This work investigates the truthful auction for dependent task offloading in vehicular edge computing by considering the selfishness and rationality of participating nodes. Specifically, we first illustrate a truthfulness-guaranteed dependent task offloading architecture. Then, we formulate the Truthfulness-Guaranteed Dependent Task Offloading problem, aiming at maximizing the system utility (SU) while ensuring truthfulness and individual rationality in dynamic environments. Further, we design both centralized and distributed auction mechanisms to derive the optimal and approximate solutions, respectively. For centralized auction mechanism, we adopt the branch-and-price algorithm to determine the offloaded nodes, which yields maximum SU. Then, we adopt VCG mechanism to determine the payment of buyers. For distributed auction mechanism, each seller independently chooses the winning bid, and the buyer greedily chooses the offloaded node with maximum utility. Then, a novel payment mechanism regarding the cost of failed buyers is designed to guarantee the truthfulness and individual rationality. Finally, we build the simulation model and conduct the performance evaluation based on realistic vehicular trajectories. The results demonstrate that the proposed distributed auction mechanism achieves performance within approximately 4% of the optimal method, while significantly reducing computational complexity. Additionally, it significantly outperforms other methods in terms of system utility across various task requirements.

With rapid development of vehicular sensing and mobile communication technologies, cooperative sensing becomes an emerging paradigm of future intelligent transportation systems (ITSs). This paper investigates a LiDAR based cooperative sensing scenario in Vehicular Edge Computing (VEC). Specifically, we present the system architecture, in which vehicles with on-board LiDAR are able to detect objects via local processing of the sensed point-cloud data, and the outputs can be further shared via vehicle-to-vehicle (V2V) /vehicle-to-infrastructure (V2I) communications and fused in edge nodes. Then, we formulate the Edge Assisted Task Offloading (EATO) problem by considering the heterogeneous computation and communication capacities of vehicles and edge nodes, aiming at minimizing the average delay of the cooperative sensing tasks. Further, we propose a Multi-Armed Bandit (MAB)-based algorithm to make task offloading decisions adaptively. Finally, we implement the system prototype and give a comprehensive performance evaluation, which demonstrates the effectiveness of the proposed algorithm.

Traditional traffic event monitoring and detection solutions mainly rely on roadside surveillance cameras. However, existing solutions cannot be applied for traffic event augmentation due to both restricted monitoring angles and limited camera coverage. Therefore, this paper investigates a novel architecture for traffic event augmentation via vehicular edge computing. In particular, multiple vehicles can collaborate with roadside infrastructures for detecting, re-identification and augmenting certain traffic event via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. To enable such an application, we formulate the problem of multi-view augmentation task offloading (MATO) by considering the heterogeneous capabilities of vehicles and edge servers, which aims at minimizing average request delay. On this basis, we design the offloading scheduling framework and propose an adaptive real-time offloading algorithm (ARTO), which makes online offloading decision of object detection and re-identification, by balancing real-time workload among heterogeneous devices. Finally, we implement the hardware-in-the-loop testbed for performance evaluation. The comprehensive results demonstrate the superiority of the proposed algorithm in various realistic traffic scenarios.

Recent advances in wireless communication, sensing, and computing technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Nevertheless, it is challenging to process data and computation intensive tasks with strict time constraints due to heterogeneous communication, storage, and computation capacities of IoV network nodes, spotty wireless connections in vehicles and infrastructures, unevenly distributed workload, and high vehicles mobility. In this paper, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the fog nodes, and the terminals on processing data and computation intensive IoV tasks. Then, we formulate the real-time task offloading model, aiming at maximizing the task service ratio. Further, considering the dynamic requirements and resource constraints, we propose a real-time task offloading algorithm to adaptively categorize all tasks into four types, and then cooperatively offload them. Finally, we build the simulation model and give a comprehensive performance evaluation, which validates the performance of the proposed method.

With recent development of wireless communication, sensing and computing technologies, Internet of Vehicles (IoV) has attracted great attention in both academia and industry. Services with low communication latency and high reliability are necessary to enable safety-critical applications in IoV. Nevertheless, it is challenging to satisfy the service requirement due to unique characteristics of IoV, including limited wireless communication bandwidth, high vehicle mobility, massive data transmission, and overwhelming computation overhead. In view of this, we propose a novel vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the static fog and the mobile fog by defining corresponding service modes. On this basis, we further formulate a task offloading model, which quantitatively analyzes the characteristics of the three service modes and enables task offloading based on particular service requirements. Finally, we implement a traffic abnormity detection and warning system based on the proposed architecture as a case study. The hardware-in-the-loop performance evaluation not only demonstrates the effectiveness of the proposed architecture, but also enlightens future research directions on developing adaptive task offloading for dynamic IoV applications.

Effective control strategies are required to disperse incident-based traffic jams in urban networks when dispersal cannot be achieved simply by removing the obstruction. This paper develops a selection of such control strategies and demonstrates their effectiveness in dispersing incident-based traffic jams in two-way rectangular grid networks. Using the spatial topology of traffic jam propagation, we apply the concept of vehicle movement ban, which is frequently adopted in real urban networks as a temporary traffic management measure. Four control strategies were developed, which are referred to as single-line control, multiline control, area control, and diamond control. We also explore a combination of these control strategies and evaluate the impact of these control strategies on the changes in traffic jam size and congestion delay. Finally, we simulate the processes of traffic jam formation and dissipation using the cell transmission model and demonstrate the performance of the proposed strategies. Simulation results show that the proposed strategies can indeed disperse incident-based traffic jams efficiently.