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Data Management Techniques for Smartphone Networks

This talk discusses the use of smartphones as powerful sensing devices in urban environments, exploring techniques for data acquisition, participatory sensing, and urban sensing using smartphone networks. It also covers the differences and similarities between smartphone networks and traditional sensor networks.

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Data Management Techniques for Smartphone Networks

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

  2. Smartphone Networks • Smartphone: A powerful sensing device! • Processing: 1 GHzdual 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

  3. Built-in Sensors on Smartphones Camera: Find the right coupons on the right moment! Microphone: MedicalStethoscope. Compass / Accelerometer: Augmented Reality GPS/WIFI/Cell:Smartphone Social Networks

  4. External Sensors for Smartphones Movement Sensors for Athletes Nike+Apple Body Sensors: ECG, etc. Urban Sensing: CO2, etc.

  5. Infrastructure-based Urban Sensing Mapping Road Traffic is traditionally carried out with fixed cameras & sensors mounted on roadsides http://www.rta.nsw.gov.au/

  6. Infrastructure-less Urban Sensing 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. Smartphone Networks: Applications NoiseMap "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. • Monitoring Urban Spaces • Traffic (VTrack), Road Quality (PotHole), Air Quality (HazeWatch,CommonSense), Noise Pollution (Earphone), ...

  8. 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

  9. 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) platform applications economics

  10. Smartphone Networks vs. Sensor Networks • Similarities with Sensor Networks: • Limited Energy • Multi-hop Networks • In-Network Processing and Aggregation • Query Routing Tree Structures

  11. 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

  12. 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/

  13. SmartTrace: System Model Find the K most similar trajectories to Q without pulling together all traces at QN

  14. SmartTrace: Constraints • 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.

  15. 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 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 "Disclosure-free GPS Trace Search in Smartphone Networks", D. Zeinalipour-Yazti, C. Laoudias, M. I. Andreou, D. Gunopulos, The 12th IEEE International Conference on Mobile Data Management (MDM'11), IEEE Computer Society, Lulea, Sweden, June 6-9, 2011

  16. SmartTrace: Protocol (STP) Querying Node Server (QN) Participating Node LCSS(MBEQ,Ai) 1 2 LCSS(Q,Ai) 3 Text Protocol, RFC-like specification

  17. SmartTrace: 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)

  18. SmartTrace: Prototype System (GPS) Privacy Setting Answer With Trace Answer

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

  20. 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

  21. SmartOpt: Smartphone Social Network A social structure made up of individuals carrying smartphones used for Sharing and Collaboration (Content, Interest, Comments, Places, etc.) Latitude

  22. SmartOpt: Smartphone Social Network • 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)

  23. SmartOpt: Smartphone Social Network Mobile Social Network applications are projected to grow in the future.

  24. SmartOpt: Motivating Example Scenario: Five (5) User moving in Lower Manhattan collecting data (video, photos, sound, rss, …) U5 U2 U3 U4 U1

  25. SmartOpt: The Search Problem Query Processor (QP) Fact: Content is Distributed and there is no Global Index! Problem: How to find the answer more “efficiently”. Find Video of street artists performing right now? U5 U2 U3 U4 U1 {(X,Y,T,obj) | X,Y: spatial, T: temporal, Obj: object}

  26. SmartOpt: Search Solutions Query Processor (QP) U1 • 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

  27. SmartOpt: Solution Outline Interest Matrix (Profile) Social Site Arts Food Cinema … QP U1 X Social Graph (G) U2 X X U3 X U4 X X Query Routing Tree (T) Disseminate Query using T (WiFi| 3G) Bluetooth (cheaper) Bluetooth (cheaper) U5 U2 U3 U4 U1 Objectives: Time - Recall- Energy

  28. SmartOpt: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 IEEE International Conference on Mobile Data Management (MDM'11) (Short Paper), IEEE Computer Society, Lulea, Sweden, June 6-9, 2011.

  29. 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

  30. 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.

  31. SmartPro: Finding Close-by Smartphones Application: Proximity Chat

  32. 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

  33. 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.

  34. SmartNet: Programming Cloud 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.

  35. SmartNet: Programming Cloud Alpha Release: September 2011

  36. Conclusions • Smartphone Networks are gaining momentum as a newcomputing 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!

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

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