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Greta: Towards a General roadside unit deployment Framework

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第7期23卷 7602-7617页

作者： Wu, Xianjing Liu, Zhidan Li, Zhenjiang Zhao, Shengjie City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Minist Educ Engn Res Ctr Key Software Technol Smart City Percept & Planning Shanghai 201804 Peoples R China Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China

As an essential component, roadside units (RSUs) play an indispensable role in realizing Vehicle-to-Everything (V2X) by seamlessly connecting various intelligent devices and vehicles. To facilitate the construction of V2X, much research has been done in designing effective RSU deployment strategies. However, most of these efforts are largely limited by design utility and deployment scalability. To address the limitations of previous works, this paper proposes a general RSU deployment framework, Greta, which can evaluate candidate deployment sites from different perspectives with rich input data, and satisfy different requirements on optimization metrics. To this end, we model the general RSU deployment problem as a customized reinforcement learning (RL) problem that intelligently explores the deployment environment to find a good deployment strategy. Specifically, we design an effective data profiling network to extract features from multi-modality input data. These extracted features are gradually weighted, fused, and encoded as part of the state representation of the RL model. We further design new reward functions considering various deployment metrics and propose an action space pruning scheme to speed up model training. We implement a prototype system of Greta and extensively evaluate its performance using real-world data. The results show Greta achieves remarkable performance gains compared to recent RSU deployment methods.

关键词： Measurement Roads Optimization Vehicle-to-everything Feature extraction Urban areas Mobile computing Reinforcement learning roadside unit deployment vehicle-to-everything

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Reducing congestion and emissions via roadside unit deployment under mixed traffic flow

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International Journal of Coal Science & Technology 2023年第1期10卷 197-211页

作者： Yuhao Liu Zhibin Chen Siyuan Gong Han Liu Shanghai Key Laboratory of Urban Design and UrbanScience NYU ShanghaiShanghaiChina Division of Engineering and Computer Science NYUShanghaiShanghaiChina The Joint Laboratory for Internet of Vehicles Ministryof Education-China Mobile Communications CorporationXi’anShaanxiChina School of Information Engineering Chang’an UniversityXi’anShaanxiChina Central Research Institute China Coal Technologyand Engineering Group(CCTEG)BeijingChina

It is expected that for a long time the future road trafc will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV under the mixed trafc fow,roadside units(RSUs)can also improve the quality of information received by CAVs,thereby infuencing the routing behavior of CAV *** paper explores the possibility of leveraging the RSU deployment to afect the route choices of both CAVs and RVs and the adoption rate of CAVs so as to reduce the network congestion and *** this end,we frst establish a logit-based stochastic user equilibrium model to capture drivers’route choice and vehicle type choice behaviors provided the RSU deployment plan is ***,CAV users’perception error can be reduced by higher CAV penetration and denser RSUs deployed on the road due to the improved information *** the established equilibrium model,the RSU deployment problem is then formulated as a mathematical program with equilibrium *** active-set algorithm is presented to solve the deployment problem *** results suggest that an optimal RSU deployment plan can efectively drive the system towards one with lower network delay and emissions.

关键词： roadside unit deployment Connected autonomous vehicle Information quality Congestion and emission

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A New Hybrid Hierarchical roadside unit deployment Scheme Combined with Parking Cars

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APPLIED SCIENCES-BASEL 2024年第16期14卷 7032页

作者： Li, Hongming Ji, Yuqing Wang, Ziwei Univ Shanghai Sci & Technol Dept Traff Engn Shanghai 200093 Peoples R China Tongji Univ Minist Educ Sch Transportat Engn Key Lab Rd & Traff Engn Shanghai 201804 Peoples R China

This paper proposes a new hybrid hierarchical roadside unit (RSU) deployment algorithm that combines traditional RSU deployment strategies with temporary RSUs (t-RSUs) selected from many parked vehicles. The driving vehicles in the corresponding RSU coverage section are divided into clusters and the demand is thus summarized. We first solve the problem of how to choose the appropriate cars serving as t-RSUs and then optimize the RSU deployment with the dynamic existing t-RSUs. We simulate the deployment application in a downtown area of Shanghai, China, using the simulation of urban mobility (SUMO), version 1.11.0, an open-source traffic simulation package, and its traffic-control interface (TraCI) with Python. The simulation results show that, within a given economic cost, the proposed hybrid hierarchical RSU deployment algorithm outperforms the maximum vehicle coverage deployment algorithm (MVCD) and the random deployment algorithm (RandDeploy) in terms of overall vehicle coverage and point-to-point vehicle connectivity ratio. Our scheme provides new solutions and ideas for the deployment of RSUs in future urban traffic environments.

