Performance-Cost Tradeoff of Using Mobile Roadside Units for V2X Communication

Heo, Jeongyoon; Kang, Byungjun; Yang, Jong Oh; Paek, Jeongyeup; Bahk, Saewoong

doi:10.1109/tvt.2019.2925849

Cited by 45 publications

(16 citation statements)

References 33 publications

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“…As future work, we plan to adopt machine learning techniques to enable each vehicle to predict its neighbors' decision and find the optimal resource selection policy such as a recent study leveraging a deep learning tool for vehicular communications [52]. We will also investigate utilizing roadside units [53] for resource scheduling of V2V messages.…”

Section: Discussionmentioning

confidence: 99%

ATOMIC: Adaptive Transmission Power and Message Interval Control for C-V2X Mode 4

et al. 2021

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To support the emerging vehicle-to-everything (V2X) communication for autonomous vehicles and smart transportation services, 3rd Generation Partnership Project (3GPP) has recently introduced cellular V2X (C-V2X) standards. In C-V2X, radio resources can be managed not only centrally by the cellular network base station, but also in a completely distributed manner (Mode 4) without any cellular support. However, Mode 4 may suffer significant collisions and interference in a dense environment since each vehicle selects its own resources to transmit V2X messages autonomously without adapting appropriately to vehicle density. To address this challenge, we propose ATOMIC, an Adaptive Transmission pOwer and Message Interval Control scheme for C-V2X Mode 4, in which each vehicle utilizes real-time channel sensing and neighbor information to reduce channel contention for improved reliability and latency. Through analysis and extensive simulations, we show that ATOMIC outperforms the standard Mode 4 in both urban and highway scenarios especially in highly dense environments.INDEX TERMS Vehicle-to-vehicle (V2V) communication, vehicle-to-everything (V2X), cellular V2X (C-V2X) mode 4, TX power adaptation, rate control.

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Section: Discussionmentioning

confidence: 99%

ATOMIC: Adaptive Transmission Power and Message Interval Control for C-V2X Mode 4

et al. 2021

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“…D is the direction of the node, A is the availability of the node, and R is the range of the radio. Then the network priority (P N W ) could be calculated from (16).…”

Section: Ranking Of Data Nodes and Networkmentioning

confidence: 99%

“…Different standardizations have already addressed the safety-critical applications of the V2X by using redundant and multiple communication channels and low delay technologies [10]- [12]. The available solutions have cost and timing challenges to address as well [13]- [16]. Currently, V2X rely on multi-technology and multi-network capabilities in disseminating and collecting information among the vehicles [17]- [20].…”

Section: Introductionmentioning

confidence: 99%

Robust, Resilient and Reliable Architecture for V2X Communications

Khan

Ghosh

Busari

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive these further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plugin electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everythingto-everything (X2X) communications.

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“…Every vehicle has a communication coverage of 100 m and uses the 802.11p (WAVE) [ 49 ] protocol as the MAC protocol with a header size of 70 bytes. For the propagation delay and the propagation loss models [ 50 ], our simulations use the Constant Speed Propagation Delay Model [ 51 , 52 ] and the Nakagami Propagation Loss Model [ 53 , 54 ], respectively. We set the size of the requested content from 150 to 400 (MB).…”

Section: Performance Evaluationmentioning

confidence: 99%

Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks

Nam

Choi

Shin

et al. 2021

Sensors

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Content-Centric Vehicular Networks (CCVNs) are considered as an attractive technology to efficiently distribute and share contents among vehicles in vehicular environments. Due to the large size of contents such as multimedia data, it might be difficult for a vehicle to download the whole of a content within the coverage of its current RoadSide Unit (RSU). To address this issue, many studies exploit mobility-based content precaching in the next RSU on the trajectory of the vehicle. To calculate the amount of the content precaching, they use a constant speed such as the current speed of the vehicle requesting the content or the average speed of vehicles in the next RSU. However, since they do not appropriately reflect the practical speed of the vehicle in the next RSU, they could incorrectly calculate the amount of the content precaching. Therefore, we propose an adaptive content precaching scheme (ACPS) that correctly estimates the predictive speed of a requester vehicle to reflect its practical speed and calculates the amount of the content precaching using its predictive speed. ACPS adjusts the predictive speed to the average speed starting from the current speed with the optimized adaptive value. To compensate for a subtle error between the predictive and the practical speeds, ACPS appropriately adds a guardband area to the precaching amount. Simulation results verify that ACPS achieves better performance than previous schemes with the current or the average speeds in terms of the content download delay and the backhaul traffic overhead.

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Performance-Cost Tradeoff of Using Mobile Roadside Units for V2X Communication

Cited by 45 publications

References 33 publications

ATOMIC: Adaptive Transmission Power and Message Interval Control for C-V2X Mode 4

ATOMIC: Adaptive Transmission Power and Message Interval Control for C-V2X Mode 4

Robust, Resilient and Reliable Architecture for V2X Communications

Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks

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