As the complexity and density of electronic components continue to increase, manual printed circuit board (PCB) routing has become an increasingly labor-intensive and costly task. However, the lack of large, publicly available datasets for training machine-learning (ML) models has hindered potential advancements in this field. To address this gap, we introduce PCBRouteNet, a comprehensive, large-scale dataset specifically designed to accelerate ML innovations in automated PCB routing. To handle the high complexity of PCB data and enhance extraction efficiency, we propose a dynamic, adjustable, quadrilateral network flow model. This model constructs a network flow graph composed of quadrilateral tiles, efficiently transforming the original design data into a network flow-based format. This format facilitates feature extraction for both global and detailed routing tasks. Additionally, we introduce and analyze various flow-encoding methods to explore the generalization and relationships between data size and network parameters, leveraging scaling laws. Our dataset features a diverse array of layouts with varying complexities, such as multilayer boards and high-density interconnects. Furthermore, we propose several practical ML tasks that utilize PCBRouteNet to demonstrate its potential in improving the efficiency and effectiveness of automated PCB routing solutions.

The proliferation of immersive applications such as Virtual, Augmented, and Mixed Reality (VR/AR/MR) imposes stringent low-latency and reliability requirements that challenge conventional O-RAN slicing mechanisms. Existing frameworks often fail to anticipate rapid XR traffic fluctuations driven by user motion and gaze dynamics, leading to inefficient resource utilization and SLA violations. To overcome these limitations, we propose a cross-layer intelligent control framework that integrates traffic prediction and reinforcement learning-based slice orchestration across the Non-RT and Near-RT RIC. By coupling long-term foresight with short-term adaptability, the proposed design enables proactive, SLA-aware scheduling under highly dynamic conditions. We further develop a trace-driven network emulator to reproduce realistic 5G behaviors and validate system robustness. Extensive experiments demonstrate that our framework consistently achieves over 95% SLA compliance, below 2% latency violations, and up to 30% latency reduction compared with state-of-the-art baselines, confirming its effectiveness and scalability for next-generation immersive networks.

Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.