Edge computing has great potential to address the challenges in mobile vehicular networks by transferring partial storage and computing functions to network edges. However, it is still a challenge to efficiently utilize heterogeneous edge computing architectures and deploy large-scale IoV systems. In this article, we focus on the collaborations among different edge computing anchors and propose a novel collaborative vehicular edge computing framework, called CVEC. Specifically, CVEC can support more scalable vehicular services and applications by both horizontal and vertical collaborations. Furthermore, we discuss the architecture, principle, mechanisms, special cases, and potential technical enablers to support the CVEC. Finally, we present some research challenges as well as future research directions.

Data is the input for various artificial intelligence (AI) algorithms to mine valuable features, yet data in Internet is scattered everywhere and controlled by different stakeholders who cannot believe in each other, and usage of the data in complex cyberspace is difficult to authorize or to validate. As a result, it is very difficult to enable data sharing in cyberspace for the real big data, as well as a real powerful AI. In this paper, we propose the SecNet , an architecture that can enable secure data storing, computing, and sharing in the large-scale Internet environment, aiming at a more secure cyberspace with real big data and thus enhanced AI with plenty of data source, by integrating three key components: 1) blockchain-based data sharing with ownership guarantee, which enables trusted data sharing in the large-scale environment to form real big data; 2) AI-based secure computing platform to produce more intelligent security rules, which helps to construct a more trusted cyberspace; 3) trusted value-exchange mechanism for purchasing security service, providing a way for participants to gain economic rewards when giving out their data or service, which promotes the data sharing and thus achieves better performance of AI. Moreover, we discuss the typical use scenario of SecNet as well as its potentially alternative way to deploy, as well as analyze its effectiveness from the aspect of network security and economic revenue.

The development and popularity of vehicle-to-everything communication have caused more risks to the in-vehicle networks security. As a result, an increasing number of various and effective intrusion detection methods appear to guarantee the security of in-vehicle networks, especially deep-learning-based methods. Nevertheless, the state-of-the-art deep-learning-based intrusion detection methods lack a quantitative and fair horizontal performance comparison analysis. Also, they have no comparative analysis of the detection capability for the unknown attacks as well as on the time and hardware resource consumption of their intelligent intrusion detection models. Therefore, this paper investigates ten representative advanced deep-learning-based intrusion detection methods and illustrates the characteristics and advantages of each method. Moreover, quantitative and fair experiments are set to make horizontal comparison analyses. Also, this study provides some significant suggestions on baseline method selection and valuable guidance, for the direction of future research about lightweight models and the ability to detect unknown attacks.

As ubiquitous and personalized services are growing boomingly, an increasingly large amount of traffic is generated over the network by massive mobile devices. As a result, content caching is gradually extending to network edges to provide low-latency services, improve quality of service, and reduce redundant data traffic. Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests. In this article, we propose an evolving learning-based content caching policy, named PA-Cache in edge networks. It adaptively learns time-varying content popularity and determines which contents should be replaced when the cache is full. Unlike conventional deep neural networks (DNNs), which learn a fine-tuned but possibly outdated or biased prediction model using the entire training dataset with high computational complexity, PA-Cache weighs a large set of content features and trains the multi-layer recurrent neural network from shallow to deeper when more requests arrive over time. We extensively evaluate the performance of our proposed PA-Cache on real-world traces from a large online video-on-demand service provider. The results show that PA-Cache outperforms existing popular caching algorithms and approximates the optimal algorithm with only a 3.8% performance gap when the cache percentage is 1.0%. PA-Cache also significantly reduces the computational cost compared to conventional DNN-based approaches.

With the development of the Internet of Things, a complex CPS system has emerged and is becoming a promising information infrastructure. In the CPS system, the loss of control over user data has become a very serious challenge, making it difficult to protect privacy, boost innovation, and guarantee data sovereignty. In this article, we propose HyperNet, a novel decentralized trusted computing and networking paradigm, to meet the challenge of loss of control over data. HyperNet is composed of the intelligent PDC, which is considered as the digital clone of a human individual; the decentralized trusted connection between any entities based on blockchain as well as smart contract; and the UDI platform, enabling secure digital object management and an identifier-driven routing mechanism. HyperNet has the capability of protecting data sovereignty, and has the potential to transform the current communication-based information system to the future data-oriented information society.