Abstract

Vehicular Ad hoc NETworks (VANET) has been well studied for a long time as a means to exchange information among moving vehicles. As vehicular networks do not always have connected paths, vehicular networks can be regarded as a kind of delay-tolerant networks (DTNs) when the density of vehicles is not high enough. In this case, packet delivery ratio degrades significantly so that reliability of networks as an information infrastructure is hardly held. Past studies such as SADV (Static-node Assisted Data dissemination protocol for Vehicular networks) and RDV (Reliable Distance-Vector routing) showed that the assistance of low-cost unwired static nodes located at intersections, which work as routers to provide distance-vector or link-state routing functions, significantly improves the communication performance. However, they still have problems: SADV does not provide high-enough delivery ratio and RDV suffers from traffic concentration on the shortest paths. In this paper, we propose MP-RDV (Multi-Path RDV) by extending RDV with multiple paths utilization to improve performance against both of those problems. In addition, we apply a delay routing metric, which is one of the major metrics in this field, to RDV to compare performance with the traffic-volume metric, which is a built-in metric of RDV. Evaluation results show that MP-RDV achieves high load-balancing performance, larger network capacity, lower delivery delay, and higher fault tolerance against topology changes compared to RDV. As for routing metrics, we showed that the traffic-volume metric is better than the delay one in RDV because delay measurement is less stable against traffic fluctuation.

Highlights

  • Vehicles are regarded as a kind of mobile sensor nodes and vehicular ad hoc networks are regarded as a potential infrastructure to help collecting or delivering various data that may enhance our life

  • Evaluation results show that MP-RDV achieves high load-balancing performance, larger network capacity, lower delivery delay, and higher fault tolerance against topology changes compared to RDV

  • If vehicle density is low, Vehicular Ad hoc NETworks (VANET) is regarded as a kind of Delay-Tolerant Networks (DTNs), and the past contributions on DTN routing have proved that high packet delivery ratio is hardly achievable with sparsely located nodes; with simple schemes such as the epidemic routing or the spray-and-wait approach, a significant load for duplicated packets is required to improve delivery ratio of packets [1,2,3]

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Summary

Introduction

Vehicles are regarded as a kind of mobile sensor nodes and vehicular ad hoc networks are regarded as a potential infrastructure to help collecting or delivering various data that may enhance our life. Carry-and-forward behavior [8] that affords vehicles to have time to choose the next-hop vehicle has been deployed with techniques for selecting good next-hop vehicles such as utilizing vehicles’ trajectories (e.g., [9]) or estimating vehicle traffic amount of roads (e.g., [10]) Those techniques certainly improve the delivery ratio of packets in sparse scenarios of VANETs, the ratio is still too low in order to support practical applications over VANETs. The idea to utilize static, but not wire-connected wireless nodes located at intersections is introduced in SADV [12]. Yoshihiro et al proposed a distance-vector-based routing scheme RDV that provides the pre-configured expectation on packet delivery ratio by creating the sufficient number of packet copies at static nodes [13]. MP-RDV forks forwarding paths at each intermediate static node and creates duplicated packet copies for each of the paths to improve the load balancing performance as well as the delivery delay.

Vehicular Ad Hoc Networks
Deployment of Static Nodes in Vehicular Networks
Enhancing Vehicular Ad Hoc Networks with Unwired Static Nodes
Overview
Distance-Vector Based Routing Strategy
Packet Duplication
The Routing Metric
An Example
Selection of Multiple Paths
Extending APD for Multiple Paths
Designing Delay Metrics for MP-RDV
Methods
Results
Evaluating Delay Metrics
Conclusions
Full Text
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