Modern vehicles are equipped with sensors, communication, and computation units that make them capable of providing monitoring services and analysis of real-time traffic information to improve road safety. The main aim of communication in vehicular networks is to achieve an autonomous driving environment that is accident-free alongside increasing road use quality. However, the demanding specifications such as high data rate, low latency, and high reliability in vehicular networks make 5G an emerging solution for addressing the current vehicular network challenges. In the 5G IoV environment, various technologies and models are deployed, making the environment open to attacks such as Sybil, Denial of Service (DoS) and jamming. This paper presents the security and privacy challenges in an IoV 5G environment. Different categories of vehicular network attacks and possible solutions are presented from the technical point of view.
With the emergence and development of communication technology and new computing paradigm named mobile edge computing (MEC), fast response and ultralow latency are given higher requirements. Nevertheless, due to the low penetration and coverage of the MEC network, it is difficult to guarantee the large-scale connection needs of all user groups in industry 4.0. In addition, user mobility is closely related to the network connection between edge nodes (ENs) and mobile devices (MDs) in industry 4.0, the frequent mobility of MDs makes the computation offloading process not smooth and the channel unstable, which can reduce the network performance. Hence, this paper constructs an edge network environment for MEC-based industrial internet of things (IIoT), considering the combined benefits of energy consumption, time delay, and computing resource cost to tackle the aforementioned problem by maximizing the utility of the entire system. In order to solve this problem, this paper proposes a mobility-aware offloading and resource allocation scheme (MAORAS). This scheme first employs the Lagrange multiplier method to solve the problem of computing resource allocation; then, a noncooperative game between MDs is established and the existence of Nash equilibrium (NE) has been proven. Simulation results demonstrate that the practical performance of the MAORAS optimization scheme could improve the system utility significantly.
Huge networks and increasing network traffic will consume more and more resources. It is critical to predict network traffic accurately and timely for network planning, and resource allocation, etc. In this paper, a combined network traffic prediction model is proposed, which is based on Prophet, evolutionary attention-based LSTM (EALSTM) network, and Gaussian process regression (GPR). According to the non-smooth, sudden, periodic, and long correlation characteristics of network traffic, the prediction procedure is divided into three steps to predict network traffic accurately. In the first step, the Prophet model decomposes network traffic data into periodic and non-periodic parts. The periodic term is predicted by the Prophet model for different granularity periods. In the second step, the non-periodic term is fed to an EALSTM network to extract the importance of the different features in the sequence and learn their long correlation, which effectively avoids the long-term dependence problem caused by long step length. Finally, GPR is used to predict the residual term to boost the predictability even further. Experimental results indicate that the proposed scheme is more applicable and can significantly improve prediction accuracy compared with traditional linear and nonlinear models.
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