The Internet of Vehicles (IoV) connects a massive amount of smart vehicles for inter/intra-vehicle information sharing. Data privacy issues, such as privacy leakage and privacy cost are the key challenges that hinder vehicle operators from sharing their data safely. Traditional privacy-preserving techniques, including Federated Learning (FL) and Differential Privacy (DP) techniques, can protect data privacy and security, but the high privacy cost severely limits learning performance. In addition, the IoV services place high demands on low communication latency, which can be obtained by reducing the communication bits, but it also limits the learning performance. Thus, how to solve the communication-privacy-accuracy tradeoffs to achieve low latency, high privacy preservation and model performance has been a complicated issue in IoV. In this paper, a privacy-enhancement differentially private federated learning framework (FedSDP) is proposed based on the shuffle model to ensure secure and efficient data sharing under the constraint of low latency in IoV. In our proposed framework, four privacy enhancement methods are proposed, including data subsampling, vehicle sampling, shuffle model and dummy points, to amplify the privacy and obtain higher learning performance. Then, a Top-K sparsification mechanism of the vehicle training process is proposed to reduce communication bits. Finally, the experimental results indicate that our approach can reduce the communication latency by 31.66%, enhance the privacy ϵc by 30.77% and improve the test accuracy by 48.56%, compared with the traditional SDP mechanism.

With the continued evolution of autonomous driving, the communication demand for in-vehicle networks (IVNs) has dramatically increased, and the traditional IVN solutions have become ineligible for the needs of new in-vehicle applications. Time-sensitive network (TSN) with the characteristics of determinism, low latency, high reliability, large bandwidth, and open industry standards, has been considered as the most promising technology for the next-generation IVNs. This article summarizes the current state and methodologies of in-vehicle TSN research through the following five aspects: 1) Quality of Service (QoS) strategy; 2) reliability; 3) clock synchronization; 4) network planning; and 5) network management. The corresponding research priorities and technical challenges are also addressed, respectively.

Multiaccess edge computing (MEC) allows terminal devices (TDs) with limited energy and computing capabilities to offload tasks to edge servers, which have more energy and computing capabilities to provide better service. In order to accommodate more users, an edge server can be divided into many virtual machines (VMs), but the interference among VMs could cause decreased service quality. At the same time, due to server hardware and software errors, there are random temporary failures regardless of whether a virtual machine is serving or not. This article uses a multidimensional Markov chain to establish a unified performance analysis model that jointly considers multiple types of terminals, different local service capabilities, different request arrival traffic, different virtual machine numbers, and different virtual machine failure rates. The statistical models of access blocking probability, resource availability, capacity, forced dropping probability, and retainability are presented. Furthermore, we analyze whether and how the local service should be performed when the virtual machine used fails and propose a backward offloading scheme. Analysis results show that some of quality is not monotonous. The model in this article provides a powerful and accurate analytical method for the preperformance simulation of actual engineering design.

The rapid growth of Internet of Vehicles (IoV) has brought huge challenges for large data storage, intelligent management, and information security for the entire system. The traditional centralized management approach for IoV faces the difficulty in dealing with real-time response. The blockchain, as an effective technology for decentralized distributed storage and security management, has already showed great advantages in its application of Bitcoin. In this paper, we investigate how the blockchain technology could be extended to the application of vehicle networking, especially with the consideration of the distributed and secure storage of big data. We define several types of nodes such as vehicle and roadside for vehicle networks and form several sub-blockchain networks. In this paper, we present a model of the outward transmission of vehicle blockchain data, and then give detail theoretical analysis and numerical results. This paper has shown the potential to guide the application of blockchain for future vehicle networking.

Wireless Body Area Networks (WBAN) have broad prospects for the use in mobile health, sports training support, etc. One critical research problem on WBAN is the cross-interference among multiple WBANs when they are close to each other, because they work on unlicensed open wireless frequency bands. This paper proposes a social group interaction power control game model to mitigate inter- WBAN interference, which consists of new utility and cost functions designed to accommodate both convergence speed and quality. This work proves that only one Nash equilibrium (NE) point exists for this game model, which guarantees its convergence. Extensive simulation has been performed to evaluate the performance and the results demonstrate that the proposed algorithm is highly effective and the convergence is rapid.

This paper develops the traffic models of smart grid electronic data (E data) and multimedia video over cognitive radio (CR). Unlike the traditional “Poisson” arrival model, each arrival monitoring stream follows fixed time triggered Gaussian distribution which approximates to the reality, and the video data is classified as key frame data with higher priority than nonkey frame data to reduce communication burden. To enhance the delivery probability of E data and multimedia data, we adopt a buffer mechanism to store the “sending fail” data and try to resend them together with new coming data by using the new data's sending opportunity. To avoid buffer overflow, the unsent data should be compressed and some should be removed, and the new coming data rate should be reduced to alleviate the congestion of the CR communication network. In this paper, we propose a new binary exponential backoff (NBEB) algorithm to “compress” the unsent data which can keep key information but recover the electronic tendency as much as possible. With NBEB, the new coming data can be temporally selected and thrown into the buffer and more new data can be put in the buffer. The algorithm can reduce the arrival traffic rate exponentially related with the sending failure times. The results show that NBEB can significantly decrease the blocking/dropping probability, increase the communication success probability, and improve the communication performance.

