Highway vehicular delay tolerant networks: Information propagation speed properties

Emmanuel Baccelli*, Philippe Jacquet, Bernard Mans, Georgios Rodolakis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

Abstract

In this paper, we provide a full analysis of the information propagation speed in bidirectional vehicular delay tolerant networks such as roads or highways. The provided analysis shows that a phase transition occurs concerning the information propagation speed, with respect to the vehicle densities in each direction of the highway. We prove that under a certain threshold, information propagates on average at vehicle speed, while above this threshold, information propagates dramatically faster at a speed that increases quasi-exponentially when the vehicle density increases. We provide the exact expressions of the threshold and of the average information propagation speed near the threshold, in case of finite or infinite radio propagation speed. Furthermore, we investigate in detail the way information propagates under the threshold, and we prove that delay tolerant routing using cars moving on both directions provides a gain in propagation distance, which is bounded by a sublinear power law with respect to the elapsed time, in the referential of the moving cars. Combining these results, we thus obtain a complete picture of the way information propagates in vehicular networks on roads and highways, which may help designing and evaluating appropriate vehicular ad hoc networks routing protocols. We confirm our analytical results using simulations carried out in several environments (The One and Maple).

Original languageEnglish
Article number6071007
Pages (from-to)1743-1756
Number of pages14
JournalIEEE Transactions on Information Theory
Volume58
Issue number3
DOIs
Publication statusPublished - Mar 2012

Fingerprint

Dive into the research topics of 'Highway vehicular delay tolerant networks: Information propagation speed properties'. Together they form a unique fingerprint.

Cite this