关键词： roadside unit deployment parked car mixed traffic flow vehicular ad hoc networks

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Intelligent roadside unit deployment in Vehicular Network

Intelligent Roadside Unit Deployment in Vehicular Network

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IEEE International Conference on Electro Information Technology (EIT)

作者： Xu, Xiangyu Cao, Xiang Wolffe, Gregory Du, Jie Grand Valley State Univ Sch Comp Allendale MI 49401 USA

ISBN: (纸本)9781665418461

Intelligent Transportation Systems (ITS) have been an important research area focused on improving the efficiency of future transportation systems. In an ITS, vehicular networks are used to collect and transmit data from various points of interest. In these vehicular networks, the roadside unit (RSU) is a key infrastructure component facilitating communications among all elements of the transportation system. Inspired by the concept of edge computing, we believe RSUs can store data from points of interest and perform certain pre-processing tasks before further uploading the data to the Cloud. It is important to carefully deploy RSUs in vehicular networks in order to ensure full coverage of data generators. However, RSUs have limited data storage capacities and restricted communication ranges. In this paper, we jointly consider these two aspects with a goal of deploying the minimal number of RSUs while still covering all points of interest. Performance evaluation demonstrates our solution improves coverage performance under these constraints.

关键词： Intelligent Transportation System Vehicular Networks roadside unit deployment Data Storage

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Augmented Powell-Based Krill Herd Optimization for roadside unit deployment in Vehicular Ad Hoc Networks

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JOURNAL OF TESTING AND EVALUATION 2019年第6期47卷 4108-4127页

作者： Saravanan, D. Janakiraman, S. Chandraprabha, K. Kalaipriyan, T. Raghav, R. S. Venkatesan, S. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur 522502 Andhra Pradesh India Pondicherry Univ Dept Banking Technol Pondicherry 605014 India RVS Coll Engn & Technol Dept Comp Sci & Engn Dindigal 624005 Tamil Nadu India Indian Inst Informat Technol Dept Informat Technol Allahabad 211015 Uttar Pradesh India

This article focuses on roadside unit (RSU) deployment based on the analysis of aggregation delay causes between the accident-occurring location and the nearby RSU. Information aggregation in vehicular ad hoc networks using RSU is one of the phenomenal concepts that look ahead in today's recent advancements, since the consistency inaggregation and dissemination of data in a volatile network is high when compared with vehicle to vehicle communications. Deploying the RSU as appropriate in rural areas is cost-sensitive since the collection of information is not up to the mark when compared with urban regions. A mathematical model called Powell's Method and a bioinspired algorithm, namely krill herd algorithm, have been hybridized and proposed in this research article for effective RSU deployment. The proposed algorithm has been tested under three different road maps, and the consistency of the algorithm has been evaluated under appropriate performance measures.

关键词： roadside unit deployment vehicular ad hoc networks optimization Powell's method krill herd

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An evolutionary algorithm for roadside unit deployment with betweenness centrality preprocessing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 88卷 776-784页

作者： Moura, Douglas L. L. Cabral, Raquel S. Sales, Thiago Aquino, Andre L. L. Univ Fed Alagoas UFAL Nucleo Ciencias Exatas NCEx Arapiraca Alagoas Brazil Univ Fed Alagoas UFAL Inst Comp Maceio Alagoas Brazil

This paper presents a genetic algorithm strategy to improve the deployment of roadside units in VANETs. We model the problem as a Maximum Coverage with Time Threshold Problem and the network as a graph, and perform a preprocessing based on the betweenness centrality measure. Moreover, we show that by using a simple genetic algorithm with few interactions, we achieve better results when compared with other strategies. We consider five realistic datasets to evaluate our approach and the experiments show that it finds better results in all scenarios, mainly when compared with the greedy-based approach, which increased up to 20% of the vehicle coverage in specific scenarios. Additionally, the betweenness centrality preprocessing helps the solution convergence by selecting candidate intersections, which suggests that this preprocessing is a good measure to explore in these scenarios. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Vehicular networks roadside unit deployment Betweenness centrality

