Internet of Vehicles (IoV) has drawn great interests recent years. Various IoV applications have emerged for improving the safety, efficiency and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving, emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this paper, we integrate mobile edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge computing aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with reprocessing mechanism to EC-SDIoV. Since the optimization problem is non-convex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

As one of the key services for non-safety applications in Vehicular Ad-hoc Networks (VANETs), the Popular Content Distribution (PCD) has become a hot issue in recent years. In popular content distribution, the On-Board Units (OBUs) passing the Area of Interest (AoI) receive popular content broadcast by the Road Side Units (RSUs). However, due to the high speed of OBUs, limited bandwidth, and unstable wireless connections, only a portion of the popular content can be received by OBUs. To address this issue, in this paper, a cooperative V2V-aided transmission scheme based on a coalitional game (CVCG) is proposed. The scheme allows the OBUs to cooperate with their neighbors to provide the missing popular content. In addition, a coalition graph game algorithm is designed for optimizing the cooperative behaviors among OBUs. The performance of our CVCG scheme is evaluated by different metrics compared to other three content distribution schemes. The numerical results show that the proposed CVCG scheme could outperform the three schemes in terms of the number of iterations for 99% finished PCD, the average content completion percentage, and the number of completed OBUs.

Swarm of drones, as an intensely significant category of swarm robots, is widely used in various fields, e.g., search and rescue, detection missions, military, etc. Because of the limitation of computing resource of drones, dealing with computation-intensive tasks locally is difficult. Hence, the cloud-based computation offloading is widely adopted, nevertheless, for some latency-sensitive tasks, e.g., object recognition, path planning, etc., the cloud-based manner is inappropriate due to the excessive delay. Even in some harsh environments, e.g., disaster area, battlefield, etc., there is no wireless infrastructure existed to combine the drones and cloud center. Thus, to solve the problem encountered by cloud-based computation offloading, in this paper, Fog Computing aided Swarm of Drones (FCSD) architecture is proposed. Considering the uncertainty factors in harsh environments which may threaten the success of FCSD processing tasks, not only the latency model, but also the reliability model of FCSD is constructed to guarantee the high reliability of task completion. Moreover, in view of the limited battery life of the drone, we formulated the problem as the task allocation problem which minimized the energy consumption of FCSD under the constraints of latency and reliability. Furthermore, to speed up the process of the optimization problem solving to improve the practicality, relying on the recent advances in distributed convex optimization, we develop a fast Proximal Jacobi Alternating Direction Method of Multipliers (ADMM) based distributed algorithm. Finally, simulation results validate the effectiveness of our proposed scheme.

Existing research on data collection using wireless mobile vehicle network emphasizes the reliable delivery of information. However, other performance requirements such as life cycle of nodes, stability and security are not set as primary design objectives. This makes data collection ability of vehicular nodes in real application environment inferior. By considering the features of nodes in wireless IoV, such as large scales of deployment, volatility and low time delay, an efficient data collection algorithm is proposed for mobile vehicle network environment. An adaptive sensing model is designed to establish vehicular data collection protocol. The protocol adopts group management in model communication. The vehicular sensing node in group can adjust network sensing chain according to sensing distance threshold with surrounding nodes. It will dynamically choose a combination of network sensing chains on basis of remaining energy and location characteristics of surrounding nodes. In addition, secure data collection between sensing nodes is undertaken as well. The simulation and experiments show that the vehicular node can realize secure and real-time data collection. Moreover, the proposed algorithm is superior in vehicular network life cycle, power consumption and reliability of data collection by comparing to other algorithms.

In recent years, the rapid improvement of sensor and wireless communication technologies powerfully impels the development of advanced cooperative driving systems, generating the demands to form the Internet of Vehicles (IoV). With the assistance of cooperative communication among vehicles, the road safety can be greatly enhanced in the IoV. In this paper, we propose a cooperative driving scheme for vehicles at intersections in the IoV. First, the driver’s intention is modeled by the BP neural network trained with driving dataset. Then, the identified intention is used as the control matrix of the Kalman filter model, by which the vehicle trajectory can be predicted. Finally, by collecting the information of vehicles’ trajectories at the intersections, we develop a collision probability evaluation model to reflect the conflict level among vehicles at intersections. Through obtained collision probability, the driver or the autonomous control unit can determine the next step to avoid the possible collisions. Numerical results show that our proposed scheme has high accuracy in terms of driver’s intention identification, trajectory prediction and collision probability evaluation.