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Using Location Based Social Networks for Quality-aware Participatory Data Transfer

This paper explores the use of location-based social networks (LBSNs) for quality-aware participatory data transfer. It presents a case study on data transfer in real and virtual worlds, discusses the variations of the quality-aware problem, and proposes a heuristic approach for optimal data source placement. The study concludes with insights into the complexity of the problem and suggestions for future work.

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Using Location Based Social Networks for Quality-aware Participatory Data Transfer

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  1. Using Location Based Social Networks for Quality-aware Participatory Data Transfer HoutanShirani-Mehr, FarnoushBanaei-Kashani and Cyrus Shahabi Infolab, University of Southern California Second International Workshop on Location-Based Social Networks (LBSN’10)

  2. Outline • Introduction • Problem Definition • A Case Study • Conclusions and Future Work

  3. D D D D D Introduction

  4. Data Transfer Media • Wireless or wired communication infrastructures Installing and using such infrastructures may be expensive and/or impossible

  5. D D D D D D S A B PDT Data Transfer in Real world Data Transfer in Virtual world LBSN LBSN LBSN LBSN

  6. PDT Network of real world connections Network of virtual world connections

  7. Different PDT Variations

  8. D D Quality of Transferred Data • Sources Placement • Data Routing • Q(P): quality of transferred data during T D

  9. Problem Definition: Quality-Aware PDT (Q-PDT) • Input • Sources S={s1,s2,…,sn} • Destinations D={d1,d2,…,dm} • IndividualsU={u1,u2,…,uo} • Constraints • Devices should be reachable • An LBSN L to specify friendship relation • Communication resources d2 d1 Q-PDT is NP-hard (the proof can be found in the paper) • Objective • To maximize Q(P) during T by • Placing data sources and destinations • Instructing optimal trajectories

  10. Case Study

  11. Methodology • T=30 minutes • PDT participants • Synthetic social network (scale free model) with 500 nodes • Participants movements • GPS tracks of vehicles in the city of Beijing • Individuals data transfer • When vehicles pass by, data is transferred • Each individual uses virtual network on average twice to transfer data during T • Heuristic approach to place data sources • Located in the locations with the highest frequency of visit and at least 1km apart

  12. Results

  13. Conclusions and Future Work • Conclusions • Introduced variations of the problem of Q-PDT • Studied the complexity of Q-PDT • Future work • Development of efficient heuristics for different Q-PDT variations

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