A novel fine-grained service scheduling scheme based on application-aware network (APN) for Internet of Vehicles (IoV) is proposed to achieve efficient and differentiated network resource orchestration. Application and service flow information is translated to application identifier (ID) and Sub-service ID encapsulated in IPv6 header. In this way, network can perceive various applications and provide suitable network channel resource for them. The APN-based information identifying method for application does not require unpacking of data packets and does not overly rely on network controllers, which can improve service efficiency. The experimental verification has been conducted on IoV experimental network in Xiong'an, China. In the experiment, the end-to-end delay of remote driving of APN autonomous vehicle is decreased by 67.5% compared to that of normal autonomous vehicle. The reason is that the network can identify the critical service flow sent by the APN vehicle and guide it to the dedicated channel to avoid competing for bandwidth resources in the shared channel which has 10% packet loss rate. In addition, the packet loss rate thresholds for all remote driving service flows are obtained through experiment, so that the optimal flow orchestration can be used to realize efficient utilization of network resource while ensuring smooth remote control.

Nowadays, wireless communication is rapidly reshaping entire industry sectors. In particular, mobile-edge computing (MEC) as an enabling technology for the Industrial Internet of Things (IIoT) brings a powerful computing/storage infrastructure closer to the mobile terminals and, thereby, significantly lowers the response latency. To reap the benefit of proactive caching at the network edge, precise knowledge on the popularity pattern among the end devices is essential. However: 1) the spatiotemporal variability of content popularity; 2) the data deficiency in privacy-preserving system; 3) the costly manual labels in supervised learning; as well as 4) the not independent and identically distributed (non-i.i.d.) user behaviors pose tough challenges to the acquisition and prediction of content popularities. In this article, we propose an unsupervised and privacy-preserving popularity prediction framework for MEC-enabled IIoT to achieve a high popularity prediction accuracy while addressing the challenges. Specifically, the concepts of local and global popularities are introduced and the time-varying popularity of each user is modeled as a model-free Markov chain. On this basis, we derive and validate the essential relationship between the local and global popularities and then propose an unsupervised recurrent federated learning (URFL) algorithm to predict the distributed popularity while achieving privacy preservation and unsupervised training. Moreover, a federated loss-weighted averaging (FedLWA) scheme for the parameter aggregation is further designed to alleviate the problem of non-i.i.d. user behaviors. Simulations indicate that the proposed framework can enhance the prediction accuracy in terms of a reduced root-mean-squared error by up to 60.5%–68.7% compared to other baseline methods, i.e., recommendation algorithms, centralized learning algorithms, and other distributed learning algorithms. Additionally, manual labeling and violation of users’ data privacy are both avoided.

Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy.

In this paper, we investigate the problem of edge caching (EC) optimization in a multi-user privacy-preserving mobile edge computing (MEC) system. The time-varying content popularity is considered and the primary objective is to maximize the EC hit rate on each caching entity in the distributed network. To this end, we introduce the concept of local and global popularities and cast the time-varying local popularities as model-free Markov chains. Next, an unsupervised recurrent federated learning (URFL) algorithm is proposed to predict the popularities while achieving privacy-preserving goal. The underlying distributed optimization problem is then reformulated as a distributed Markov decision process and solved by the privacy-preserving distributed deep deterministic policy gradient algorithm incorporating the URFL algorithm. Simulation results demonstrate the superiority of the proposed scheme in terms of prediction error and hit rate over the baseline methods.

Video-based person re-identification plays important roles in surveillance video analysis. This paper proposes a duplex spatiotemporal filtering network (DSFN) to re-identify persons in videos. A video sequence is represented as a duplex spatiotemporal matrix. DSFN model containing a group of filters performs filtering at feature levels in both temporal and spatial dimensions, by which the model focuses on feature-level semantic information rather than image-level information as in the traditional filters. We propose sparse-orthogonal constraints to enforce the model to extract more discriminative features. DSFN seeks to characterize not only the appearance features but also dynamic information such as gaits embedded in video sequences and obtains performance improvement as a result. Experimental results show that the proposed method outperforms other comparison approaches.

Mobile edge computing (MEC) has received broad attention as an effective network architecture and a key enabler of the wireless virtual reality (VR) video service which is expected to take a huge share of communication traffic. In this work, we investigate the scenario of multi-tiles-based wireless VR video service with the aid of MEC network, where the primary objective is to minimize the system energy consumption and the latency as well as to arrive at a tradeoff between these two metrics. To this end, we first cast the time-varying view popularity as a model-free Markov chain and use a long short-term memory autoencoder network to predict its dynamics. Then, a mixed strategy, which jointly considers the dynamic caching replacement and the deterministic offloading, is designed to fully utilize the caching and computing resource in the system. The underlying multiobjective optimization problem is reformulated as a partially observable Markov decision process and solved by using a deep deterministic policy gradient algorithm. The effectiveness of the proposed scheme is confirmed by numerical simulations.