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A geometry-based coverage strategy over urban VANETs

Published: 03 November 2013 Publication History

Abstract

Vehicular ad hoc networks have emerged as a promising field in wireless networking research. Unlike traditional wireless sensor networks, vehicular networks demand more consideration due to their assorted road topology, the high mobility of vehicles and the irregularly placed feasible region of deployment. As one of the most complex issues in vehicular networks, coverage strategy has been researched extensively, especially in complex urban scenarios. However, most existing coverage approaches are based on an ideal traffic map consisting of straight lines and nodes. These simplifications misrepresent the road networks. In order to provide more realistic vehicular networks deployment, this paper proposes a geometry-based coverage strategy to handle the deployment problem over urban scenarios. By taking the shape and area of road segments into account, our scheme suits different kinds of road topology and effectively solves the maximum coverage problem. To evaluate the effectiveness of our scheme, we compare this coverage strategy with α-coverage algorithm. The simulation result verifies that geometry-based coverage strategy culminates in a higher coverage ratio and a lower drop rate than α-coverage under the same constraints. The results also show that the deployment of Road Side Units (RSUs) in regions with high traffic flow is able to cover the majority of communication, so that less RSUs are able to provide better communication performance.

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  • (2024)Optimizing the Deployment of Static and Mobile Roadside Units Using a Branch-and-Price AlgorithmIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340775725:11(17078-17091)Online publication date: Nov-2024
  • (2024)Optimizing Roadside Unit Deployment in VANETs: A Study on Consideration of FailureIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337307925:9(10835-10850)Online publication date: Sep-2024
  • (2024)Deploying Roadside Unit Efficiently in VANETs: A Multi-Objective Delay-Based Optimization Strategy Using Lagrangian RelaxationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.331521325:2(1646-1660)Online publication date: Feb-2024
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      cover image ACM Conferences
      PE-WASUN '13: Proceedings of the 10th ACM symposium on Performance evaluation of wireless ad hoc, sensor, & ubiquitous networks
      November 2013
      164 pages
      ISBN:9781450323604
      DOI:10.1145/2507248
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 03 November 2013

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      Author Tags

      1. area coverage
      2. genetic algorithm
      3. road geometry
      4. road-side unit deployment
      5. vanets (vehicular ad hoc networks)

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      PE-WASUN '13 Paper Acceptance Rate 12 of 36 submissions, 33%;
      Overall Acceptance Rate 70 of 240 submissions, 29%

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      • (2024)Optimizing the Deployment of Static and Mobile Roadside Units Using a Branch-and-Price AlgorithmIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340775725:11(17078-17091)Online publication date: Nov-2024
      • (2024)Optimizing Roadside Unit Deployment in VANETs: A Study on Consideration of FailureIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337307925:9(10835-10850)Online publication date: Sep-2024
      • (2024)Deploying Roadside Unit Efficiently in VANETs: A Multi-Objective Delay-Based Optimization Strategy Using Lagrangian RelaxationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.331521325:2(1646-1660)Online publication date: Feb-2024
      • (2024)2CON-DEPLOY: A Fixed Infrastructure Deployment Strategy to Enable DTN in Vehicular Networks2024 IEEE 13th International Conference on Cloud Networking (CloudNet)10.1109/CloudNet62863.2024.10815843(1-8)Online publication date: 27-Nov-2024
      • (2022)A novel traffic simulator for evaluating complex SIoT-based algorithms in typical road traffic scenarios2022 25th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT57492.2022.10055903(300-305)Online publication date: 17-Dec-2022
      • (2021)Solutions for the Deployment of Communication Roadside Infrastructure for Streaming Delivery in Vehicular NetworksJournal of Network and Systems Management10.1007/s10922-021-09600-029:3Online publication date: 30-Apr-2021
      • (2021)Secured Intelligent Cooperative Communication in Vehicular Networks—A Comprehensive ReviewSoft Computing and Signal Processing10.1007/978-981-16-1249-7_30(319-326)Online publication date: 24-Jul-2021
      • (2019)Planning the Deployment of QoS-based Communication Infrastructures for Connected Vehicles using GRASP and Path Relinking2019 IEEE Latin-American Conference on Communications (LATINCOM)10.1109/LATINCOM48065.2019.8937994(1-6)Online publication date: Nov-2019
      • (2019)Joint Roadside Unit Deployment and Service Task Assignment for Internet of Vehicles (IoV)IEEE Internet of Things Journal10.1109/JIOT.2018.28824366:2(3271-3283)Online publication date: Apr-2019
      • (2019)An Algorithm for Mapping a Traffic Domain Into a Complex Network: A Social Internet of Things ApproachIEEE Access10.1109/ACCESS.2019.29066477(40925-40940)Online publication date: 2019
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