Crowdsourced Trace Similarity with Smartphones
Demetrios Zeinalipour-Yazti, Christos Laoudias, Constantinos Costa,
Michail Vlachos, Maria I. Andreou, Dimitrios Gunopulos
University of Cyprus, IBM Research Zurich, Open University
of Cyprus and University of Athens
Goals and Contributions
Problem: Find the K users moving more similarly to a
query trajectory Q, in a Smartphone Network.
• Privacy: User trajectories and User identities are
not disclosed to the Query Processor.
• Performance: a) In-situ data storage of trajectories (on
smartphone flash) and b) Query Processing using a TopK Query Processing Algorithm that uses Bound Scores*
• Ubiquity: Our system works both outdoors (using GPS)
and indoors (using WLAN Signal Strength)
Similarity Comparison
ignore majority of noise
– Flexible matching in time (ignore temporal noise)
– Flexible matching in space (ignore spatial noise)
The SmartTrace Framework
Performance Evaluation
High Level Idea
System Model
Smartphone Energy: ↓ 81%
Android-based Smartphone Implementation
Server Console
“No Sharing” Policy
SmartTrace Indoors (WLAN RSS)
• Ubuntu Linux
• JDK 6, ~1500 LOC
SmartTrace Outdoors (GPS)
• HTC Desire smartphones
• Android 2.1 (Eclair)
• Google Map API
• ~2500 LOC, ~250 lines XML
• 510KB installation package APK
• Runs on Dalvik VM (future:
native C with Android NDK)
SmartTrace Client GUI
• Query devices by example
• Plot and iterate through the
responses using a variety of
presentation styles
• Configure parameters (e.g. K)
• Control privacy settings
• Online/Offline modes for
recorded scenario playback
• GPS/WiFi modes
Indoor scenario at KIOS Research Center
• 560m2 area, 3 APs, 1 Query (Q) RSS trajectory
• 4 other (T1-T4) RSS trajectories, top-2 search
• T2 and T3 correctly identified as top-2 answers
"Crowdsourced Trace Similarity with Smartphones", Demetrios Zeinalipour-Yazti and Christos Laoudias and Constandinos Costa and Michail Vlachos and Maria I. Andreou and Dimitrios Gunopulos, IEEE Transactions on
Knowledge and Data Engineering (TKDE '13), IEEE Computer Society, Volume 25, Pages: 1240-1253, Los Alamitos, CA, USA, 2013.
"Disclosure-Free GPS Trace Search in Smartphone Networks", Demetrios Zeinalipour-Yazti, Christos Laoudias, Maria I. Andreou, Dimitrios Gunopulos, "Proceedings of the 2011 IEEE 12th International Conference on Mobile
Data Management - Volume 01" (MDM '11), IEEE Computer Society, Pages: 78--87, Washington, DC, USA, ISBN: 978-0-7695-4436-6, 2011.
"SmartTrace: Finding similar trajectories in smartphone networks without disclosing the traces", Constandinos Costa, Christos Laoudias, Demetrios ZeinalipourYazti, Dimitrios Gunopulos, "Proceedings of the 2011 IEEE 27th
International Conference on Data Engineering" (ICDE '11), IEEE Computer Society, Pages: 1288--1291, Washington, DC, USA, ISBN: 978-1-4244-8959-6, 2011.
Data Management Systems Laboratory
Email: [email protected]
Acknowledgements: This work was supported in part the third author’s Startup Grant, EU’s FP6 Marie Curie TOK “SEARCHiN” project, EU’s FP7 CONET project and EU’s FP7 “SemSorGrid4Env” and “MODAP” projects.

SmartTrace Crowdsourced Trace Similarity with Smartphones