1. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Invited Talk at the 10 th Intl. ACM Workshop on Data Engineering for Wireless and Mobile Access (MobiDE’11), June 12th, 2011, Athens, Greece Data Management Techniques for Smartphone Networks Demetris Zeinalipour Data Management Systems Laboratory Department of Computer Science University of Cyprus http://www.cs.ucy.ac.cy/~dzeina/

2. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Smartphone Networks Smartphone: A powerful sensing device! –Processing: 1 GHz dual core –RAM & Flash Storage: 1GB & 48GB, respectively –Networking: WiFi, 3G (Mbps) / 4G (100Mbps), BlueT. –Sensing: Proximity, Ambient Light, Accelerometer, Microphone, Geographic Coordinates based on AGPS (fine), WiFi or Cellular Towers (coarse). Combining many of those smartphones creates “smartphone networks” that can be utilized for Data Acquisition in Urban Environments. –Opportunistic Sensing (Active): with conscious human involvement –Participatory Sensing (Passive): w/out involvement 2

3. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Built-in Sensors on Smartphones Camera: Find the right coupons on the right moment! Microphone: Medical Stethoscope. GPS/WIFI/Cell: Smartphone Social Networks Compass / Accelerometer: Augmented Reality 3

4. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti External Sensors for Smartphones Nike+Apple Body Sensors: ECG, etc. 4 Movement Sensors for Athletes Urban Sensing: CO 2, etc.

5. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Infrastructure-based Urban Sensing Mapping Road Traffic is traditionally carried out with fixed cameras & sensors mounted on roadsides 5 http://www.rta.nsw.gov.au/

6. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Infrastructure-less Urban Sensing 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 6 Received Signal Strength (RSS): power present in WiFi radio signal

7. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Smartphone Networks: Applications Monitoring Urban Spaces –Traffic (VTrack), Road Quality (PotHole), Air Quality (HazeWatch,CommonSense), Noise Pollution (Earphone),... 7 "Ear-Phone: An End-to-End Participatory Urban Noise Mapping System " Rajib Rana, Chun Tung Chou, Salil Kanhere, Nirupama Bulusu, and Wen Hu. In ACM/IEEE IPSN 10, SPOTS Track, Stockholm, Sweden, April 2010. NoiseMap

8. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Smartphone Networks: Incentives Crowd-Sourcing –“Crowdsourcing is the act of outsourcing tasks to an undefined large group of people through an open call” – Wikipedia –Gigwalk: Perform “Gigs” (e.g., photograph POI, collect prices, populate GIS databases, etc.) and earn money Marketing –e.g., allow companies to run real-time privacy-aware marketing queries on participating smartphone units 8

9. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Smartphone Networks vs. Sensor Networks Differences with Sensor Networks: –Mobility, as Sensor Networks were mostly static. –Multi-modal Communication (Bluetooth, WiFi, 3G, NFC) while WSNs use mostly one (e.g., Zigbee) –More Sensing and Processing Possibilities –Privacy / Anonymity / Higher Security Requirements –Sensing is a Secondary Function –Human Spaces vs. Environment Monitoring –Human-controlled Sensing vs. Human-operated Sensing –No Capital Costs, existing network (3G or WiFi), Smartphones and Infrastruc. (market) can be used. –Economies of scale (>5 Billion phones) 9 platform applications economics

10. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Smartphone Networks vs. Sensor Networks Similarities with Sensor Networks: –Limited Energy –Multi-hop Networks In-Network Processing and Aggregation Query Routing Tree Structures 10

11. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Presentation Outline Introduction, Background and Applications Contributions –SmartTrace: Disclosure-free Trace Search –SmartOpt: Multi-Objective Query Optimization –SmartPro: Finding Neighboring Smartphones –SmartNet: A Testbed for Smartphone Network Application Development. Conclusions 11

12. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartTrace: Motivation Popular Smartphones are already collecting 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/ 12

13. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartTrace: System Model Find the K most similar trajectories to Q without pulling together all traces at QN 13

14. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti 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. SmartTrace: Constraints 14

15. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti 15 SmartTrace: Trajectory Similarity 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

16. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti 16 SmartTrace: Trajectory Similarity * 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

17. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti 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 linear-time trajectory computation on the smartphones to approximate the answer. Step B: Exploit the approximation to iteratively ask specific nodes to conduct a more expensive quadratic function and find the answer 17 "Disclosure-free GPS Trace Search in Smartphone Networks", D. Zeinalipour-Yazti, C. Laoudias, M. I. Andreou, D. Gunopulos, The 12 th IEEE International Conference on Mobile Data Management (MDM'11), IEEE Computer Society, Lulea, Sweden, June 6-9, 2011

18. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartTrace: Protocol (STP) 18 Server (QN) Participating Node Querying Node LCSS(MBE Q,A i ) LCSS(Q,A i ) 1 2 3 Text Protocol, RFC-like specification

19. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartTrace: Prototype System (GPS) 19 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)

20. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartTrace: Prototype System (GPS) 20 Answer With Trace Privacy Setting

21. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartTrace: Prototype System (RSS) 21 A Η Ε Ζ Δ Γ B The SmartTrace algorithm works equally well for indoor environments (using RSS)

22. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Presentation Outline Introduction, Background and Applications Contributions –SmartTrace: Disclosure-free Trace Search –SmartOpt: Multi-Objective Query Optimization –SmartPro: Finding Neighboring Smartphones –SmartNet: A Testbed for Smartphone Network Application Development. Conclusions 22

23. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartOpt: Smartphone Social Network 23 Latitude A social structure made up of individuals carrying smartphones used for Sharing and Collaboration (Content, Interest, Comments, Places, etc.)

24. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartOpt: Smartphone Social Network 24 Main Functionality of these Services Who - What - When – Where Upload Photos / Tag (Comment) on Photos Facebook 50+ Billion Photos in 07/2010 “Check-in” (Places, Gowalla, Foursquare): let user's friends know where they are at the moment. receive location-based deals (e-loyalty card) Location History (Latitude)

25. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartOpt: Smartphone Social Network 25 Mobile Social Network applications are projected to grow in the future.

26. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartOpt: Motivating Example 26 Scenario: Five (5) User moving in Lower Manhattan collecting data (video, photos, sound, rss, …) U1 U2 U4 U5 U3

27. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartOpt: The Search Problem 27 Find Video of street artists performing right now? U1U2U3U4 U5 {(X,Y,T,obj) | X,Y: spatial, T: temporal, Obj: object} Fact: Content is Distributed and there is no Global Index! Problem: How to find the answer more “efficiently”. Query Processor (QP)

28. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartOpt: Search Solutions 28 U1 Query Processor (QP) Centralized Search (CS): –Build a big repository with all objects and tags currently utilized by all social networking sites –  Privacy, Network Traffic & Local Links – Recall

29. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartOpt: Solution Outline 29 U1U2U3U4 U5 Interest Matrix (Profile) ArtsFood Cinema U1 X U2 XX U3 X U4 XX … Query Routing Tree (T) Disseminate Query using T Social Site QP (WiFi| 3G) Bluetooth (cheaper) Bluetooth (cheaper) Social Graph (G) Objectives: Time - Recall- Energy

30. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti 30 SmartOpt: Peer-to-Peer Search in Smartphone Networks "Multi-Objective Query Optimization in Smartphone Networks" A. Konstantinidis, D. Zeinalipour-Yazti, P. Andreou, G. Samaras, 12th IEEE International Conference on Mobile Data Management (MDM'11) (Short Paper), IEEE Computer Society, Lulea, Sweden, June 6-9, 2011. “Finding objects (e.g., images, videos, etc.) in a social neighborhood, without the necessity of having the objects disclosed to the social network provider.”

31. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Presentation Outline Introduction, Background and Applications Contributions –SmartTrace: Disclosure-free Trace Search –SmartOpt: Multi-Objective Query Optimization –SmartPro: Finding Neighboring Smartphones –SmartNet: A Testbed for Smartphone Network Application Development. Conclusions 31

32. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartPro: 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.

33. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartPro: Finding Close-by Smartphones Application: Proximity Chat

34. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Presentation Outline Introduction, Background and Applications Contributions –SmartTrace: Disclosure-free Trace Search –SmartOpt: Multi-Objective Query Optimization –SmartPro: Finding Neighboring Smartphones –SmartNet: A Testbed for Smartphone Network Application Development. Conclusions 34

35. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti 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. Difficult to re-program many devices. SmartNet project (at UCY 2010-2012) is developing 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 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartNet: Programming Cloud 36 SmartNet Install APK, Upload File, Reboot, Screenshots, Monkey Runners, etc.… Programming cloud for the development of smartphone network applications & protocols as well as experimentation with real smartphone devices.

37. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti SmartNet: Programming Cloud 37 Alpha Release: September 2011

38. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Conclusions Smartphone Networks are gaining momentum as a new computing paradigm. Many new Data Management challenges: –Energy-Efficiency –Cloud + Smartphones –Mobile Peer-to-Peer –Mobile Sensor Databases –Flash Storage and Indexing –Caching, Replication, Compression. Lots and Lots of Opportunities for an immediate impact! 38

39. Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 MobiDE 2011, June 12 th, Athens, Greece © Zeinalipour-Yazti Invited Talk at the 10 th Intl. ACM Workshop on Data Engineering for Wireless and Mobile Access (MobiDE’11), June 12th, 2011, Athens, Greece Data Management Techniques for Smartphone Networks Demetris Zeinalipour University of Cyprus Thanks! Questions? http://www.cs.ucy.ac.cy/~dzeina/talks/smart.mobide11.12.06.11.ppt