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Querying Sensor Data in Smartphone Networks

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Querying Sensor Data in Smartphone Networks

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  1. Querying Sensor Data in Smartphone Networks Demetris Zeinalipour Department of Computer Science University of Cyprus Colloquium: Department of Computer Science, University of Cyprus, Room 148, Building 12 (FST-01), 09:15-10:15,Thursday, Oct. 11, 2012.

  2. 2011 - current : The Post-PC Era Oct. 8, 2011. The Economist. "Beyond the PC" 02/2012: Canalys Device Ship. 2011 Annual growth Smartphones 487.7 62.7% Total PCs 414.6 14.8% - Notebooks 209.6 7.5% - Desktops 112.4 2.3% - Tablets 63.2 274.2% - Netbooks 29.4 -25.3% 06/2012: IDC 1.6 B mobiles phones shipped in 2011. (Gartner: PCs in use will reach 2B in 2014! 1.7 B units in 2012 . (61% Android, 20.5% iOS, 5.2% Win) 2.2 B units in 2016. (53% Android, 19.2% iOS, 19% Win)

  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. • Examples: Motorola Atrix 4G / LG OPTIMUS G • CPU: 1 GhzDual core / 1.5Ghz Quad-core (Qualcomm Snapdragon S4) • RAM & Flash:1GB & 48GB / 2GB & 32GB • Networking: WiFi, 3G (Mbps) / 4G (100Mbps–1Gbps) • Sensing: Proximity, Ambient Light, Accelerometer, Microphone, Geographic Coordinates based on AGPS (fine), WiFi or Cellular Towers (coarse), Camera (13MB!)

  4. Smartphones: Networking Wireless Data Transfer Rates • 4G ITU peak rates: • 100 Mbps (high mobility, such as trains and cars) • 1Gbps (low mobility, such as pedestrians and stationary users) Plot Courtesy of H. Kim, N. Agrawal, and C. Ungureanu, "Revisiting Storage for Smartphones", The 10th USENIX Conference on File and Storage Technologies (FAST'12), San Jose, CA, February 2012. *** Best Paper Award ***

  5. Smartphone: Sensors (Internal) Camera: Find the right coupons on the right moment! Microphone: MedicalStethoscope. Compass / Accelerometer: Augmented Reality GPS/WIFI/Cell:Smartphone Social Networks

  6. Smartphone Sensors (External) Movement Sensors for Athletes Nike+Apple Body Sensors: ECG, etc. Urban Sensing: CO2, etc.

  7. Smartphones: Sensors (External) Amateur "Space" Exploration Program with an iPhone! (www.brooklynspaceprogram.org) 31KM | -70 C Capsule: Courtesy of: windows2universe.org

  8. 1 Smartphone = ~1M Applications Apple App Store: 700,000 apps Google Play Store: 675,000 apps Graphic Courtesy of: Cnet.com / September 27, 2012

  9. N Smartphones = ? Applications Smartphone Network: Many Smartphones sensing and communicating without explicit user interactions in order to realize a collaborative task.”

  10. Smartphone Networks: The Past Mapping Road Traffic with fixed cameras & sensors mounted on roadsides? http://www.rta.nsw.gov.au/

  11. Smartphone Networks Mapping the Road traffic by collecting WiFi signals. Received Signal Strength (RSS): power present in WiFi radio signal 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

  12. Smartphone Networks • Monitoring Urban Spaces • Traffic (VTrack@MIT), Road Quality (PotHole @MIT), Air Quality (HazeWatch,CommonSense @ UNSW), Noise Pollution (Earphone), ... 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.

  13. Smartphone Networks Client/Cloud Architectures Cloud "Big-data" (NoSQL/NewSQL) Privacy

  14. Smartphone Networks Peer-to-Peer Architectures (e.g., SmartP2P)

  15. Smartphone Networks Hybrid Architectures SmartTrace, Proximity, BloomMap, etc. Query Processor Energy! Privacy!

  16. Research Focus Data Management in Systems and Networks (Sensor, Smartphone, P2P, Crowds, …) Word cloud on titles of venues I have published at. / wordle.net Distributed Query Processing, Storage and Retrieval Methods for Sensor, Smartphone and Peer-to-Peer Systems, Mobile and Network Data Management, Energy-aware Data Management.

  17. Research Focus Data Management in Systems and Networks PC Co-Chair MDM'10, DMSN'10 (VLDB'10) | Contest Chair ICDM'10 | General Co-Chair MobiDE'10 (SIGMOD) PC Co-Chair MobiDE'09 (ACM SIGMOD) General Co-Chair DMSN'11 (VLDB'11) 1st PhD Graduate (P. Andreou), EWSN'12 Dissertation Award. Demo Co-Chair: MDM'13 - MDM'12 Best Demo! - Industrial NRE with Taiwan Mult. Comp. 2009 2010 2011 2012 2013 Sensor Data Management (MINT, MicroPulse, KSpot, ETC, MHS, SenseSwarm, FlashSort, etc.) Smartphone Data Management (SmartTrace, Proximity, SmartLab, SmartP2P, Airplace, CrowdCast, BloomMap)

  18. SmartTrace Publications Main Related Articles: • [J14]"Crowdsourced Trace Similarity with Smartphones", Demetrios Zeinalipour-Yazti and Christos Laoudias and Costantinos Costa and Michalis Vlachos and Maria I. Andreou and Dimitrios Gunopulos, IEEE Transactions on Knowledge and Data Engineering (TKDE '12), IEEE Computer Society, Volume 99, Los Alamitos CA USA, 2012. • [C31] "Disclosure-Free GPS Trace Search in Smartphone Networks", Christos Laoudias, Maria I. Andreou, Dimitrios Gunopulos, "Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 01" (MDM '11), IEEE Computer Society, Pages: 78--87, Washington DC USA, ISBN: 978-0-7695-4436-6, 2011. • [C30] "SmartTrace: Finding similar trajectories in smartphone networks without disclosing the traces", Constandinos Costa, Christos Laoudias, Demetrios ZeinalipourYazti, Dimitrios Gunopulos, "Proceedings of the 2011 IEEE 27th International Conference on Data Engineering" (ICDE '11), IEEE Computer Society, Pages: 1288--1291, Washington DC USA, ISBN: 978-1-4244-8959-6, 2011. Other Related Work: • "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.

