Optimization Deployment of Roadside Units with Mobile Vehicle Data Analytics

Cao, Xuemei; Cui, Qimei; Zhang, Sihai; Jiang, Xueying; Wang, Ning

doi:10.1109/apcc.2018.8633531

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Furthermore, Cao et al [ 84 ] proposed an RSU optimized deployment scheme as a multi-objective optimization problem for mathematical modeling based on large vehicle data, which improves the quality of time-sensitive services while also reducing deployment costs. They proposed a two-step solution in which they obtained the initial RSU deployment location based on road topology and analyzed big vehicle data.…”

Section: Optimization Problems In Vehicular Networkmentioning

confidence: 99%

Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

Peyman

Fluechter

Panadero

et al. 2023

Sensors

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Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an `Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.

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Section: Optimization Problems In Vehicular Networkmentioning

confidence: 99%

Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

Peyman

Fluechter

Panadero

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Then, they proposed an optimal algorithm based on the greedy approach and dynamic programming, which dynamically reduces search space. Cao et al 23 presented an RSU deployment problem to minimize the transmission delay between the vehicle and the RSU and to minimize the total installation cost of the RSUs. Their proposal to solve this problem is based on large vehicle data and a branch‐bound algorithm.…”

Section: Related Terminologiesmentioning

confidence: 99%

An enhanced pseudonym certificates distribution mechanism for connected vehicles

Ercan

Ayaida

Messai

2022

Int J Communication

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Summary Cooperative Intelligent Transportation System (C‐ITS) is a growing research field since it improves traffic safety and efficiency. However, the security of C‐ITS messages is an important issue to deal with. This paper proposes a cost‐efficient solution that can help in the deployment of C‐ITS. The idea herein presented is to introduce trusted road operator vehicles (ROVs), to provide certificates for other vehicles when needed. These certificates, which are generally provided by roadside units (RSUs), are mandatory for the security and the anonymity of the communication. The main objective of this work is to help in enhancing the security of C‐ITS by allowing connected vehicles to download the needed certificates without using a physical fixed infrastructure of RSUs. The latter cannot be, in fact, widely deployed due to their high cost. The alternative herein proposed is to delegate the task of providing certificates to some trusted vehicles, namely, ROVs, which are in all the cases moving around the city to ensure some assigned missions. A mathematical model, considering both ROVs and RSUs, is proposed to quantify the effects of introducing ROVs on the probability of downloading certificates. The results show that ROVs almost double the communication probability between the vehicles and the certificate providers from 40.1% to 78.7% on a highway with a high traffic density. The higher the communication probability, the easier downloading certificates is. Moreover, realistic simulations are conducted using Simulation of Urban Mobility (SUMO) traffic simulator together with Network Simulator 3 (NS3) network simulator to show the efficiency of our proposal. The simulation results demonstrate a significant enhancement in the number of downloaded certificates and the number of exchanged messages, even when a few numbers of ROVs are introduced.

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“…Chi et al [14] proposed a RSU layout method based on cross priority and applied three different algorithms, greedy, dynamic, and hybrid, to realize the optimal deployment of RSU in cities. Cao et al [15] proposed an optimized deployment scheme based on the large-scale vehicle trajectory data, where K-nearest neighbours and branch-andbound algorithms were proposed to obtain the optimal deployment of RSU. In a word, the above research work mainly applies the algorithm to realize the deployment of RSUs.…”

Section: Introductionmentioning

confidence: 99%

“…Existing work regarding RSU deployment schemes can be roughly divided into three categories: mathematical models, heuristic algorithms with game theory, and graph theory. Although greedy algorithm, genetic algorithm, and other ones are used in aforementioned works [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] to solve the RSU deployment problem, multiobjective optimization algorithm is not leveraged to solve multiple conflicting ones. The deployment of RSU often involves multiple conflicting objectives, thus facilitating the utilization of multiobjective evolutionary 2…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Differential Evolution with Discrete Elite Guide in Internet of Vehicles Roadside Unit Deployment

Wang

et al. 2021

Wireless Communications and Mobile Computing

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In a vehicular ad hoc network (VANET), roadside units (RSUs) are installed at roadside and intersections to process vehicle-to-infrastructure communication, collect and analyse intelligent vehicle traffic data, send information to vehicles, and achieve early warning of safe driving of vehicles. Owning to the high cost of implementing and maintaining RSUs, it is of vital importance to determine where and how many RSUs to deploy. Optimal RSU deployment requires both a small number of RSUs and the maximum coverage of vehicle running process, which constitutes a conflicting multiobjective problem. Nevertheless, existing works do not explicitly utilize multiobjective algorithm to solve the RSU deployment problem. Therefore, a multiobjective differential evolution approach is proposed in this work to solve the problem. Firstly, to conquer the complexity of urban road RSU deployment, the static model is established. Secondly, in the proposed multiobjective differential evolution with discrete elitist guide (MODE-deg), the sigmoid function is applied to discrete individual values. Finally, elitist individuals are selected based on crowding distance ranking and nondominated ranking to generate new individuals, which further improve the convergence speed and population performance. Experimental results show that MODE-deg can generate the optimal nondominant solution set with good convergence and diversity, in contrast to other multiobjective evolutionary algorithms in five test functions of ZDT.

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Optimization Deployment of Roadside Units with Mobile Vehicle Data Analytics

Cited by 7 publications

References 19 publications

Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

An enhanced pseudonym certificates distribution mechanism for connected vehicles

Multiobjective Differential Evolution with Discrete Elite Guide in Internet of Vehicles Roadside Unit Deployment

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