Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Building Transmission Backbone for Highway Vehicular Networks: Framework and Analysis (1807.02237v1)

Published 6 Jul 2018 in cs.NI

Abstract: The highway vehicular ad hoc networks, where vehicles are wirelessly inter-connected, rely on the multi-hop transmissions for end-to-end communications. This, however, is severely challenged by the unreliable wireless connections, signal attenuation and channel contentions in the dynamic vehicular environment. To overcome the network dynamics, selecting appropriate relays for end-to-end connections is important. Different from the previous efforts (\emph{e.g.}, clustering and cooperative downloading), this paper explores the existence of stable vehicles and propose building a stable multi-hop transmission backbone network in the highway vehicular ad hoc network. Our work is composed of three parts. Firstly, by analyzing the real-world vehicle traffic traces, we observe that the large-size vehicles, \emph{e.g.}, trucks, are typically stable with low variations of mobility and stable channel condition of low signal attenuation; this makes their inter-connections stable in both connection time and transmission rate. Secondly, by exploring the stable vehicles, we propose a distributed protocol to build a multi-hop backbone link for end-to-end transmissions, accordingly forming a two-tier network architecture in highway vehicular ad hoc networks. Lastly, to show the resulting data performance, we develop a queueing analysis model to evaluate the end-to-end transmission delay and throughput. Using extensive simulations, we show that the proposed transmission backbone can significantly improve the reliability of multi-hop data transmissions with higher throughput, less transmission interruptions and end-to-end delay.

Citations (18)

Summary

We haven't generated a summary for this paper yet.