Broadcast speedup in vehicular networks via information teleportation

Philippe Jacquet, Dalia Popescu, Bernard Mans

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)


The goal of this paper is to increase our understanding of the fundamental communication properties in urban vehicle-to-vehicle mobile networks by exploiting the self-similarity and hierarchical organization of modern cities. We use an innovative model called “hyperfractal” that captures the self-similarities of both the traffic and vehicle locations, and yet avoids the extremes of regularity and randomness. We use analytical tools to derive matching theoretical upper and lower bounds for the information propagation speed in an urban delay tolerant network (i.e., a network that is disconnected at all time, and thus uses a store-carry-and-forward routing model). We prove that the average broadcast time behaves as n1−δ (times a slowly varying function), where δ depends on the precise fractal dimension. Furthermore, we show that the broadcast speedup is due in part to an interesting self-similar phenomenon, that we denote as information teleportation. This phenomenon arises as a consequence of the topology of the vehicle traffic, and triggers an acceleration of the broadcast time. We show that our model fits real cities where open traffic data sets are available. The study presents simulations that confirm the validity of the bounds in multiple realistic settings, including scenarios with variable speed.
Original languageEnglish
Title of host publication43rd IEEE Conference on Local Computer Networks, LCN 2018
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781538644133
ISBN (Print)9781538644140
Publication statusPublished - 2018
Event43rd IEEE Conference on Local Computer Networks, LCN 2018 - Chicago, United States
Duration: 1 Oct 20184 Oct 2018


Conference43rd IEEE Conference on Local Computer Networks, LCN 2018
Country/TerritoryUnited States


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