Edge computing is booming as a promising paradigm to extend service provisioning from the centralized cloud to the network edge. Benefit from the development of serverless computing, an edge server can be configured as a carrier of limited serverless functions, in the way of deploying Docker runtime and Kubernetes engine. Meanwhile, an application generally takes the form of directed acyclic graphs (DAGs), where vertices represent dependent functions and edges represent data traffic. The status quo of minimizing the completion time (a.k.a. makespan) of the application motivates the study on optimal function placement. However, current approaches lose sight of proactively splitting and mapping the traffic to the logical data paths between the heterogeneous edge servers, which could affect the makespan significantly. To remedy that, we propose an algorithm, termed as Dependent Function Embedding (DPE), to get the optimal edge server for each function to execute and the moment it starts executing. DPE finds the best segmentation of each data traffic by exquisitely solving several infinity norm minimization problems. DPE is theoretically verified to achieve the global optimality. Extensive experiments on Alibaba cluster trace show that DPE significantly outperforms two baseline algorithms in makespan by 43.19% and 40.71%, respectively.

The increasing need for communication capabilities in mobile devices has led to the recognition of mobile edge computing (MEC) as a critical solution for addressing computationally intensive and latency-sensitive tasks due to its widespread distribution of resources close to devices. However, in scenarios such as disaster response and emergency rescue, the rapid deployment of edge servers to handle tasks may be challenging. Therefore, unmanned aerial vehicle (UAV)-assisted MEC systems have garnered significant interest due to their ease of deployment and high mobility. Nonetheless, the limited computational resources and sensing radius of UAVs give rise to the challenge of optimizing target area coverage and mission data processing timeliness within a restricted time period. In response to this challenge, we present PCRDAC, a novel reinforcement learning-based mobility management framework for UAVs. This framework periodically instructs UAVs to collaboratively update their decision networks, thus determining their movement patterns. This framework can also control UAVs to support worst-case scenarios. Comprehensive simulation experiments validate the efficacy of our framework. Our framework promotes efficient collaboration among UAVs and significantly reduces data staleness in the system. As a result, edge devices can collect ambient data that is fresh enough.

The property of Internet of Things (IoT) applications is their capability to execute tasks through the collaboration of interconnected IoT objects. However, IoT collaborations face significant challenges due to security threats that undermine their reliability. An uncertified task publisher may deceive IoT devices into executing illegal tasks, while malicious attackers may intercept and modify transmitted data. Existing works on IoT trusted management issues tend to concentrate on individual aspects, such as authentication, privacy protection, and access control. However, trusted management for IoT collaboration is a multifaceted and intricate endeavor that necessitates a comprehensive approach. To fill this gap, we propose a lightweight authentication-driven trusted management framework that includes a novel authentication and key agreement scheme to guarantee the validity of task publishers, with greatly reduced overheads compared to recent works. The framework also incorporates a distributed data storage scheme and a fine-grained access control mechanism. We record the interactive messages on the blockchain to ensure behavior traceability. We evaluate the authentication scheme through comparative experiments and formal security analysis, demonstrating its efficiency and effectiveness. The experimental results of data storage and acquisition in real-world IoT environments indicate that the proposed framework is a feasible solution for reliable IoT collaboration.

3D object detection in autonomous driving systems perceives the surrounding environment and is the foundation for autonomous driving. Due to the sparsity inherent in point clouds in autonomous driving scenarios, LiDAR-based 3D object detection often fails to distinguish distant objects effectively. Addressing the issue of point cloud sparsity will enhance the detection range in autonomous driving scenarios. Pseudo point clouds have been used to enhance the ability of deep learning models to detect distant points. However, this approach has several shortcomings. In this paper, a curbed fake point collector (CFPC), which addresses the three issues caused by pseudo points, is proposed to support 3D object detection for autonomous vehicles. First, for noise points with inaccurate coordinates, the dead pixel checker (DPC) calculates the depth map gradient using the Sobel operator. This approach enables the deep learning model to identify noise points. Second, because of the excessive quantity of points, sparse prioritized local sampling (SPLS) reduces the number of input point clouds to a lightweight level that can be accommodated by computing devices with limited memory. This is achieved through grid-based random sampling and real-point-prioritized farthest point sampling. This module effectively samples an appropriate pseudo point cloud based on the density of points in local space. Third, with respect to interference among channels, channel mask set abstraction (CMSA) isolates channels describing different information within the point cloud using GroupMLP, which is an MLP that separates channels into their respective groups. Group separation facilitates the extraction of features without mutual influence, allocating half of the output channels to color information and the other half to geometric information. The effectiveness of our approach is demonstrated by the results of experiments conducted on the KITTI dataset. It is superior to the baseline in most situations, particularly in the categories of cars and riders.