Cognitive Internet of Vehicles

Cao

International Journal of Distributed Sensor Networks

et al. 2018

Internet of Vehicles has become a promising way to realize the evolution from vehicular ad hoc networks and nextgeneration intelligent transportation system into future autonomous driving scenarios, clean-energy intelligent vehicles, and Smart Cities. However, multicasting service messages on available service channels and periodic exchanges of beacon messages on control channel cause the problem of efficiently scheduling those messages via multichannel transmission for intelligent vehicle terminal, to support real-world applications in Internet of Vehicles scenario. In this article, we investigate the intelligent vehicle terminal architecture and, particularly, design the wireless communication board by incorporating multicasting and congestion control modules. Especially, we present a multicast data delivery scheme with random-delay lowest-cost constraint to transfer service messages on service channels. Furthermore, a priority-aware congestion control scheme is also proposed by considering differentiated priorities of beacon messages on control channel, to cope with the congestion problem at bottleneck vehicle node. Based on the proposed schemes, we build up the RanLow (Random-delay Lowest-cost) module and the priority-aware congestion control (PARCEL) module by enabling multicasting and congestion control together in wireless communication board of the intelligent vehicle terminal architecture. Finally, the experimental results and comparison show that our devised RanLow module and PARCEL module are feasible and more efficient than existing schemes.

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MAC²: Enabling multicasting and congestion control with multichannel transmission for intelligent vehicle terminal in Internet of Vehicles

Cao

International Journal of Distributed Sensor Networks

et al. 2018

“…When the value of ψ gradually increases, the risk of collision increases; when the value of ψ decreases, the risk of collision decreases. When ψ > 1, it means there is no risk of collision at present [13,14].…”

Section: Berkeley Algorithmmentioning

confidence: 99%

A Safety Collision Avoidance Algorithm Based on Comprehensive Characteristics

Wang

et al. 2020

Complexity

Aiming at the requirements of vehicle safety collision avoidance system, a safety collision avoidance algorithm based on environmental characteristics and driver characteristics is proposed. By analyzing the relationship between collision avoidance time and the environment, a safety time model is established. In the established safety time model, parameters based on driver characteristics are added, which increases the flexibility of the algorithm. The algorithm can adapt to more different driving conditions and give appropriate warning thresholds. After simulation and comparison with other algorithms, the algorithm proposed in this paper can satisfied the requirements of reducing vehicle collision risk. The effectiveness and feasibility of the algorithm are verified, and the safety of vehicle driving can be improved.

“…With rapid advances in mobile computing and wireless communication technologies, the demands of mobile end users have been largely met by deploying edge computing [1] and other solutions on the ground, while employing Internet of Vehicles as a supplementary infrastructure to accommodate unmanned applications [2], [3]. Recently, researchers have shown increasing interest towards processing in the air complex tasks as automatic cruise, aerial photography, and precision target identification.…”

Section: Introductionmentioning

confidence: 99%

Ready Player One: UAV-Clustering-Based Multi-Task Offloading for Vehicular VR/AR Gaming

Tian

Yang

et al. 2019

IEEE Network

Self Cite

With rapid development of unmanned aerial vehicle (UAV) technology, application of the UAVs for task offloading has received increasing interest in the academia. However, real-time interaction between one UAV and the mobile edge computing (MEC) node is required for processing the tasks of mobile end users, which significantly increases the system overhead and is unable to meet the demands of large-scale artificial intelligence (AI) based applications. To tackle this problem, in this article, we propose a new architecture for UAV clustering to enable efficient multi-modal multi-task task offloading. By the proposed architecture, the computing, caching and communication resources are collaboratively optimized using AI based decision-making. This not only increases the efficiency of UAV clusters, but also provides insight into the fusion of computation and communication.