Evolutionary V2X Technologies Toward the Internet of Vehicles: Challenges and Opportunities

Zhou, Haibo; Xu, Wenchao; Chen, Jiacheng; Wang, Wei

doi:10.1109/jproc.2019.2961937

Cited by 469 publications

(191 citation statements)

References 77 publications

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“…(2) Task offloading mode The task offloading mode mainly includes three stages: task transmission stage, task calculation stage and task result return stage [25]. In the task transmission stage, according to the system model, IoV users multiplex cellular channel to transmit tasks to MEC server.…”

Section: B Calculation Modelmentioning

confidence: 99%

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

Wang

Li³

et al. 2020

IEEE Access

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With the rapid increase of vehicles, the explosive growth of data flow and the increasing shortage of spectrum resources, the performance of existing task offloading scheme is poor, and the onboard terminal can't achieve efficient computing. Therefore, this paper proposes a task offload strategy based on reinforcement learning computing in edge computing architecture of Internet of vehicles. Firstly, the system architecture of Internet of vehicles is designed. The Road Side Unit receives the vehicle data in community and transmits it to Mobile Edge Computing server for data analysis, while the control center collects all vehicle information. Then, the calculation model, communication model, interference model and privacy issues are constructed to ensure the rationality of task offloading in Internet of vehicles. Finally, the user cost function is minimized as objective function, and double-layer deep Q-network in deep reinforcement learning algorithm is used to solve the problem for real-time change of network state caused by user movement. The results show that the proposed offloading strategy can achieve fast convergence. Besides, the impact of user number, vehicle speed and MEC computing power on user cost is the least compared with other offloading schemes. The task offloading rate of our proposed strategy is the highest with better performance, which is more suitable for the scenario of Internet of vehicles.

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Section: B Calculation Modelmentioning

confidence: 99%

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

Wang

Li³

et al. 2020

IEEE Access

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“…There are various use cases involved in the V2X services, e.g., emergency brake warning, pre-crash sensing warning, road hazard warning, congestion alert, speed guiding, autonomous driving, remote driving, etc. [ 8 , 9 , 10 ]. Thus, one fundamental element in C-V2X is positioning, namely recognizing the car’s absolute and relative positions concerning other surrounding objects such as vehicles, pedestrians, traffic signs, and other buildings.…”

Section: Introductionmentioning

confidence: 99%

A Highly Accurate Positioning Solution for C-V2X Systems

Liu

Peng

Junjie

et al. 2021

Sensors

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Cellular vehicle-to-everything (C-V2X) is essential in enabling safe, reliable, and efficient transportation services. It serves as serve as the foundation for vehicles to communicate with each other and everything around them. One fundamental element in C-V2X is positioning, namely extracting the vehicle’s absolute and relative positions concerning other objects such as buildings, pedestrians, traffic signs, and other vehicles. However, its feasibility in enabling vehicular positioning has not been fully explored yet. In this paper, key performance indicators (KPIs) for C-V2X positioning have been described firstly. Then positioning challenges and conventional positioning methods for C-V2X are reviewed. Afterward, two user equipment (UE)-based and UE-assisted C-V2X positioning architectures are proposed, and key technologies are also described. Lastly, testing and typical application cases are provided.

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“…A S vehicles playing an increasingly important role in people's daily life, an intelligent transportation system (ITS), which aims to provide smooth urban traffic, autonomous driving, accurate route navigation, etc., has received considerable attention from both academia and industry [1][2][3][4][5][6][7][8]. In order to realize the vision of ITS, various types and massive amounts of data are required to be processed reliably within a very limited time.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Vehicular-Based Architecture for Vehicular Networks: A Case Study on Computation Offloading

et al. 2020

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In order to realize an intelligent transportation system (ITS) which will provide smooth urban traffic, autonomous driving, accurate route navigation, etc., enormous computations need to be migrated from cloud centers to edge nodes, especially for the services requiring stringent latency. In addition to base stations and road side units (RSUs), vehicles can be alteratively considered as a kind of computation resources. In this paper, a hierarchical vehicular-based architecture which consists of cloud centers and vehicles is investigated. Computation offloading performance in the hierarchical architecture is also studied. In specific, the main components in vehicular networks and their characteristics on communication and computations are presented firstly. Several communication techniques that are essential in enabling computation offloading among these components are then discussed. Secondly, a hierarchical vehicular-based architecture, which integrates the main components, is constructed. Thirdly, a case study on computation offloading in the proposed architecture is conducted. In the concerned scenario, the computation offloading problem is modelled as a multi-dimensional multiple knapsack problem (MMKP). Two algorithms are investigated, among which, the first algorithm is a greedy heuristic method providing a sub-optimal solution with a low computational complexity. The second algorithm is a modified branch and bound (B&B) method, which can obtain the best solution with a high computational complexity. Numerical results are also presented to verify the performance of the two algorithms. It can be demonstrated that the proposed architecture can migrate more computations from cloud centers to vehicular nodes, when the computations require more communication resources.

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Evolutionary V2X Technologies Toward the Internet of Vehicles: Challenges and Opportunities

Cited by 469 publications

References 77 publications

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

A Highly Accurate Positioning Solution for C-V2X Systems

A Hierarchical Vehicular-Based Architecture for Vehicular Networks: A Case Study on Computation Offloading

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