Abstract

To bring VANET into reality, it is crucial to devise routing protocols that can exploit the inherited characteristics of VANET environment to enhance the performance of the running applications. Previous studies have shown that a certain routing protocol behaves differently under different presumed mobility patterns. Bypass-AODV is a new optimization of the AODV routing protocol for mobile ad-hoc networks. It is proposed as a local recovery mechanism to enhance the performance of the AODV routing protocol. It shows outstanding performance under the Random Waypoint mobility model compared with AODV. However, Random Waypoint is a simple model that may be applicable to some scenarios but it is not sufficient to capture some important mobility characteristics of scenarios where VANETs are deployed. In this paper, we will investigate the performance of Bypass-AODV under a wide range of mobility models including other random mobility models, group mobility models, and vehicular mobility models. Simulation results show an interesting feature that is the insensitivity of Bypass-AODV to the selected random mobility model, and it has a clear performance improvement compared to AODV. For group mobility model, both protocols show a comparable performance, but for vehicular mobility models, Bypass-AODV suffers from performance degradation in high-speed conditions.

Highlights

  • Research has gained a significant advance in the development of routing protocols for wireless ad hoc networks [1, 2]

  • The observations made and the conclusions drawn from the simulation studies may be misleading

  • We examine the impact of different random mobility models as well as group and vehicular mobility models on the performance of Bypass-Ad hoc On-demand Distance Vector (AODV) and AODV routing protocols

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Summary

Introduction

Research has gained a significant advance in the development of routing protocols for wireless ad hoc networks [1, 2]. The movement pattern of mobile nodes plays an important role in the performance analysis of mobile and wireless networks. The extra signaling traffic over the air interface consumes radio resources, and it increases the interferences that affect the performance of other mobile nodes. Some researchers [3, 4] have observed that the performance of routing algorithms may be influenced by the choice of mobility models. Random models are not a good choice to simulate the real-world mobility scenarios because usually mobile users either move toward certain attraction points such as classrooms or train stations, or move in certain directions such as vehicles. Devising a realistic mobility model that accurately reflects actual user mobility is a key challenge in evaluating the performance of any routing algorithm, and it has a significant effect on the obtained results. If the model is unrealistic, invalid conclusions may be drawn

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