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Optimizing the deployment of Static and Mobile roadside units Using a Branch-and-Price Algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 17078-17091页

作者： Liang, Bingjie Xu, Xinyue Lu, Wenqi Wang, Fujun Ran, Bin Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Univ Wisconsin Sch Traff & Transportat Beijing 53706 Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

The roadside unit (RSU), which enables vehicle-to-infrastructure communication, is essential for improving the communication performance of vehicular ad hoc networks. However, optimizing the deployment of RSUs while considering their deployment at both fixed locations and on mobile vehicles remains a challenging issue. To bridge this gap, we develop two spatio-temporal networks derived from vehicle trajectories. Subsequently, the joint deployment challenge of static and mobile RSUs is articulated as a mixed-integer programming model. After linearization, the model can be directly solved by CPLEX. Additionally, the integrated optimization problem can also be formulated as a route-based model. Due to the exponential growth of route numbers, a branch-and-price (BAP) algorithm is designed to solve the route-based model. Within the framework of the BAP, we develop a heuristic technique for generating effective initial solutions. Based on the characteristics of the spatio-temporal network, a directed acyclic graph shortest path algorithm is utilized to accelerate the solution of column generation pricing problem at each node. Simulation examples are presented to demonstrate the proposed algorithm. The results indicate that the BAP can generate verifiable high-quality solutions and has a significant speed advantage over CPLEX for large-scale problems. Furthermore, a series of sensitivity analyses are conducted to assess the system's responses to various influencing factors.

关键词： roadside unit deployment spatio-temporal network route-based model branch-and-price

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A multi-objective roadside units deployment strategy based on reliable coverage analysis in Internet of Vehicles

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AD HOC NETWORKS 2024年 164卷

作者： Huo, Yan Yang, Ruixue Jing, Guanlin Wang, Xiaoxuan Mao, Jian Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Shandong Univ Sch Comp Sci & Technol Qingdao 266200 Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China

The deployment of roadside units (RSUs) in the Cellular-Vehicle to Everything enabled Internet of Vehicles is crucial for the transition from individual intelligence of vehicles to collective intelligence of vehicle-road collaboration. In this paper, we focus on improving the adaptability of RSU deployment to real scenarios, and optimizing deployment costs and vehicle-oriented service performance. The RSU deployment problem is modeled as a Multi-objective Optimization Problem (MOP), with a cost model integrating the purchase and installation costs, and a service-oriented Quality of Service (QoS) model adopting the total time the RSUs cover the vehicles as the evaluation metric. Specifically, we propose an RSU reliable coverage analysis method based on Packet Delivery Ratio model to estimate the coverage distances in different scenarios, which will be used in QoS calculation. Then, an evolutionary RSU deployment algorithm is designed to solve the MOP. The performance of the proposed method is simulated and discussed in real road network and dynamic scenarios. The results prove that our method outperforms the baseline method in terms of significant cost reduction and total coverage time improvement.

关键词： Cellular-Vehicle to Everything roadside unit deployment Reliable coverage analysis Multi-objective optimization Evolutionary algorithm

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File downloading oriented roadside units deployment for vehicular networks

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JOURNAL OF SYSTEMS ARCHITECTURE 2013年第10期59卷 938-946页

作者： Liu, Yazhi Niu, Jianwei Ma, Jian Wang, Wendong Hebei United Univ Coll Informat Engn Tangshan 063000 Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Vehicular Ad hoc Networks (VANETs) have been recently introduced to provide high-speed Internet access to vehicles by deploying 802.11 enhanced roadside units (RSUs) along roads. However, few file downloading oriented RSU deployment strategies have been proposed. In this paper, we propose a new RSU deployment strategy for file downloading in VANETs. The encounters between vehicles and RSUs are modeled as a time continuous homogeneous Markov chain. The optimal inter-meeting time between vehicles and RSUs is analyzed based on the encounter model. Then, the road network is modeled as a weighted undirected graph, and a RSU deployment algorithm is designed based on the depth-first traversal algorithm for edges of a graph. Simulation results show that the proposed RSU deployment algorithm can satisfy the file downloading service requirements with the lowest RSU deployment cost. (C) 2013 Elsevier B.V. All rights reserved.

关键词： VANETs File downloading roadside unit deployment

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