1. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 12th IEEE International Conference on Mobile Data Management (MDM’11), June 7th, 2011, Luleå, Sweden Disclosure-free GPS Trace Search in Smartphone Networks Demetrios Zeinalipour-Yazti Christos Laoudias Maria I. Andreou Dimitrios Gunopulos http://www.cs.ucy.ac.cy/~dzeina/ MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos

2. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Smartphones Smartphone: A powerful sensing device! –Processing: 1 GHz dual core –RAM & Flash Storage: 1GB & 48GB, respectively –Networking: WiFi, 3G (Mbps) / 4G (100Mbps–1Gbps) –Sensing: Proximity, Ambient Light, Accelerometer, Microphone, Geographic Coordinates based on AGPS (fine), WiFi or Cellular Towers (coarse). Combining many of those “sensors” creates opportunities for Crowd- sourced data acquisition in Urban Environments. –Aka. Opportunistic/Participatory Sensing 2

3. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos A Word on GPS Trace Collection Popular Smartphones are already collect positional information. Same applies to Social Networking Applications (e.g., Latitude, Gowalla, Twitter, etc.) iPhone User Position Logging: –iPhone collects coarse-grain positional information (i.e., triangulated Cell tower) locally on your smartphone (and iTunes backup). –The unencrypted log file is even migrated between devices! –Displaying your iPhone trace history on a Map: http://petewarden.github.com/iPhoneTracker/ http://petewarden.github.com/iPhoneTracker/ 3

4. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work 4

5. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos System Model and Problem Formulation Find the K most similar trajectories to Q without pulling together all traces at QN 5

6. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos A.Don’t Disclose the User’s Trajectory to QN –Social sites are already undergoing significant privacy restructuring (e.g., google buzz, facebook) –Trajectories are large (270MB/year with 2s samples) B.Minimize Net Traffic and Local Processing –3G/4G and WiFi traffic: i) depletes smartphone battery and ii) degrades network health* * In 2009 AT&T’s customers affected by iPhone release. Constraints and Objectives 6

7. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos SmartTrace Applications Our framework finds applications in a wide range of domains: –Intelligent Transportation Systems: “Find whether a new bus route is similar to the trajectories of K other users.” –Social Networks: “Find whether there is a cycling route from MOMA to the Julliard” GeoLife, GPS-Waypoints, Sharemyroutes, etc. offer centralized counterparts. –Habitant Monitoring: Find zebras that moved more similarly to zebra X before it got injured. 7

8. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work 8

9. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Trajectory Similarity Search Query D = 7.3 D = 10.2 D = 11.8 D = 17 D = 22 Distance ? Problem: Compare the query with all the distributed sequences and return the k most similar sequences to the query. Similarity between two objects A, B is associated with a distance function (see next) K 9

10. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Lp-norms are the simplest way to compare trajectories (e.g., Euclidean, Manhattan, etc.) Lp-norms are fast (i.e., O(n)), but inaccurate. –No Flexible matching in time. (miss out-of-phase) –No Flexible matching in space. (miss outliers) Trajectory Similarity Search 10 P=1 Manhattan P=2 Euclidean

11. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos 11 Trajectory Similarity Search LCSS: Given strings A and B, LCSS is the longest string that is a subsequence of both A and B; A Dynamic Programming algorithm for this problem requires O(|A|*|B|) time. * It can be computed in O(δ(|A|+|B|)) if we limit the matching window within δ. => Still expensive δ A B * Procesing a trajectory with size |Ai|=1.8MB, requires 111 seconds on a smartphone ignore majority of noise match Time

12. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 12 LCSS(MBE Q, A i ): Bounding Above LCSS* * Indexing multi-dimensional time-series with support for multiple distance measures, M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, E. Keogh, In KDD 2003. QA ε 2δ 40 pts 6 pts ΜΒΕ: Minimum Bounding Envelope

13. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work 13

14. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos SmartTrace Algorithm Outline An intelligent top-K processing algorithm for identifying the K most similar trajectories to Q in a distributed environment. Step A: Conduct the cheap linear-time LCSS(MBE Q,A i ) computation on the smartphones to approximate the answer. Step B: Exploit the approximation to identify the correct answer by iteratively asking specific nodes to conduct LCSS(Q, A i ). 14

15. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos SmartTrace Algorithm (1/2) Input: Query Trajectory Q, m Target Trajectories, Result Preference K (K << m), Iteration Step Increment λ. Output: K trajectories most similar to Q. At the query node QN: 1.Upper Bound (UB) Computation: Instruct each of the m smartphones to invoke a computation of the linear-time LCSS(MBE Q,A i ) (i ≤ m). 2.Collection of UB: Receive the UBs of all m trajectories participating in the query and add those scores to the METADATA vector stored at QN. Let METADATA be sorted in descending order based on the UB scores. 15

16. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos SmartTrace Algorithm (2/2) 3.Full Computation: Ask the λ (λ ≥ K) highest UB nodes to compute LCSS(Q,Ai) and then send back the computed “full” scores. 4.Termination Condition: If the next highest UB is smaller than the K-th largest full score then stop; else goto step 3 in order to identify the next λ candidates. 5.(Tentative) Ship Matching: If the termination condition has been met, tentatively ship the respective matches to QN, based on some local trace disclosure policy. 16 23 22 Top-2 FULL ScoresUB Scores A 23 22 Top-2 FULL Scores UB Scores A CONTINUE STOP

17. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos SmartTrace Protocol (STP) 17 Server (QN) Participating Node Querying Node LCSS(MBE Q,A i ) LCSS(Q,A i ) 1 2 3 Text Protocol, RFC-like specification

18. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work 18

19. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Experimental Methodology Datasets & Queries –Oldenburg (Realistic): IAPG Institute, Germany Dataset –2,000 Car Trajectories moving in the city of Oldenburg. –Trajectory Length: 11,731 ±7,193 points Queryset –Randomly sampled out of the original dataset with interpolated noise –Trajectory Length: 100 points. –GeoLife (Real): Microsoft Research Asia Dataset –1,100 Human Trajectories over the city of Beijing in the time frame 2007-2009 (1 sample / 5 seconds or 1 sample / 10 meters) –Trajectory Length: 190,110 ±126,590 points Queryset –Randomly sampled out of the original dataset with interpolated noise –Trajectory Length: 500 points http://research.microsoft.com/en-us/projects/geolife/ http://iapg.jade-hs.de/personen/brinkhoff/generator/ 19

20. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Experimental Methodology Algorithms: –Centralized (C): 1) Ship Trajectories to QN; 2) Conduct centralized LCSS(Q,A i ) computation; –Decentralized (D): 1) Ship Q to all nodes; 2) Conduct the LCSS(Q,A i ) computation locally; –SmartTrace (ST): 1) Ship Q to all nodes; 2) Conduct the linear- time LCSS(MBE Q,A i ) computation; 3) Iteratively ask specific nodes to calculate LCSS(Q,A i ); Metrics: –Execution Time (T): The total time to answer the query. –Amortized Energy (E) per Device: average energy consumed by a smartphone for answering the query (based on Powertutor profile – Univ. of Michigan) –δ and ε (temporal and spatial matching) parameters are kept constant for all experiments. The values affect the matching granularity, which is similar for all algorithms. 20

21. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Experimental Results (Execution Time) Result I: ST and D are 1 order of magnitude faster than C. Expl: ST and D rely mainly on processing while C relies on data transfer, which is slow! Result II: ST is faster than D (i.e., 17% and 8%, respectively for the two datasets) 10x Expl: Attributed to the variable length of trajectories (i.e., D always compares against the longest trajectory while ST compares against it only if it belongs to the candidate S-set) 21

22. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Experimental Results (Energy Consumption) Result III: C is network-intensive while ST and D are cpu- intensive Expl: ST and D have very little network activity (i.e., which accounts for 2.59mJ and 2.29mJ, respectively) Result IV: - ST is 67% more energy efficient than D -ST is 81% more energy efficient than C -Expl: ST doesn’t execute LCSS(Q,A i ) on all nodes. 22

23. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Prototype System (GPS) 23 QueryDevice BDevice C * “ SmartTrace: Finding Similar Trajectories in Smartphone Networks without Disclosing the Traces”, C. Costa, C. Laoudias, D. Zeinalipour-Yazti, D. Gunopulos Demo at the 27th IEEE Intl. Conf. on Data Engineering (ICDE’11), Hannover, Germany, 2011. SmartTrace: Implemented as a Client-Server text-based protocol –Server implemented in JAVA (4,500 LOC) –Client implemented in JAVA on Android (2,500 LOC + XML files)

24. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Prototype System (GPS) 24 Answer With Trace Privacy Setting

25. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Prototype System (RSS) 25 A Η Ε Ζ Δ Γ B The SmartTrace algorithm works equally well for indoor environments (using RSS)

26. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work 26

27. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Future Work Evaluate the SmartTrace prototype system over the SmartNet testbed we are developing. Develop extensions that do not require the iterative execution of LCSS(Q,Ai) but can postpone them to a final post-processing step. Develop new Similarity Measures for (Highly Dimensional) RSS Trajectories. Develop a killer application for our algorithm and deploy the executable APK on Google Market to gain further experiences with this. Possibly also develop a client for iPhone devices. 27

28. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Future Work 28 SmartNet Install APK, Upload File, Reboot, … Programming cloud for the development of smartphone network applications & protocols as well as experimentation with real smartphone devices.

29. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Disclosure-free GPS Trace Search in Smartphone Networks Demetrios Zeinalipour-Yazti Christos Laoudias Maria I. Andreou and Dimitrios Gunopulos http://www.cs.ucy.ac.cy/~dzeina/ MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Thanks! Questions? www.modap.org

30. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos A Word on GPS Trace Collection 30

31. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos 31 LCSS Definition

32. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Experimental Results (Varying K Parameter) Result V: Performance results are the same when the preference K is constraint within 1% of the answer set (typical for top-K query processing algorithms). 32

33. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Experimental Results (Varying the λ Parameter) The λ parameter defines how aggressively ST explores the top-k result set (Higher λ => Faster Convergence) Theorem: ST requires O(m/λ) iterations in the worse case, where λ denotes the step increment and m the number of trajectories Result VI (λ-convergence): Our algorithm convergences in 7.6 and 9.3 iterations, on average, for the Oldenburg and Geolife datasets, respectively. 33

34. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 34 SmartTrace Execution Query: Find the K=2 most similar trajectories to Q λ ≥?≥? λ+1 Q A4 LCSS(Q,A4)=23 Ask A4 & A2 for the computation of LCSS Stop if Kth LCSS >= Last UB  Kth LCSS 23 22 K=2

35. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos SmartNet: Programming Cloud Currently, there are no testbeds (like motelab, planetlab) for realistically emulating and prototyping Smartphone Network applications and protocols at a large scale. Currently applications are tested in emulators. –Drawbacks: Sensors are not emulated. It is difficult to concurrently re-program several devices between the devices. MobNet project (at UCY 2010-2012), will develop an innovative cloud testbed of mobile sensor devices using 50+ Android devices. 35

36. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos Road Traffic Mapping (RTM): Past Mapping Road Traffic is traditionally carried out with fixed cameras & sensors mounted on roadsides 36 http://www.rta.nsw.gov.au/

37. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MDM 2011 © Zeinalipour-Yazti, Laoudias, Andreou, Gunopulos RTM with Smartphone Networks: Future Opportunistic (w/ user interaction) and Participatory Sensing (w/out user interaction): Mapping the Road traffic by collecting WiFi signals. A Η Ε Ζ Δ Γ B Graphics courtesy of: A.Thiagarajan et. al. “Vtrack: Accurate, Energy-Aware Road Traffic Delay Estimation using Mobile Phones, In Sensys’09, pages 85-98. ACM, (Best Paper) MIT’s CarTel Group 37 Received Signal Strength (RSS): power present in WiFi radio signal