeks original research papers as well as review papers on Sensor Networks and Internet of Things that include topics of interest, but are not limited to:

2. AI-Driven Human/Humanoid Robot Collaboration and Applications

Organizer

• Jing Ma, Auckland University of Technology, jing.ma@aut.ac.nz
• Luyang Hou, Fuyao University of Science and Technology, Luyang.hou@fyust.edu.cn
• Yanjun Shi, Dalian University of Technology, syj@ieee.org

Description

This special session aims to bring together researchers and practitioners from academia and industry to present their latest advancements in intelligent systems and robotics, with a particular focus on AI-integrated intelligent systems, including mobile robots, explainable artificial intelligence (XAI), and AI applications in software engineering. As intelligent agents and robots increasingly operate in dynamic, human-centric environments—from automated warehouses to software testing pipelines—challenges in explainability, adaptability, and coordination become central to their development and deployment. The session invites contributions that address these challenges through novel AI methods, system architectures, and real-world applications, especially in contexts such as smart logistics, human-robot collaboration, multi-agent systems, and AI-enhanced development environments.

Topics of Interest include (but are not limited to):

3. Adaptive Collaboration Systems

Organizer

• Haibin Zhu, Nipissing University, haibinz@nipissingu.ca

Description

Adaptability is a common and typical property for natural systems in the real world. It is also an important and desirable property for computer supported artificial distributed intelligent systems. Adaptive collaboration system can be viewed as a set of interacting intelligent agents, real or abstract, forming an integrated system that is able to respond to internal and environmental changes. Feedback is a key feature of adaptive systems, enabling the response to changes. Artificial systems can be made adaptive using feedback to sense new conditions in the environment and adapt accordingly. Distributed adaptive systems can find applications in almost all industrial sectors, particularly in aerospace, automotive, and manufacturing.

4. Collaborative Technologies For Ageing Societies

Organizers

• Prof. Govindarajan Usharani Hareesh, Business School, University of Shanghai for Science and Technology, Shanghai, China, Hareesh.pillai@usst.edu.cn
• Prof. Marcin Pawel Jarzebski, Sustainable Society Design Center, Graduate School of Frontier Sciences, the University of Tokyo, marcin.jarzebski@edu.k.u-tokyo.ac.jp
• Prof. Martijn ten Bhömer, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China, martijn.tenbhomer@xjtlu.edu.cn
• Dr. Gagan Narang, Vision Robotics and Artificial Intelligence Lab, Università Politecnica delle Marche, Ancona, Italy, g.narang@pm.univpm.it

Description

Collaborative technologies are redefining how societies address ageing, accessibility, and mobility. With longer lifespans and demographic shifts, innovation is vital to sustain independence, participation, and intergenerational exchange. This theme explores the intersection of artificial intelligence, cyber-physical systems, and human-centered design in creating environments that empower older adults and persons with disabilities. Core interests include embodied intelligence, assistive robotics, digital twins, barrier-free mobility, and AI-driven accessibility systems enhancing autonomy and social connection. The scope extends from theoretical models to real-world applications, highlighting scalable, ethical, and human-aware design approaches. It also values cross-sector engagement among academia, industry, and policymakers to ensure adoption and impact. Emphasis is placed on co-design and data-driven governance to integrate ageing, disability, and mobility into everyday life, linking engineering, computing, design, ethics, and social sciences toward inclusive, age-friendly innovation.

5. Knowledge-driven Big Data Computing and Its Applications

Organizers

• Ling Wang, Northeast Electric Power University, smile2867ling@neepu.edu.cn
• Keun Ho Ryu, Chungbuk National University, khryu@chungbuk.ac.kr
• Lei Kou, Qilu University of Technology (Shandong Academy of Sciences), koulei1991@qlu.edu.cn
• Wei Ding, Qilu University of Technology (Shandong Academy of Sciences), dingw@sdas.org
• Teng Li, Shandong University, li.teng@sdu.edu.cn

Description

Knowledge Driven is uplevel computing of basic big data, which aims to construct a sustainably knowledge upgraded structure by valuable rules discovery continuously. It can support more complex knowledge decisions through more widely knowledge cross computation, such as understandability interaction, potential valuable relevance mining, interest-aware computing, and etc. This special session will discuss recent advanced in knowledge-driven big data computing and its applications.

