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Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting
Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting
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Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting
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Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting
Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting

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Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting
Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting
Paper

Information Dissemination Speed in Delay Tolerant Urban Vehicular Networks in a Hyperfractal Setting

2019
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Overview
This paper studies the fundamental communication properties of urban vehicle 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 but avoids the extremes of regularity and randomness. We use analytical tools to derive 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 \\(n^{1-\\delta}\\) times a slowly varying function, where \\(\\delta\\) 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 {\\em 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. We present simulations confirming the validity of the bounds in multiple realistic settings, including scenarios with variable speed, using both QualNet and a discrete-event simulator in Matlab.