Federated learning (FL) provides a new paradigm for protecting data privacy in Industrial Internet of Things (IIoT). To reduce network burden and latency brought by FL with a parameter server at the cloud, hierarchical federated learning (HFL) with mobile edge computing (MEC) servers is proposed. However, HFL suffers from a bottleneck of communication and energy overhead before reaching satisfying model accuracy as IIoT devices dramatically increase. In this paper, a deep reinforcement learning (DRL)-based joint resource allocation and IIoT device orchestration policy using non-orthogonal multiple access is proposed to achieve a more accurate model and reduce overhead for MEC-assisted HFL in IIoT. We formulate a multi-objective optimization problem to simultaneously minimize latency, energy consumption and model accuracy under the constraints of computing capacity and transmission power of IIoT devices. To solve it, we propose a DRL algorithm based on deep deterministic policy gradient. Simulation results show proposed algorithm outperforms others.

For in-vehicle application, task type and vehicle state information, i.e., vehicle speed, bear a significant impact on the task delay requirement. However, the joint impact of task type and vehicle speed on the task delay constraint has not been studied, and this lack of study may cause a mismatch between the requirement of the task delay and allocated computation and wireless resources. In this article, we propose a joint task type and vehicle speed-aware task offloading and resource allocation strategy to decrease the vehicle’s energy cost for executing tasks and increase the revenue of the vehicle for processing tasks within the delay constraint. First, we establish the joint task type and vehicle speed-aware delay constraint model. Then, the delay, energy cost, and revenue for task execution in the vehicular edge computing (VEC) server, local terminal, and terminals of other vehicles are calculated. Based on the energy cost and revenue from task execution, the utility function of the vehicle is acquired. Next, we formulate a joint optimization of task offloading and resource allocation to maximize the utility level of the vehicles subject to the constraints of task delay, computation resources, and wireless resources. To obtain a near-optimal solution of the formulated problem, a joint offloading and resource allocation based on the multiagent deep deterministic policy gradient (JORA-MADDPG) algorithm is proposed to maximize the utility level of vehicles. Simulation results show that our algorithm can achieve superior performance in task completion delay, vehicles’ energy cost, and processing revenue.

Recent years have witnessed a growing interest in compressed video action recognition due to the rapid growth of online videos. It remarkably reduces the storage by replacing raw videos with sparsely sampled RGB frames and other compressed motion cues (motion vectors and residuals). However, existing compressed video action recognition methods face two main issues: First, the inefficiency caused by the usage of coarse-level information under full resolution, and second, the disturbing due to the noisy dynamics in motion vectors. To address the two issues, this paper proposes a dynamic spatial focus method for efficient compressed video action recognition (CoViFocus). Specifically, we first use a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors. Then the selected patch pair will be processed by a high-capacity two-stream deep model for the final prediction. Such a patch selection strategy crops out the irrelevant motion noise in motion vectors, as well as reduces the spatial redundancy of the inputs, leading to the high efficiency of our method in the compressed domain. Moreover, we found that the motion vectors can help our method to address the possibly happened static-issue , which means that the focus patches get stuck at some regions related to static objects rather than target actions, which further improves our method. Extensive results on both the HMDB-51 and UCF-101 datasets demonstrate the effectiveness and efficiency of our method in compressed video action recognition tasks.

Caching and transcoding at multi-access edge computing (MEC) server and wireless resource allocation in eNodeB interact with each other and together determine the quality of experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients. However, the relationship among the three factors has not been explored, which has led to limited improvement in clients’ QoE. Therefore, we propose a joint optimization framework of video segment caching and transcoding in MEC servers and resource allocation to improve the QoE of DASH clients. Based on the established framework, we develop an MEC caching management mechanism that consists of the MEC caching partition, video segment deletion, and MEC caching space transfer. Then, a joint optimization algorithm that combines the video segment caching and transcoding in the MEC server and resource allocation is proposed. In the algorithm, the clients’ channel state and the playback status and cooperation among MEC servers are employed to estimate the client's priority, video segment representation switch and continuous playback time. Considering the above four factors, we develop a utility function model of clients’ QoE. Then, we formulate a mixed-integer nonlinear programming mathematical model to maximize the total utility of DASH clients, where the video segment caching and transcoding strategy and resource allocation strategy are jointly optimized. To solve this problem, we propose a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms efficiently improve client's throughput, received video quality and hit ratio of video segments while decreasing the playback rebuffering time, video segment representation switch and system backhaul traffic.

The rapid development of vision-based 3D perceptions, in conjunction with the inherent vulnerability of deep neural networks to adversarial examples, motivates us to investigate realistic adversarial attacks for the 3D detection models in autonomous driving scenarios. Due to the perspective transformation from 3D space to the image and object occlusion, current 2D image attacks are difficult to generalize to 3D detectors and are limited by physical feasibility. In this work, we propose a unified framework to generate physically printable adversarial patches with different attack goals: 1) instance-level hiding —pasting the learned patches to any target vehicle allows it to evade the detection process; 2) scene-level creating —placing the adversarial patch in the scene induces the detector to perceive plenty of fake objects. Both crafted patches are universal , which can take effect across a wide range of objects and scenes. To achieve above attacks, we first introduce the differentiable image-3D rendering algorithm that makes it possible to learn a patch located in 3D space. Then, two novel designs are devised to promote effective learning of patch content: 1) a Sparse Object Sampling Strategy is proposed to ensure that the rendered patches follow the perspective criterion and avoid being occluded during training, and 2) a Patch-Oriented Adversarial Optimization is used to facilitate the learning process focused on the patch areas. Both digital and physical-world experiments are conducted and demonstrate the effectiveness of our approaches, revealing potential threats when confronted with malicious attacks. We also investigate the defense strategy using adversarial augmentation to further improve the model’s robustness.

Wireless offloading in mobile-edge-computing (MEC)-enabled Internet of Things (IoT) networks inevitably suffers the risk of eavesdropping. Physical-layer security (PLS) approaches can be applied to prevent eavesdropping. However, the existing PLS techniques are not well targeted for videos due to the fact that video’s distortion characteristics, which allow encoding parameters to be flexibly adjusted to enhance security in offloading, are ignored. A deep reinforcement learning (DRL)-based real-time, secure, and efficient video offloading scheme is proposed in this article, where video frame resolution, one key parameter of video’s distortion characteristics, is introduced and jointly optimized with PLS scheme to guarantee video’s security, improve users’ Quality of Experience (QoE) and save energy consumption. We formulate a joint optimization problem of video frame resolution selection, computation offloading, and resource allocation strategy, to minimize energy consumption and maximize QoE in terms of delay and analytic accuracy, while subject to security rate, computing capability, and transmission power. To solve the formulated NP-hard problem with the form of high-dimensional nonlinear mixed-integer programming, the hierarchical reward-function-based DRL (JVFRS-CO-RA-MADDPG) algorithm is proposed to guide the agents to obtain the optimal policy efficiently. Finally, the simulation results show that the proposed algorithm outperforms the existing algorithms in terms of delay, energy consumption, and security level.