Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Colloquium: Department of Computer Science, University of Pittsburgh, Sennott Square - Seminar Room 5317, 14:00-15:00, Friday, April 29 th, Querying Smartphone Networks with SmartTrace Demetris Zeinalipour Department of Computer Science University of Cyprus

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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, “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, Other Related Work: “Distributed Spatio-Temporal Similarity Search’’, D. Zeinalipour- Yazti, et. al, In 15 th ACM Conference on Information and Knowledge Management (CIKM’06), Arlington, VA, USA, "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 , Elsevier Press,

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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 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). 3

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Applications of Smartphones Sensors Camera: Find the right coupons on the right moment! Microphone: Medical Stethoscope. GPS/WIFI/Cell: Smartphone Social Networks Compass / Accelerometer: Augmented Reality 4

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Road Traffic Mapping (RTM): Past Mapping Road Traffic is traditionally carried out with fixed cameras & sensors mounted on roadsides 5

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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 ACM, (Best Paper) MIT’s CarTel Group 6 Received Signal Strength (RSS): power present in WiFi radio signal

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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: 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): 7

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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) 8 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.

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works 9

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, System Model and Problem Formulation Find the K most similar trajectories to Q without pulling together all traces at QN 10

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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. Constrains and Objectives 11

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works 12

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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 13

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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) System Model and Problem Definition 14 P=1 Manhattan P=2 Euclidean

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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 …

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 16 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. δ A B Procesing a trajectory with size |Ai|=1.8MB, requires 111 seconds on a smartphone ignore majority of noise match Time

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 17 LCSS Definition

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/ 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 QA ε 2δ 40 pts 6 pts ΜΒΕ: Minimum Bounding Envelope

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works 19

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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 ). 20

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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. 21

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, SmartTrace Algorithm (2/2) 3.Full Computation: Ask the λ + 1 (λ ≥ K) highest UB nodes to compute LCSS(Q,Ai) and then send back their λ full scores. 4.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. 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 Top-2 FULL ScoresUB Scores A Top-2 FULL Scores UB Scores A CONTINUE STOP

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/ 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 K=2

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, SmartTrace Protocol 24 Server (QN) Participating Node Querying Node LCSS(MBE Q,A i ) LCSS(Q,A i ) 1 2 3

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works 25

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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 (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

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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. 27

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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) 28

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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. 29

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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). 30

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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. 31

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Prototype System (GPS) 32 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, 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)

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Prototype System (GPS) 33 Answer With Trace Privacy Setting

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Prototype System (RSS) 34 A Η Ε Ζ Δ Γ B The SmartTrace algorithm works equally well for indoor environments (using RSS)

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works 35

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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. 36

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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. 37

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, Presentation Outline Introduction System Model and Problem Formulation Background on Trajectory Similarity The SmartTrace Algorithm Experimental Evaluation Future Work Other Related Research Works 38

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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 ), will develop an innovative cloud testbed of mobile sensor devices using 50+ Android devices. 39

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, SmartNet: Programming Cloud 40 SmartNet Install APK, Upload File, Reboot, … Programming cloud for the development of smartphone network applications & protocols as well as experimentation with real smartphone devices.

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, SmartNet: Programming Cloud 41

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 42 SmartP2P: Peer-to-Peer Search in Smartphone Networks "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, “Finding objects (e.g., images, videos, etc.) in a social neighborhood, without the necessity of having the objects disclosed to the social network provider.”

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, 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.

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Demetris Zeinalipour, PROXIMITY: Εύρεση Γειτονικών Συσκευών Application: Proximity Chat

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Colloquium: Department of Computer Science, University of Pittsburgh, Sennott Square - Seminar Room 5317, 14:00-15:00, Friday, April 29 th, Querying Smartphone Networks with SmartTrace Demetris Zeinalipour Department of Computer Science University of Cyprus Thanks! Questions?