In this paper, we propose a novel Media Access Control (MAC) scheme for Cognitive Radio Ad Hoc Networks (CRAHNs). The scheme includes two mechanisms. One mechanism is called Exposed Terminal Secondary users Communication (ETSC), which allows a Secondary User (SU) to continue communication attempt even when the occupied channel could be used by a new Primary User (PU). The other is called Four-Stage RTS/CTS Back protocol (FSRCB) of PUs by which the communication links between PUs can be created after at most four RTS/CTS shacking attempts. FSRCB can support a PU receiver which is in the interference area of a SU sender, and decrease transmission power to increase frequency utilization efficiency. Compared with the traditional "PU-driven SU self- termination" scheme, our scheme can achieve much better performance, including lower dropping rate, higher success rate, higher throughput, higher channel utilization efficiency, and lower blocking probability.

Multimedia applications have been becoming a majority type of traffic over cognitive radio network (CRN). Considering the different delay requirements of heterogeneous multimedia applications, we classified the secondary users (SUs) into four priority classes. Due to delay-sensitive nature of real-time (RT) multimedia services, we give it high priority to access channels. Non-real-time (NRT) traffic is characterized as delay-insensitive. In this paper, we proposed a dynamic spectrum handoff scheme with finite-size buffer queues to store preempted SUs which aims at avoiding the dropping events even though slightly increase the blocking probability. The finite-size buffer queue can avoid too many RT traffics piling up. Through limiting the buffer size the NRT traffics can get a fair chance to use channels. Additionally, spectrum sensing and channel allocation are controlled by a central base station (CBS). Such centralized admission control mechanism can efficiently prevent multiple SUs from simultaneously requesting to access the same spectrum band, consequently, to protect the channel form SUs' collision. A preemptive resume priority (PRP) M/M/c/c+k Markov model is established to analysis the performance, including the blocking probability, the throughput of SUs and the average completion time of RT traffic. The results show that the proposed scheme can meet the various performance requirements of heterogeneous multi-media applications. Moreover, the buffer mechanism can improve the channel utilization considerably.

ABSTRACT In this paper, a new channel allocation and re‐location scheme is proposed for cognitive radio users to efficiently utilize available spectrums. We also present a multiple‐dimension Markov analytical chain to evaluate the performance of this scheme. Both analytical results and simulation results demonstrate that the new scheme can enhance the radio system performance significantly in terms of blocking probability, dropping probability, and throughput of second users. The proposed scheme can work as a non‐server‐based channel allocation, which has practical values in real engineering design. Copyright ©2011 John Wiley & Sons, Ltd.

With the fast growing of multimedia communication applications, cognitive radio networks have gained the popularity as they can provide high wireless bandwidth and support quality-driven wireless multimedia services. In multimedia applications such as video conferences over the cognitive radio, the Quality of Experience (QoE) that directly measures the satisfaction of the end users cannot be easily realized due to the limited spectrum resources. The opportunistic spectrum access cognitive radio (CR) is an efficient technology to address this issue. However, the unstable channels allocated to the multimedia secondary users (SUs) can be re-occupied by the primary users (PUs) at any time, which makes the CR difficult to meet the QoE requirements. Therefore, it is important to study how to allocate frequency or spectrum resources to SUs according to their QoE requirements. This paper proposes a novel QoE-driven channel allocation scheme for SUs and cognitive radio networks (CRN) base station (BS). The historical QoE data under different primary channels (PCs) are collected by the SUs and delivered to a Cognitive Radio Base Station (CRBS). The CRBS will allocate available channel resources to the SUs based on their QoE expectations and maintain a priority service queue. The modified ON/OFF models of PCs and service queue models of SUs are jointly investigated for this channel allocation scheme. The performance of multimedia transmission of images and H.264 videos under our CR channel allocation scheme is studied, the results show that the proposed channel allocation approach can significantly improve the QoE of the priority-based SUs over the cognitive radio networks.

There is a strong need to explore green and harvestable energy in computer communications. However, adapting wireless network performance to harvested energy has largely been ignored in literature. In this paper, we propose a new resource allocation scheme to improve data delivery quality in energy harvesting enabled wireless networks. In the proposed approach, packet Automatic Repeat reQuest (ARQ) limit of each wireless node is adaptively adjusted according to harvested energy. To achieve such optimal retry adaptation, energy neutrality constraint is considered in the overall optimization process. Simulation results show that the proposed retry adaptation approach significantly improves packet delivery ratio by exploring the harvested energy.

In this paper, the performance of bidirectional call overflow channel allocation scheme in hierarchical cellular systems (HCSs) with/without queuing for handoff calls is studied, the analytical results are derived and the numerical evaluation show that the scheme with queuing for handoff calls outperforms the strategy without queuing in terms of low handoff failure probability, whereas the blocking probability of new calls doesn't became worse.