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Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, we survey existing researches for state-of-the-art data-driven CA techniques. Firstly, we discuss the major steps of CA and key research issues. For each step, we review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, we present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined. [ABSTRACT FROM AUTHOR]

This paper investigates a joint resource allocation for ultra-reliable and low-latency radio access networks (URLLRANs) with edge computing. Compared with conventional networks, URLLRANs have more restrictive latency and reliability requirements, and always feature short packet communications. It is a challenging work to provide edge computing services in URLLRANs, since the processing and transmission delay as well as packet loss during computation and communications should all be taken into considerations. Along these lines, to specify the trade-off between latency and reliability, this paper defines computation rates and transmission rates for short packets. Different from the existing work, the proposal takes effective information as well as energy consumption as performance metrics based on the definition. The packet request rates, computation latency, service rates, communication power, blocklength, and transmission information amounts are jointly optimized to reduce energy consumption and meanwhile generate more effective information for both the computation system and the communication system. To solve the NP-hard problem, the locally optimal solution and global optimal solution are both derived. Simulation results validate the performance advantage of the proposal and also indicate that the locally optimal solution can greatly reduce the computation complexity with only a small performance loss when compared with the global optimal solution. [ABSTRACT FROM AUTHOR]

Driven by the extensively emerging applications requiring big data processing, a series of heterogeneous network architectures have been proposed to meet user experience requirements. Among them, the concept of fog computing facilitates the effective integration and utilization of ubiquitous computing resources. In fog computing scenarios, willingness and service billing issues become significant to computing resource sharing and transactions. In this article, the recently developed blockchain technology characterized by successfully enabling consensus in an untrustworthy environment is introduced. Based on the blockchain technology, we propose a new architecture for resource sharing and transactions in fog computing networks, named Blockchain-Enabled Resource Sharing and Transactions in Fog Computing (B-ReST). The physical architecture, functional architecture, and workflow in B-ReST are defined. We also discuss the key technologies in B-ReST such as the smart contracts, the consensus mechanism and the requester and provider matching (RPM). The wireless characteristics of fog computing and blockchain technology are closely combined to make full and efficient use of ubiquitous computing resources. To prove the feasibility of the proposed architecture, the RPM problem is solved by a deep reinforcement learning (DRL) based method. Simulation results show the advantages of B-ReST to realize resource sharing and transactions, and the performance of B-ReST based on the DRL method has been enhanced. Challenges and future research directions are summarized as well. [ABSTRACT FROM AUTHOR]

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications. To remedy that, leading methods such as ranging and cooperation have improved the positioning accuracy to varying degrees, but they are still full of challenges in practical applications. Especially for cooperative positioning, in addition to the performance of methods, cooperators may provide false data due to attacks or selfishness, which can seriously affect the positioning accuracy. By fully exploiting the characteristics of blockchain and edge computing, this paper proposes a vehicular blockchain-based secure and efficient GPS positioning error evolution sharing framework, which improves vehicle positioning accuracy from ensuring security and credibility of cooperators and data. First, by analyzing the GPS error, a bridge can be established between the sensor-rich vehicles and the common vehicles to achieve cooperation by sharing the positioning error evolution at a specific time and location. Particularly, the positioning error evolution is obtained by a deep neural network (DNN)-based prediction algorithm running on the edge server. We further propose to use blockchain technology for storage and sharing the evolution of positioning errors, mainly to guarantee the security of cooperative vehicles and mobile edge computing nodes (MECNs). In addition, the corresponding smart contracts are designed to automate and efficiently perform storage and sharing tasks as well as solve inconsistencies in time scales. Extensive simulations based on actual data indicate the accuracy and security of our proposal in terms of positioning error correction and data sharing. [ABSTRACT FROM AUTHOR]

Although some excellent works have been done on networking, caching and computing, these three important areas have traditionally been addressed separately in the literature. In this article, we propose a novel framework that jointly considers networking, caching and computing techniques in order to improve end-to-end system performance. This integrated framework can enable dynamic orchestration of networking, caching and computing resources to meet the requirements of different applications. We define and develop the key components of this framework: the data plane, the control plane, and the management plane. The data plane consists of the devices that are responsible for networking, caching and computing operations. The control plane has a logically centralized controller to guide these operations. The management plane enables not only traditional applications, such as traffic engineering, but also new applications, such as content distribution and big data analytics. Simulation results are presented to show the effectiveness of the proposed framework. In addition, we discuss a number of challenges of implementing the proposed framework of software defined networking, caching and computing. [ABSTRACT FROM AUTHOR]