As vehicles playing an increasingly important role in people’s daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computation-intensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirements, service requirements, and design considerations, and (v) proposing several open research issues of designing cloud/edge/fog computing architectures for CVs.

Computation offloading over fog computing has the potential to improve reliability and reduce latency in future networks. This paper considers a scenario where roadside units (RSUs) are installed for offloading tasks to the computation nodes including nearby fog nodes and a cloud center. To guarantee the reliable communication, we formulate the first subproblem of power allocation, and leverage the conditional value-at-risk approach to analyze the successful transmission probability in the worse-case channel condition. To complete computation tasks with low latency, we formulate the second subproblem of task allocation into a multi-period generalized assignment problem (MPGAP), which aims at minimizing the total delay by offloading tasks to the ‘right’ fog nodes at ‘right’ period. Then, we propose a modified branch-and-bound algorithm to derive the optimal solution and a heuristic greedy algorithm to obtain approximate performance. In addition, the master problem is proposed as a non-convex optimization problem, which considers both the reliability-guaranteed and delay-sensitive requirements. We design the Lagreedy algorithm by combining the subgradient algorithm and the heuristic algorithm. Comprehensive evaluations demonstrate that the Lagreedy is able to obtain the shortest delay with a high power consumption, while the branch-and-bound algorithm can achieve both shorter delay and lower power consumption with reliability guarantees.

Multi-access edge computing (MEC) has emerged as a promising paradigm because of its good performance for computation-intensive and latency-critical applications. However, the enormous computing requests from computation service subscribers (CSSs) still cannot be satisfied by pre-existing edge computation nodes (ECNs). To fully utilize the advantage of the MEC network, a hierarchical computation offloading framework is developed under network virtualization (NV) scenario. Accordingly, a two-step sequential process is designed to stimulate the proposed framework. In the first step, an incentive mechanism is proposed in which more temporary ECNs can be motivated by MEC operator and then join the MEC network. Without perfect ECN information, the optimal contract items (the ECN's CPU contribution and reward) between the MEC operator and ECNs can be achieved by taking account of individual rationality (IR) and incentive compatible (IC) constraints. After acquiring the ECNs' CPU contributions, the computing resource allocation problem between the ECNs and CSSs is then considered in the second step. Since the CSSs have private information, a Bayesian matching game with externality is leveraged to model the problem. Whereas, the conventional resident-oriented Gale-Shapley (RGS) algorithm cannot ensure the stability. Hence, an iterative matching algorithm that can always converge to stable results is developed. Finally, simulation results demonstrate that our proposed two-step sequential decision process can significantly improve social welfare considering the practical scenarios, with reasonable computational complexity.

A sharded blockchain with the Proof-of-Stake (PoS) consensus protocol has advantages in increasing throughput and reducing energy consumption, enabling the resource-limited participants to manage transactions and in a decentralized way and obtain rewards at a lower cost, e.g., Internet-of-Things (IoT) users. However, the latest PoS (e.g., Casper) requires a steep security deposit, which is the key to provide more robust security guarantees than Proof of Work, but not practical for the owners of heterogeneous IoT devices. This article considers any individual and institute who owns the IoT devices as the potential participant and focuses on designing the proper security deposits in a practical scenario with hidden information and hidden action. To bridge blockchain and the IoT users, we study the problem of balancing the security incentive and the economic incentive under two cases: 1) stake oriented and 2) effort oriented. We propose two joint models under the contract theory framework to efficiently address the problems: 1) joint adverse selection and moral hazard and 2) joint adverse selection and tournament. Both optimal contracts can provide a maximized profit for blockchain. The optimal rewards and security deposits for different types of participants can be determined accordingly. Simulations indicate that the proposed models can overcome asymmetric information and offer feasible contracts. Moreover, it demonstrates that both joint models can provide an economic incentive for the participants without reducing security incentives for the sharded blockchain.

In order to overcome the inherent latency in multi-user orthogonal multiple access (OMA) unmanned aerial vehicle (UAV) networks. In this paper, we investigate a UAV-enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by the ground users. To maximize the users' sum-rate, we formulate a optimization problem, in which the UAV deployment position and the power control are jointly optimized subject to the transmission power constraints and the quality of service (QoS) constraints. However, this problem is a mixed integer non-convex optimization problem and thus it is difficult to solve directly. Therefore, we first transform the non-convex optimization problem into a convex one based on the first-order approximation technique and the penalty function method. Then, a successive convex approximate (SCA) technique based iterative algorithm as well as its initialization scheme are developed to find a suboptimal solution to the original problem. Afterwards, a low-complexity approximate algorithm is proposed to reduce the high computational complexity of the iterative algorithm. Numerical results show that our proposed iterative algorithm and low-complexity approximate algorithm can achieve a similar performance, and they can efficiently improve the sum-rate compared with OMA scheme and conventional NOMA scheme.

Owning to the powerful support of 5 G/B5G technologies, it is promising that, in a vehicular network, the ever-increasing demands for artificial intelligence applications along with privacy concerns can be fulfilled by introducing federated learning (FL) into vehicular networks. However, in such a 5 G/B5G cellular-based vehicular network, distributed clients, i.e., vehicles, usually suffer from intermittent connectivity to the uplink base station (BS) due to the small cell networking and their mobility, and thereby the FL execution time and the learning performance cannot be guaranteed. In order to ensure a required execution time and the FL performance, we propose to investigate the client selection schemes in the uplink-downlink decoupled 5 G/B5G networks. Due to the bursty feature of uplink transmissions, we first incorporate a flexible delay analytical tool, i.e., martingale theory, to characterize the access delay of each vehicle, and represent it in the form of delay violation probability. Then, given an energy budget, adjustable parameters are optimized to maximize the global learning performance. Supposing that all the clients participate in the FL and they can outperform the systematic FL performance requirement, the client selection methods under different criteria are further investigated. Especially, we propose a low-complexity algorithm to select clients in terms of enhancing energy efficiency. Numerical results are provided to demonstrate the effectiveness of the proposed client selection method in both guaranteeing the FL execution time and meeting the systematic learning performance. The energy efficiency of the vehicular FL system can also be enhanced by employing the proposed method.