The transition to Industry 4.0 is promoting personalized production, challenging traditional design methods reliant on manual expertise and lengthy iterations. Although generative AI shows promise for automation, its industrial use is limited by difficulties in capturing and applying complex rule-based design specifications. To address this, we present “AutoFrit”, a comprehensive parametric automobile frit dataset containing design pairs from major manufacturers. Our evaluation shows that models trained on AutoFrit drastically reduce design iteration time from months to minutes while ensuring adherence to industrial standards, effectively addressing the scalability challenges in modern manufacturing.

The history of information infrastructure can be read as a history of connectivity. Transmission Control Protocol/Internet Protocol (TCP/IP) made heterogeneous hosts reachable and the World Wide Web made documents linkable. A new connectivity problem is now emerging: The object to be connected is a capability-bearing agent that can perceive, reason, invoke tools, or act in the physical world. However, these capabilities remain locked inside platforms, organizations, and runtimes, while existing frameworks, interface protocols, and agent-interconnection efforts still leave open the question of what stable public abstraction can connect agents, humans, and infrastructure as a network architecture problem across organizational and physical domains. This vision and position paper argues for agent network: A reference architecture for capability discovery, connection, and coordination among heterogeneous, autonomous, and potentially self-interested agents. We do not present a complete protocol specification or a performance evaluation. Its central position is that open agent networks need a dual narrow-waist architecture. The Agent Locator Protocol (ALP), surfaced through agent://<name>, provides the connectivity waist for making capability-bearing participants globally locatable, reachable, and minimally deliverable across substrates and organizations. The Task Semantic Intermediate Representation (TSIR) protocol provides the semantic waist for circulating task intent and success criteria as signable and reusable objects. Around two waists, we propose the Agent Shared Cognition Protocol (ASCP) as a candidate coordination runtime for adjustable group cognition, with blackboard-like workspaces and sedimentation as reference mechanisms for shared working state and experience reuse. Together, these abstractions define Networked Intent Realization (NIR): A research agenda for preserving high-level intent, routing it to capable participants, and translating it into executable, traceable, and accountable collaboration across open agent networks.

The rapid growth of the Internet of Things (IoT) demands efficient system architectures and protocols to ensure consistent performance at scale. This paper explores the scalability of IoT systems across three key layers: sensing, network, and control. IoT scalability is the ability of a system to maintain consistent and reliable performance despite a continuous increase in connected devices. To evaluate scalability, we introduce the scalability indicator (SI), a metric designed to assess an IoT system’s scalability capability. Through extensive research and real-world deployments, we identify key challenges in data sensing, routing, and system control. Our study presents a model to understand these challenges and proposes strategies to optimize resource utilization, ensuring efficient data collection. The findings also emphasize the key influencing factors for the stable performance of large-scale IoT systems, providing valuable insights for how to design scalable systems that can meet the growing demand for interconnected devices.

The integration of embodied intelligence into physical environments marks a new frontier in the evolution of intelligent systems. While the Internet of Things (IoT) connects devices and Artificial Intelligence of Things (AIoT) embeds intelligence into them, we argue that a further conceptual leap is required—one that enables the composition of intelligence itself through real-world embodiment, interaction, and evolution. We introduce the paradigm of Embodied Intelligence of Things (EIoT) as a foundational framework for distributed, physically grounded intelligent systems. EIoT systems are structured across three essential dimensions: Enacted, where devices are transformed into embodied agents; Engaged, where agents interact opportunistically based on physical and contextual constraints; and Evolutionary, where intelligence adapts and self-organizes through continuous experience. We further outline a developmental trajectory for EIoT based on the openness of perception and decision spaces, providing a conceptual map from tightly constrained agents to fully autonomous and adaptive systems. This work aims to establish EIoT as a core architectural and theoretical direction for future embodied intelligent systems.

Clock synchronization is one of the most fundamental and crucial network communication strategies. With the expansion of the Industrial Internet in numerous industrial applications, a new requirement for the precision, security, complexity, and other features of the clock synchronization mechanism has emerged in various industrial situations. This paper presents a study of standardized clock synchronization protocols and techniques for various types of networks, and a discussion of how these protocols and techniques might be classified. Following that is a description of how certain clock synchronization protocols and technologies, such as PROFINET, Time-Sensitive Networking (TSN), and other well-known industrial networking protocols, can be applied in a number of industrial situations. This study also investigates the possible future development of clock synchronization techniques and technologies.

Motion tracking via Inertial Measurement Units (IMUs) on mobile and wearable devices has attracted significant interest in recent years. High-accuracy IMU-tracking can be applied in various applications, such as indoor navigation, gesture recognition, text input, etc. Many efforts have been devoted to improving IMU-based motion tracking in the last two decades, from early calibration techniques on ships or airplanes, to recent arm motion models used on wearable smart devices. In this paper, we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices. We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.

Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View (FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems (GIS). Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called “Spotlight”, which performs passive localization using crowdsourced photos. Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system’s localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.