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Querying Smartphone Networks with SmartTrace

Querying Smartphone Networks with SmartTrace. Demetris Zeinalipour Department of Computer Science University of Cyprus. Colloquium: Department of Computer Science, University of Pittsburgh, Sennott Square - Seminar Room 5317 , 14:00-15:00, Friday, April 29 th , 2011.

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Querying Smartphone Networks with SmartTrace

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  1. Querying Smartphone Networks with SmartTrace Demetris Zeinalipour Department of Computer Science University of Cyprus Colloquium: Department of Computer Science, University of Pittsburgh, Sennott Square - Seminar Room 5317, 14:00-15:00,Friday, April 29th, 2011. http://www.cs.ucy.ac.cy/~dzeina/

  2. Acknowledgments • "Disclosure-free GPS Trace Search in Smartphone Networks", D. Zeinalipour-Yazti, C. Laoudias, M. I. Andreou, D. Gunopulos, 12th Intl. Conf. on Mobile Data Management (MDM'11), IEEE Computer Society, Lulea, Sweden, June 6-9, 2011. • “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. Other Related Work: • “Distributed Spatio-Temporal Similarity Search’’, D. Zeinalipour-Yazti, et. al, In 15th ACM Conference on Information and Knowledge Management (CIKM’06), Arlington, VA, USA, 2006. • "Finding the K Highest-Ranked Answers in a Distributed Network", D. Zeinalipour-Yazti, , Z. Vagena, D. Gunopulos and V. Kalogeraki, V. Tsotras, M. Vlachos, N. Koudas, D. Srivastava, Computer Networks (ComNet), vol. 53, issue 9, pp. 1431-1449, Elsevier Press, 2009.

  3. Smartphones • Smartphone: a mobile device (phone, tablet, slate) that offers more computing ability than a basic feature phone (e.g., one running JavaME) and a “dumb” phone. • Computing Ability: CPU, Memory & Storage, Networking, Sensing. • Example (Motorola Atrix 4G) • Processing: 1 GHzdual 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).

  4. Applications of Smartphones Sensors Camera: Find the right coupons on the right moment! Microphone: MedicalStethoscope. Compass / Accelerometer: Augmented Reality GPS/WIFI/Cell:Smartphone Social Networks

  5. Road Traffic Mapping (RTM): Past Mapping Road Traffic is traditionally carried out with fixed cameras & sensors mounted on roadsides http://www.rta.nsw.gov.au/

  6. RTM with Smartphone Networks: Future Opportunistic (w/ user interaction) and Participatory Sensing (w/out user interaction): Mapping the Road traffic by collecting WiFi signals. Received Signal Strength (RSS): power present in WiFi radio signal Ε Ζ Γ Η Δ A 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

  7. Collecting Trace Data on Smartphones • Popular Smartphones are already collecting positional information (i.e., user-agnostic sensing) • Example A (iPhone logs User Positional Data): • iPhone collects Longitude / Latitude (or triangulated Cell Tower position) info locally on your smartphone (and iTunes backup). • The unencrypted log file is even migrated between devices! • Displaying your location history on a Map: http://petewarden.github.com/iPhoneTracker/ • Example B (Android logs/uploads Access Point data): • There are rumors that Google uses its Android OS for collecting (wardriving) positional info about WiFi Access Points (APs). • When the phone detects a WiFi AP, it sends the BSSID (MAC address) of the router along with signal strength and GPS coordinates over to the Geolocation database at Google • This enables a variety of interesting queries (e.g., find the location of your WiFi AP): http://samy.pl/androidmap/

  8. Collecting Trace Data on Smartphones Mapping your iPhone locations with the popular software (points are constrained to a grid, so the exact location is not revealed in the visualization) Circle Size/Color indicates the frequency of visits to a particularly spatial location The availability of such data on a device enables applications like SmartTrace, presented next.

  9. Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works

  10. System Model and Problem Formulation Find the K most similar trajectories to Q without pulling together all traces at QN

  11. Constrains and Objectives • 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) • 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.

  12. Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works

  13. Distance D = 7.3 D = 10.2 D = 11.8 D = 17 D = 22 Trajectory Similarity Search • 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 ? Query

  14. System Model and Problem Definition • 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) P=1 Manhattan P=2 Euclidean 14

  15. Longest Common Subsequence • Longest Common Subsequence (LCSS): Given strings A and B, LCSS is the longest string that is a subsequence of both A and B; • extensively utilized in text similarity, e.g., • String: CGATAATTGAGA • Substring (contiguous): CGA • SubSequence (not necess. conti.): AAGAA • Find the LCSS of the following 1D-trajectory • A = 3, 2, 5, 7, 4, 8, 10, 7 • B = 2, 5, 4, 7, 3, 10, 8, 6 • LCSS = (2, 5, 4, 7) or (2, 5, 7, 10) or …

