CRAWDAD dataset dartmouth/campus (v. 2007-02-08)

Kotz, David; Henderson, Tristan; Abyzov, Ilya; Yeo, Jihwang

doi:10.15783/c79g6j

Cited by 6 publications

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Following the right path: Using traces for the study of DTNs

Baudic

Pérennou

Lochin

2016

Computer Communications

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Contact traces collected in real situations represent a popular material for the study of a Delay Tolerant Network. Three main use cases can be defined for traces: social analysis, performance evaluation and statistical analysis. In this paper, we perform a review on the technicalities of real trace collection and processing. First, we identify several factors which can influence traces during collection, filtering or scaling, and illustrate their impact on the conclusions, based on our experience with four datasets from the literature. We subsequently propose a list of criteria to be verified each time a trace is to be used, along with recommendations on which filters to apply depending on the envisioned use case. The rationale is to provide guidelines for researchers needing to perform trace analysis in their studies.

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Following the right path: Using traces for the study of DTNs

Baudic

Pérennou

Lochin

2016

Computer Communications

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Reputation-based crowdsourced Wi-Fi topology discovery

Frangoudis

Polyzos

2015

Computer Networks

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Optimal storage allocation on throwboxes in Mobile Social Networks

et al. 2015

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a b s t r a c tIn the context of Mobile Social Networks (MSNs), a type of wireless storage device called throwbox has emerged as a promising way to improve the efficiency of data delivery. Recent studies focus on the deployment of throwboxes to maximize data delivery opportunities. However, as a storage device, the storage usage of throwboxes has seldom been addressed by existing work. In this paper, the storage allocation of throwboxes is studied as two specific problems: (1) if throwboxes are fixed at particular places, how to allocate storage to the throwboxes; and (2) if throwboxes are deployable, how to conduct storage allocation in combination with throwbox deployment. Two optimization models are proposed to calculate the optimal storage allocation with a knowledge of the contact history of users. Real trace based simulations demonstrate that the proposed scheme is able to not only decrease data loss on throwboxes but also improve the efficiency of data delivery.

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CRAWDAD dataset dartmouth/campus (v. 2007-02-08)

Cited by 6 publications

References 0 publications

Following the right path: Using traces for the study of DTNs

Following the right path: Using traces for the study of DTNs

Reputation-based crowdsourced Wi-Fi topology discovery

Optimal storage allocation on throwboxes in Mobile Social Networks

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