Beyond contact predictions in mobile opportunistic networks

Tatar, Alexandru; Phe-Neau, Tiphaine; Amorim, Marcelo Dias de; Conan, Vania; Fdida, Serge

doi:10.1109/wons.2014.6814723

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…[36] suggests that monitoring a node's vicinity up to four hops improves network performance while monitoring overhead is low. Subsequently, [37] studies the predictability of multi-hop opportunities in DTNs. The performance of non-sparse wireless networks has been studied from a percolation theory perspective in [16], [17], [38], [39], [40], [41], [42].…”

Section: Related Workmentioning

confidence: 99%

On the Performance Analysis of Epidemic Routing in Non-Sparse Delay Tolerant Networks

Rashidi¹,

Towsley²,

Mohseni-Kabir³

et al. 2020

Preprint

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We study the behavior of epidemic routing in a delay tolerant network as a function of node density. Focusing on the probability of successful delivery to a destination within a deadline (PS), we show that PS experiences a phase transition as node density increases. Specifically, we prove that PS exhibits a phase transition when nodes are placed according to a Poisson process and allowed to move according to independent and identical processes with limited speed. We then propose four fluid models to evaluate the performance of epidemic routing in non-sparse networks. A model is proposed for supercritical networks based on approximation of the infection rate as a function of time. Other models are based on the approximation of the pairwise infection rate. Two of them, one for subcritical networks and another for supercritical networks, use the pairwise infection rate as a function of the number of infected nodes. The other model uses pairwise infection rate as a function of time, and can be applied for both subcritical and supercritical networks achieving good accuracy. The model for subcritical networks is accurate when density is not close to the percolation critical density. Moreover, the models that target only supercritical regime are accurate.

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Section: Related Workmentioning

confidence: 99%

On the Performance Analysis of Epidemic Routing in Non-Sparse Delay Tolerant Networks

Rashidi¹,

Towsley²,

Mohseni-Kabir³

et al. 2020

Preprint

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“…However, as pointed out by Conan et al [106] and Passarella et al [107], these results focus on aggregate inter-contact distributions, and are not representative of the network behavior, which instead depends on the properties of individual pairs. An interesting addition to the standard contact and inter-contact analysis is proposed by Tatar et al, which consider in their model an extended notion of contact relationships [108].…”

Section: Delayed Offloadingmentioning

confidence: 99%

Data Offloading Techniques in Cellular Networks: A Survey

Rebecchi

Amorim

Conan

et al. 2015

IEEE Commun. Surv. Tutorials

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320

152

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One of the most engaging challenges for mobile operators today is how to manage the exponential data traffic increase. Mobile data offloading stands out as a promising and low cost solution to reduce the burden on the cellular network. To make this possible, we need a new hybrid network paradigm that leverages the existence of multiple alternative communication channels. This entails significant modifications in the way data is handled, affecting also the behavior of network protocols. In this paper, we present a comprehensive survey of data offloading techniques in cellular networks and extract the main requirements needed to integrate data offloading capabilities into today's mobile networks. We classify existing strategies into two main categories, according to their requirements in terms of content delivery guarantees: delayed and non-delayed offloading. We overview the technical aspects and discuss the state of the art in each category. Finally, we describe in detail the novel functionalities needed to implement mobile data offloading in the access network, as well as current and future research challenges in the field, with an eye toward the design of hybrid architectures.

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“…That is why, we inspired from the results on κ-vicinity from [12] to extend the simple FREAK solution [13] in a mechanism which would take benefit from multi-hop contacts. With this solution, that we named multi-level FREAK (ML-FREAK) DTN, we should be able to decrease problems like described earlier, that might occur in some networks.…”

Section: Where the Multi-level Freak Comes Frommentioning

confidence: 99%