  16. ignore majority of noise match match Longest Common Subsequence • A Dynamic Programming algorithm for this problem requires O(|A|*|B|)time. • However we can compute it in O(δ(|A|+|B|)), if we limit the matching within a time window of δ. Time δ B Procesing a trajectory with size |Ai|=1.8MB, requires 111 seconds on a smartphone A

  17. LCSS(MBEQ, Ai): Bounding Above LCSS* ΜΒΕ: Minimum Bounding Envelope Q A ε 2δ 6pts 40 pts * Indexing multi-dimensional time-series with support for multiple distance measures,M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, E. Keogh,In KDD 2003.

  18. Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works

  19. SmartTrace Algorithm Outline • An intelligenttop-K processing algorithm for identifying the K most similar trajectories to Q in a distributed environment. • Step A: Conduct the cheap linear-time LCSS(MBEQ,Ai) 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, Ai).

  20. 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: • Upper Bound (UB) Computation: Instruct each of the m smartphones to invoke a computation of the linear-time LCSS(MBEQ,Ai) (i ≤ m). • 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.

  21. Top-2 Top-2 22 22 23 23 SmartTrace Algorithm (2/2) • Full Computation: Ask the λ + 1 (λ ≥ K) highest UB nodes to compute LCSS(Q,Ai) and then send back their λ full scores. • Termination Condition: If the next highest UB is smaller than the K-th largest full match then stop; else goto step 3 in order to identify the next λ cand. • (Tentative) Ship Matching: If the termination condition has been met, tentatively ship the respective matches to QN, based on some local trace disclosure policy. STOP CONTINUE A A UB Scores UB Scores FULL Scores FULL Scores

  22. SmartTrace Protocol Querying Node Server (QN) Participating Node LCSS(MBEQ,Ai) 1 2 LCSS(Q,Ai) 3

  23. Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works

  24. Experimental Methodology http://iapg.jade-hs.de/personen/brinkhoff/generator/ http://research.microsoft.com/en-us/projects/geolife/ • 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

  25. Experimental Methodology • Algorithms: • Centralized (C): 1) Ship Trajectories to QN; 2) Conduct centralized LCSS(Q,Ai) computation; • Decentralized (D): 1) Ship Q to all nodes; 2) Conduct the LCSS(Q,Ai) computation locally; • SmartTrace (ST): 1) Ship Q to all nodes; 2) Conduct the linear-time LCSS(MBEQ,Ai) computation; 3) Iteratively ask specific nodes to calculate LCSS(Q,Ai); • 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) • δand ε(temporal and spatial matching) parameters are kept constant for all experiments. The values affect the matching granularity, which is similar for all algorithms.

  26. 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)

  27. 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,Ai) on all nodes.

  28. 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).

  29. 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, whereλ 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.

  30. Prototype System (GPS) Query Device B Device 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)

  31. Prototype System (GPS) Privacy Setting Answer With Trace Answer

  32. Prototype System (RSS) The SmartTrace algorithm works equally well for indoor environments (using RSS) Ε Ζ Γ Η Δ A B

  33. Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works

  34. 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.

  35. Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works

  36. 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.

  37. SmartNet: Programming Cloud SmartNet Install APK, Upload File, Reboot, … Programming cloud for the development of smartphone network applications & protocols as well as experimentation with real smartphone devices.

  38. SmartNet: Programming Cloud

  39. SmartP2P:Peer-to-Peer Search in Smartphone Networks “Finding objects (e.g., images, videos, etc.) in a social neighborhood, without the necessity of having the objects disclosed to the social network provider.” "Multi-Objective Query Optimization in Smartphone Networks" A. Konstantinidis, D. Zeinalipour-Yazti, P. Andreou, G. Samaras, 12th International Conference on Mobile Data Management (MDM'11) (Short Paper), IEEE Computer Society, Lulea, Sweden, June 6-9, 2011.

  40. PROXIMITY: Finding Close-by Smartphones • Problem:Identifying geographically close-by devices continuously for all smartphones. • Constraints: • Privacy: Users do not want to expose their precise location (we utilize location obfuscation techniques) • Complexity: Computing the above answers for millions of devices requires takes time while the answer need to be ready every few seconds.

  41. PROXIMITY: Εύρεση Γειτονικών Συσκευών Application: Proximity Chat

  42. Querying Smartphone Networks with SmartTrace Demetris Zeinalipour Department of Computer Science University of Cyprus Thanks! Questions? Colloquium: Department of Computer Science, University of Pittsburgh, Sennott Square - Seminar Room 5317, 14:00-15:00, Friday, April 29th, 2011. http://www.cs.ucy.ac.cy/~dzeina/

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