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Routing in Sparse Vehicular Ad Hoc Wireless Networks

Published: 01 October 2007 Publication History

Abstract

A vehicular ad hoc network (VANET) may exhibit a bipolar behavior, i.e., the network can either be fully connected or sparsely connected depending on the time of day or on the market penetration rate of the wireless communication devices. In this paper, we use empirical vehicle traffic data measured on 1-80 freeway in California to develop a comprehensive analytical framework to study the disconnected network phenomenon and its network characteristics. These characteristics shed light on the key routing performance metrics of interest in disconnected VANETs, such as the average time taken to propagate a packet to disconnected nodes (i.e., the re-healing time). Our results show that, depending on the sparsity of vehicles or the market penetration rate of cars using Dedicated Short Range Communication (DSRC) technology, the network re-healing time can vary from a few seconds to several minutes. This suggests that, for vehicular safety applications, a new ad hoc routing protocol will be needed as the conventional ad hoc routing protocols such as Dynamic Source Routing (DSR) and Ad Hoc On-Demand Distance Vector Routing (AODV) will not work with such long re-healing times. In addition, the developed analytical framework and its predictions provide valuable insights into the VANET routing performance in the disconnected network regime.

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  1. Routing in Sparse Vehicular Ad Hoc Wireless Networks

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      cover image IEEE Journal on Selected Areas in Communications
      IEEE Journal on Selected Areas in Communications  Volume 25, Issue 8
      October 2007
      107 pages

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      IEEE Press

      Publication History

      Published: 01 October 2007

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      • (2024)Deployment Optimization of Roadside Unit With Failure Probability Based on Stochastic Mixed Traffic EquilibriumIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335201525:7(7792-7804)Online publication date: 1-Jul-2024
      • (2022)Joint Optimization for MEC Computation Offloading and Resource Allocation in IoV Based on Deep Reinforcement LearningMobile Information Systems10.1155/2022/92305212022Online publication date: 1-Jan-2022
      • (2022)VeNet: Hybrid Stacked Autoencoder Learning for Cooperative Edge Intelligence in IoVIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.317037223:9(16643-16653)Online publication date: 1-Sep-2022
      • (2022)Modeling and Analysis of Multi-Relay Cooperative Communications in C-V2X NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314998723:9(16371-16385)Online publication date: 1-Sep-2022
      • (2022)An Enhanced Information Sharing Roadside Unit Allocation Scheme for Vehicular NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314080123:9(15462-15475)Online publication date: 1-Sep-2022
      • (2022)Physical layer security of vehicular networks with cooperative jamming helpersPhysical Communication10.1016/j.phycom.2022.10176253:COnline publication date: 1-Aug-2022
      • (2022)Secure and ultra-reliable provenance recovery in sparse networksAd Hoc Networks10.1016/j.adhoc.2022.102860131:COnline publication date: 1-Jun-2022
      • (2022)An Efficient Cross Layer Design of Stability Based Clustering Scheme Using Ant Colony Optimization in VANETsWireless Personal Communications: An International Journal10.1007/s11277-022-09849-x126:4(3001-3019)Online publication date: 1-Oct-2022
      • (2022)Deployment Strategy of Highway RSUs for Vehicular Ad Hoc Networks Considering Accident NotificationGreen, Pervasive, and Cloud Computing10.1007/978-3-031-26118-3_10(132-148)Online publication date: 2-Dec-2022
      • (2021)Environment-based roadside unit deployment for urban scenarios2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)10.1109/PIMRC.2016.7794889(1-6)Online publication date: 10-Mar-2021
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