An analysis of the information spreading delay in heterogeneous mobility DTNs

Picu, Andreea; Spyropoulos, Thrasyvoulos; Hossmann, Theus

doi:10.1109/wowmom.2012.6263682

Cited by 43 publications

(24 citation statements)

References 28 publications

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“…To capture the heterogeneity of patterns, we adopt a Marked Poisson Process model of node contacts [9], [10]. In this model, the meeting times of any two nodes (i, j) follow a Poisson Process with rate λ ij = λp ij .…”

Section: Diffusionmentioning

confidence: 99%

DROid: Adapting to individual mobility pays off in mobile data offloading

Rebecchi

Amorim

Conan

2014

2014 IFIP Networking Conference

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Abstract-Cellular operators count on the potentials of offloading techniques to relieve their overloaded data channels. Beyond standard access point-based offloading strategies, a promising alternative is to exploit opportunistic direct communication links between mobile devices. Nevertheless, achieving efficient deviceto-device offloading is challenging, as communication opportunities are, by nature, dependent on individual mobility patterns. We propose, design, and evaluate DROiD (Derivative Re-injection to Offload Data), an original method to finely control the distribution of popular contents throughout a mobile network. The idea is to use the infrastructure resources as seldom as possible. To this end, DROiD injects copies through the infrastructure only when needed: (i) at the beginning, in order to trigger the dissemination, (ii) if the evolution of the opportunistic dissemination is below some expected pace, and (iii) when the delivery delay is about to expire, in order to guarantee 100% diffusion. Our strategy is particularly effective in highly dynamic scenarios, where sudden creation and dissolution of clusters of mobile nodes prevent contents to diffuse properly. We assess the performance of DROiD by simulating a traffic information service on a realistic largescale vehicular dataset composed of more than 10,000 nodes. DROiD substantially outperforms other offloading strategies, saving more than 50% of the infrastructure traffic even in the case of tight delivery delay constraints. DROiD allows terminalto-terminal offloading of data with very short maximum reception delay, in the order of minutes, which is a realistic bound for cellular user acceptance.

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Section: Diffusionmentioning

confidence: 99%

DROid: Adapting to individual mobility pays off in mobile data offloading

Rebecchi

Amorim

Conan

2014

2014 IFIP Networking Conference

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“…In [11,12], the heterogeneous network model was established and analyzed. In this kind of model, the contact between nodes is based on IDD assumption.…”

Section: Related Workmentioning

confidence: 99%

“…What's more, more complex behavior between nodes was researched and proposed for the routing designing for DTNs [8]. In [11,12], experimental simulation results showed that heterogeneous model was more reliable and reasonable than the IID assumption based models.…”

Section: Related Workmentioning

confidence: 99%

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A heterogeneous theoretical framework for the analysis of delay tolerant networks

Bai

Ouyang

2014

2014 IEEE 5th International Conference on Software Engineering and Service Science

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In this paper, we first present a heterogeneous theoretical framework for DTNs where the contact between nodes is based on independent differently distributed assumption. Moreover, the message forwarding process follows a Gamma Distribution. Then, we propose a new spray routing scheme based on this framework. Moreover, we compare our routing protocol with traditional spray routing (TSR) protocols and epidemic (EP) routing scheme on the ONE simulator with both real dataset of Infocom06 and scenario of Working Day Movement Model. Simulation results show that our scheme is significantly better than TSR and EP in the following four aspects: average number of copies, average number of hop counts, average delivery latency and delivery rate.

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“…Common for all these works is that they assume homogeneous system. Heterogeneity is introduced by separating network nodes into multiple mobility classes in [9], [13], or modeling completely heterogeneous networks, as in [11]. However, it is debatable whether using these analytic models gives enough insight over simulations to account for their complexity.…”

Section: Related Workmentioning

confidence: 99%

Epidemic content distribution

Pajevic

Karlsson

Helgason

2013

Proceedings of the 16th ACM International Conference on Modeling, Analysis &Amp; Simulation of Wireless and Mobile Systems

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Epidemic content dissemination has been proposed as an approach to mitigate frequent link disruptions and support content-centric information dissemination in opportunistic networks. Stochastic modeling is a common method to evaluate performance of epidemic dissemination schemes. The models introduce assumptions which, on one hand make them analytically tractable, while on the other, ignore attested characteristics of human mobility. In this paper, we investigate the fitness and limitations of an analytical stochastic model for content dissemination by comparison with experimental results obtained from real mobility traces. Our finding is that a homogeneous analytic model is unable to capture the performance of content dissemination with respect to content delivery delays.

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An analysis of the information spreading delay in heterogeneous mobility DTNs

Cited by 43 publications

References 28 publications

DROid: Adapting to individual mobility pays off in mobile data offloading

DROid: Adapting to individual mobility pays off in mobile data offloading

A heterogeneous theoretical framework for the analysis of delay tolerant networks

Epidemic content distribution

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