GPS Calibrated Ad-hoc Localization for Geosocial Networking Dexter H. Hu Cho-Li Wang Yinfeng Wang

Outline Introduction –Mobile Twitter for Geosocial Networking Related Work –MCL and Amorphous MobiAmorph algorithm Performance Evaluation and Analysis Discussion Conclusion GPS Calibrated Ad-hoc Localization for Geosocial Networking

Geosocial Networking From social networking -> mobile social networking -> geosocial networking –A new type of social networking in which geographic services and capabilities such as geocoding and geotagging are used to enable additional social dynamics.social networkinggeographicgeocodinggeotagging Application Example –Location-planning –Social Shopping –Trip tracking GPS Calibrated Ad-hoc Localization for Geosocial Networking

4 Mobile Twitter 1.Practical for real life usage and encourage ad-hoc information sharing, –Mobile social applications will be more meaningful and location-aware –Twit social events with location information attached. Car accident, Taxi call, Voting, Disaster/rescue 2.Localization is possible without the deployment of large infrastructure –Help of GPS-enabled mobile users 3.Under certain mobility model of pedestrians in typical urban environment, accurate GPS information can quickly propagate to non-GPS users GPS Calibrated Ad-hoc Localization for Geosocial Networking

Usage Scenario and Components GPS Calibrated Ad-hoc Localization for Geosocial Networking

Usage Scenario and Components (cont'd) GPS Calibrated Ad-hoc Localization for Geosocial Networking

Localization with Historical Data and Moving Velocity GPS Calibrated Ad-hoc Localization for Geosocial Networking Figure 2 possible area

Related Work Range-free Localization –Monte Carlo Localization (MCL) Posterior distribution of a node’s possible locations using a set of weighted samples –Amorphous Similar variant DV-HOP, pop-counting technique which is similar to distance vector routing. Each seed broadcasts its location to neighbors and other nodes try to estimate their distance to seeds GPS Calibrated Ad-hoc Localization for Geosocial Networking

Outline Introduction –Mobile Twitter: Geosocial Networking Related Work –MCL and Amorphous MobiAmorph algorithm Performance Evaluation and Analysis Discussion Conclusion GPS Calibrated Ad-hoc Localization for Geosocial Networking

Common Notations GPS Calibrated Ad-hoc Localization for Geosocial Networking

MobiAmorph Algorithm GPS Calibrated Ad-hoc Localization for Geosocial Networking Receive enough fresh information

Localization with Historical Data and Moving Velocity GPS Calibrated Ad-hoc Localization for Geosocial Networking Figure 2 possible area

MobiAmorph Algorithm Relaxed Trilateration: –Multilateration of Amorphous needs at least 3 reference points. –Location estimating with overlapping circles can still have a decent estimation even there are only two reference points available. Increased coverage. Historical Data –Last estimated location to increase accuracy and coverage. With relaxed trilateration, only one reference information is need –Two hop count packet Increased coverage and accuracy GPS Calibrated Ad-hoc Localization for Geosocial Networking

Outline Introduction –Mobile Twitter: Geosocial Networking Related Work –MCL and Amorphous MobiAmorph algorithm Performance Evaluation and Analysis Discussion Conclusion GPS Calibrated Ad-hoc Localization for Geosocial Networking

Performance Evaluation and Analysis MobiReal Simulator Evaluation Goals: 1.Coverage and Accuracy of MobiAmorph with MCL and Amorphous 2.MobiAmorph under various settings for recommended configuration in real deployment 3.Mobile Twitter’s power/memory consumption by MobiAmorph GPS Calibrated Ad-hoc Localization for Geosocial Networking

Evaluation Scenarios GPS Calibrated Ad-hoc Localization for Geosocial Networking Open Area (100m x 100m) Street Building (500m x 500m)

Effect of Packet Interval and Seed Ratio in Street and Open Area ParameterValue Node Speed (m/s)1.5, 3, 5 Radio Range (m)10 Seed Ratio0.2, 0.3, 0.4, 0.5 Packet Interval5, 15, 30, 60, 90 Density30 GPS Calibrated Ad-hoc Localization for Geosocial Networking

Effect of Packet Interval and Seed Ratio in Street and Open Area (cont'd) 18 Street Open

GPS Calibrated Ad-hoc Localization for Geosocial Networking OpenStreet

MobiAmorph Performance on Street Scenario ParameterValue Node Speed (m/s)1.5, 3, 5 Radio Range (m)10 Seed Ratio0.2, 0.3, 0.4, 0.5 Packet Interval5, 15, 30, 60, 90 Density10, 20, 30, 40 GPS Calibrated Ad-hoc Localization for Geosocial Networking

MobiAmorph Performance on Street Scenario (cont'd) 21

Mobile Twitter Deployment Evaluation on Android phone GPS Calibrated Ad-hoc Localization for Geosocial Networking

Outline Introduction –Mobile Twitter: Geosocial Networking Related Work –MCL and Amorphous MobiAmorph algorithm Performance Evaluation and Analysis Discussion Conclusion GPS Calibrated Ad-hoc Localization for Geosocial Networking

Discussion Resolution Limitation – Theoretical limitation for using only connectivity information Privacy and Security for Adoption –Malicious seeds –Corrupted relay nodes –Application Message encrypted Pedestrian Mobility Model –Urban Pedestrian Flows (UPF) GPS Calibrated Ad-hoc Localization for Geosocial Networking

GPS Calibrated Ad-hoc Localization for Geosocial Networking Conclusion Ad hoc localization with the help of GPS information in urban environment with pedestrians We compared MobiAmorph with other two distributed range-free localization algorithms. The Mobile Twitter application is developed with the MobiAmorph algorithm on the Android to boost adoption of geosocial networking.

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GPS Calibrated Ad-hoc Localization for Geosocial Networking