The topics of interest include, but are not limited to:

6. AI and Industrial Intelligence: Toward Sustainable Industry and Ocean Systems

Organizer

• Xu Cheng, Tianjin University of Technology, xu.cheng@ieee.org
• Tianjiao Li, Tianjin University of Technology, litianjiao@ieee.org
• Xiufeng Liu, Technical University of Denmark, xiuli@dtu.dk

Description

The accelerating evolution of Industry 4.0 has ushered in a new era of intelligent, interconnected, and autonomous industrial systems. With the integration of Artificial Intelligence (AI), Internet of Things (IoT), Digital Twin (DT), and Big Data analytics, industries are transitioning toward Industrial Intelligence—a paradigm that enables predictive maintenance, adaptive decision-making, and cross-domain optimization across manufacturing, energy, and marine sectors. In parallel, the Ocean 4.0 concept has emerged, applying the same principles to maritime and offshore industries. The convergence of industrial intelligence and ocean intelligence is fostering transformative applications in smart shipping, autonomous ocean observation, offshore renewable energy, and digital twin–enabled marine operations. AI-driven models are increasingly used for sea state estimation, ship energy optimization, and safety-critical offshore operations, bridging the gap between industrial automation and sustainable ocean development. However, these advancements also raise new challenges. Industrial and maritime systems must manage massive multimodal data streams, ensure cybersecurity and trust, address model interpretability, and adapt to dynamic and uncertain environments. Moreover, the global shift toward carbon neutrality and sustainability demands intelligent frameworks that integrate energy efficiency, low-carbon operation, and resilience to extreme conditions—both on land and at sea. This special issue aims to unite cross-disciplinary research on AI-driven Industrial and Ocean Intelligence, exploring novel theories, algorithms, and engineering applications that advance the integration of Industry 4.0 and Ocean 4.0 toward a sustainable and resilient future.

Topics of Interest (include but are not limited to):

7. Safeguarding Creative Works: Technologies, Authentication, and Trust in the Age of AI

Organizers

• Yihong Wang, Xi’an Jiaotong-Liverpool University, Yihong.wang@xjtlu.edu.cn
• Zhijie Xu, Xi’an Jiaotong-Liverpool University, Zhijie.xu@xjtlu.edu.cn
• Di Wu, Norwegian University of Science and Technology, di.wu@ntnu.no

Description

The proliferation of artificial intelligence and advanced digital technologies has fundamentally transformed how creative works are produced, distributed, and consumed. Simultaneously, these technologies present unprecedented challenges to creators: unauthorized reproduction, style imitation, deepfakes, and non-consensual AI training pose existential threats to intellectual property, artistic integrity, and creator livelihoods. This Special Issue solicits original research articles, system papers, and perspectives at the intersection of content protection, digital rights management, forensic authentication, and trustworthy computing to advance the science and practice of creator work safeguarding. We welcome contributions across multiple creative domains—including but not limited to music, visual arts, literature, code, design, animation, and multimedia—that address the technical, legal, ethical, and human-centered dimensions of creative protection. We are particularly interested in works that combine innovations in cryptography, machine learning, digital forensics, and distributed systems with insights from law, ethics, HCI, and creative practice. Specific topics of interest include: robust watermarking and fingerprinting techniques resilient to AI-based attacks; content authentication and provenance tracking mechanisms; detection and attribution methods for AI-generated or AI-manipulated content; blockchain and decentralized approaches to copyright management; biometric and stylometric signatures for creator verification; adversarial robustness of protection systems; usability and accessibility of protection tools for diverse creators; privacy-preserving authentication protocols; and real-time monitoring and enforcement frameworks. We equally encourage rigorous evaluation methodologies, user studies, and empirical analyses that address the efficacy of protection technologies against emerging threats, the trade-offs between robustness and usability, and the practical deployment of safeguarding systems across different creative ecosystems. Submissions should articulate clear threat models, report comparative baselines or ablations, and discuss limitations and failure modes. Interdisciplinary perspectives bridging computer science, cryptography, digital forensics, law, ethics, sociology, and creative practice are strongly encouraged.

8. Federated and Continuous Learning for Embodied Intelligence

Organizers

• Weishan Zhang, Petroleum University of China, zhangws@upc.edu.cn
• Jiehan Zhou, University of Oulu, jiehan.zhou@ieee.org
• Youming Hu, Huazhong University of Science and Technology, youmhwh@mail.hust.edu.cn

Description

The rapid advancement of Embodied Intelligence (EI) has opened new opportunities for autonomous robots, unmanned aerial vehicles, and self-driving cars. These embodied systems directly interact with physical and social environments, aiming to operate reliably in complex, dynamic, and human-centered contexts. However, they still face significant challenges in achieving stable and incremental knowledge acquisition across distributed devices due to data privacy concerns, and catastrophic forgetting when encountering new tasks or environments. Federated Learning (FL) and Continual Learning (CL) have emerged as two promising paradigms to address these challenges. FL enables EI systems to collaboratively enhance their intelligence while preserving local data privacy, allowing multiple EI nodes to jointly train models without sharing raw data. On the other hand, CL empowers EI nodes to acquire new knowledge progressively without overwriting previously learned information, supporting long-term autonomous operation in dynamic, non-stationary environments. Therefore, this special session aims to advance research on trustworthy federated and continual learning for embodied intelligence. It seeks to explore not only algorithmic innovations but also theoretical and practical frameworks that enhance the reliability, transparency, and safety of distributed intelligent systems that evolve continuously with their environments.