RSU-Assisted Adaptive Scheduling for Vehicle-to-Vehicle Data Sharing in Bidirectional Road Scenarios

Ko, Byungjin; Li, Kai; Son, Sang H.; Park, Kyung‐Joon

doi:10.1109/tits.2019.2961705

Cited by 40 publications

(18 citation statements)

References 30 publications

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“…The larger offload rate means the smaller load on the cellular network. As depicted in Equation (15), offload is the number of non-realtime packets arriving at the destination vehicle, and created is the number of packets generated by vehicles. offload_rate = offload created (15)…”

Section: Offload Ratementioning

confidence: 99%

“…Guntuka et al [14] developed algorithms for predicting an efficient V2V offloading path using queues to deal with connection interruptions in V2V. Bo et al [15] investigated a novel decentralised scheduling, in which vehicles driving in opposite directions share the cached data items via V2V communication when they are out of the coverage of RSU.…”

Section: Introductionmentioning

confidence: 99%

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RLbR: A reinforcement learning based V2V routing framework for offloading 5G cellular IoT

Wang

et al. 2022

IET Communications

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5G cellular IoT has several advantages compared to other access technologies, enabling operators to serve a wider area and more IoT devices. However, in the urban transportation system, a massive number of vehicles exhaust the available resources in the cell, resulting in excessive load in the 5G cellular network. This article proposes a novel reinforcement learning based V2V routing (RLbR) framework, which offloads non-realtime traffic into the V2V network and significantly relieves the load of 5G cellular network. Meanwhile, we propose a V2V routing algorithm. Specifically, the Q-values of neighbouring vehicles are firstly calculated according to the cache factor CF and energy factor EF and evaluate the quality of neighbouring vehicles. Then, the position factor PF is calculated, based on which, the vehicle forwards the data packet. In addition, an environment model is designed to accelerate the convergence of Q-table. The results show that the RLbR framework brings the highest offload rate compared to the other three frameworks, and simultaneously, the proposed algorithm improves the lifetime of the V2V network and performs well in terms of delivery ratio and average delay.

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Section: Offload Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RLbR: A reinforcement learning based V2V routing framework for offloading 5G cellular IoT

Wang

et al. 2022

IET Communications

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“…Ko et. al proposed an algorithm named RSU Cooperationbased Adaptive Scheduling (RCAS) which consists of three mechanisms centralized scheduling mechanism, ad hoc scheduling mechanism for vehicles, and cluster management mechanism to dissemination data in a vehicular environment [45]. In this algorithm, the centralized scheduling mechanism used for RSU's coverage to disseminate data using cooperative I2V and V2V.…”

Section: Related Workmentioning

confidence: 99%

Mobility Prediction Based Multi-Directional Broadcasting for Both Highway and Urban Vehicular Sensor Networks

Ryu

Cha

2020

IEEE Access

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This paper proposes a mobility prediction based multi-directional broadcasting (MPMB) protocol for both highway and urban Vehicular Sensor Networks (VSNs). VSNs have to ensure the prompt dissemination of critical sensing data to all vehicles within the Region of Interest (ROI) to avoid various road dangers. Several research studies have proposed various broadcasting protocols, but most of them just focus on either highway or urban scenarios. MPMB protocol is derived from the mobility prediction based directed broadcasting (MPDB) protocol which is a single directional broadcasting protocol for highways. The main purpose of MPMB is to improve broadcasting efficiency in both highway and urban scenarios. To do this, MPMB adopts the adaptive directional sectors and the Store-Carry-Forward (SCF) scheme to select the next broadcasting (rebroadcasting) vehicles based on the mobility prediction. MPMB protocol consists of (i) mobility prediction stage and (ii) broadcasting stage. In the mobility prediction stage, each vehicle predicts the Link Available Time (LAT) of its all neighbor vehicles through periodical beaconing. In the broadcasting stage, each broadcasting vehicle adaptively selects a vehicle with the largest LAT value predicted in the previous mobility prediction stage as a rebroadcasting vehicle in each possible directional sector. The simulation results show that MPMB protocol has better performance in terms of packet delivery rate and packet delay in comparison with three well-known reference protocols in both highway and urban scenarios with various traffic density.

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“…OBU is a microwave device used to communicate with RSU. RSU plays an important role in the distribution and dissemination of traffic data [1], [2], i.e., RSU is a key component of collaborative and distributed applications in VANET [3] and can provide services for road users such as OBUs [4]. The applications in VANET, especially road safety application, mainly depend on the communication between OBU and OBU (V2V), OBU and RSU (V2I) [5]- [7], collectively known as V2X communication.…”

Section: Introductionmentioning

confidence: 99%

“…In this scenario, both communication and sensing functions are implemented on RSUs. Then, i) RSUs with sensing and communication functions not only passively wait for vehicles to upload road traffic data [1], [2], but also actively detect road conditions. ii) The radar function of RSU can play the role of predictive beamforming design and re-authentication for communication data [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Neighbor Discovery for VANET with Gossip Mechanism and Multi-packet Reception

Wei¹,

Chen²,

Yang³

et al. 2021

Preprint

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Neighbor discovery (ND) is a key initial step of network configuration and prerequisite of vehicular ad hoc network (VANET). However, the convergence efficiency of ND is facing the requirements of multi-vehicle fast networking of VANET with frequent topology changes. This paper proposes gossip-based information dissemination and sensing information assisted ND with MPR (GSIM-ND) algorithm for VANET. GSIM-ND algorithm leverages efficient gossip-based information dissemination in the case of multi-packet reception (MPR). Besides, through multi-target detection function of multiple sensors installed in roadside unit (RSU), RSU can sense the distribution of vehicles and help vehicles to obtain the distribution of their neighbors. Thus, GSIM-ND algorithm leverages the dissemination of sensing information as well. The expected number of discovered neighbors within a given period is theoretically derived and used as the critical metric to evaluate the performance of GSIM-ND algorithm. The expected bounds of the number of time slots when a given number of neighbors needs to be discovered is derived as well. The simulation results verify the correctness of theoretical derivation. It is discovered that GSIM-ND algorithm proposed in this paper can always reach the short-term convergence quickly. Moreover, GSIM-ND algorithm is more efficient and stable compared with completely random algorithm (CRA), scan-based algorithm (SBA) and gossip-based algorithm. The convergence time of GSIM-ND algorithm is 40%-90% lower than that of these existing algorithms for both low density and high density networks. Thus, GSIM-ND can improve the efficiency of ND algorithm.

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RSU-Assisted Adaptive Scheduling for Vehicle-to-Vehicle Data Sharing in Bidirectional Road Scenarios

Cited by 40 publications

References 30 publications

RLbR: A reinforcement learning based V2V routing framework for offloading 5G cellular IoT

RLbR: A reinforcement learning based V2V routing framework for offloading 5G cellular IoT

Mobility Prediction Based Multi-Directional Broadcasting for Both Highway and Urban Vehicular Sensor Networks

Neighbor Discovery for VANET with Gossip Mechanism and Multi-packet Reception

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