  19. Talk Outline • Introduction • SmartTrace: Trajectory Similarity Framework for Smartphones • System Model and Problem Formulation • Background on Trajectory Similarity • SmartTrace (ST) Algorithm • SmartTrace Prototype Overview • Conclusions & SmartLab • Other Research and Future Directions

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

  21. Constraints 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. Minimize Networking + Processing! * * * * [J15] "Crowdsourcing with Smartphones", Georgios Chatzimiloudis, Andreas Konstantinides, Christos Laoudias, Demetrios Zeinalipour-Yazti, IEEE Internet Computing (IC '12), Special Issue: Sep/Oct 2012 - Crowdsourcing, May 2012. IEEE Press, Volume 16, Pages: 36-44, 2012.

  22. Distance D = 7.3 D = 10.2 D = 11.8 D = 17 D = 22 Trajectory Similarity Search • Problem: Compare the query with all 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

  23. Background on Trajectory Similarity • 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

  24. 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*min(|A|,|B|)) if we limit the matching within a time window of δ. Time δ B A

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

  26. Trajectory Similarity Function

  27. Talk Outline • Introduction • SmartTrace: Trajectory Similarity Framework for Smartphones • System Model and Problem Formulation • Background on Trajectory Similarity • SmartTrace (ST) Algorithm • SmartTrace Prototype Overview • Conclusions & SmartLab • Other Research and Future Directions

  28. SmartTrace Algorithm Outline • An intelligenttop-K processing algorithm for identifying the K most similar trajectories to Q in a distributed environment. • Step A: Conduct an inexpensive 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).

  29. 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. Trajectory 1 Trajectory 2 … Trajectory m

  30. 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 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. 22 23 STOP CONTINUE A A UB Scores UB Scores FULL Scores FULL Scores

  31. Talk Outline • Introduction • SmartTrace: Trajectory Similarity Framework for Smartphones • System Model and Problem Formulation • Background on Trajectory Similarity • SmartTrace (ST) Algorithm • SmartTrace Prototype Overview • Conclusions & SmartLab • Other Research and Future Directions

  32. Prototype System (GPS) • SmartTrace for Android! http://smarttrace.cs.ucy.ac.cy/ Query Q Device B Device C

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

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

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

  36. Talk Outline • Introduction • SmartTrace: Trajectory Similarity Framework for Smartphones • System Model and Problem Formulation • Background on Trajectory Similarity • SmartTrace (ST) Algorithm • SmartTrace Prototype Overview • Conclusions & SmartLab • Other Research and Future Directions

  37. SmartTrace Conclusions • We have evaluated SmartTrace (ST) using a variety of traces (Geolife@Microsoft, Oldenburg, etc.) and found that: • We consume less energy and time than competitive approaches. • We return the same results with their Centralized and Decentralized counterparts • We have also developed a Non-Iterative version of SmartTrace (coined NIST), which uses LB and UBs on LCSS to derive the results: • Derives answers in 2 rounds. • Is faster than ST, but consumes more energy (coarser bounds allow less pruning)

  38. SmartLab Programming Cloud • Currently, there are no testbeds (like motelab, planetlab) for realistically prototyping Smartphone Network applications and protocols at a large scale. • Currently applications are tested in emulators. • Sensors are not emulated.  • Reprogramming is difficult.  • SmartLab (http://smartlab.cs.ucy.ac.cy/) is a first-of-a-kind programmable cloud of 40+ smartphones deployed at our department enabling a new line of systems-oriented research on smartphones. [J15]"Crowdsourcing with Smartphones", Georgios Chatzimiloudis, Andreas Konstantinides, Christos Laoudias, Demetrios Zeinalipour-Yazti IEEE Internet Computing (IC '12), Special Issue: Sep/Oct 2012 - Crowdsourcing, May 2012. IEEE Press, 2012 [C38]"Demo: A Programming Cloud of Smartphones", A. Konstantinidis, C. Costa, G. Larkou and D. Zeinalipour-Yazti, "Demo at the 10th International Conference on Mobile Systems, Applications and Services" (Mobisys '12), Low Wood Bay, Lake District, UK, 2012.

  39. SmartLab Programming Cloud Install APK, Upload File, Reboot, … URL: http://smartlab.cs.ucy.ac.cy/

  40. SmartLab Programming Cloud http://smartlab.cs.ucy.ac.cy/

  41. Talk Outline • Introduction • SmartTrace: Trajectory Similarity Framework for Smartphones • System Model and Problem Formulation • Background on Trajectory Similarity • SmartTrace (ST) Algorithm • SmartTrace Prototype Overview • Conclusions & SmartLab • Other Research and Future Directions

  42. Research Focus Data Management in Systems and Networks 2009 2010 2011 2012 2013 Sensor Data Management (MINT, MicroPulse, KSpot, ETC, SenseSwarm MHS, FlashSort, etc.) Smartphone Data Management (SmartTrace, Proximity, SmartLab, SmartP2P, Airplace, CrowdCast, BloomMap)

  43. MicroPulse/ETC: Query Routing Trees • Query Routing Trees (QRTs) are structures for percolating query answers to a query processor in wireless sensor network. • "Optimized Query Routing Trees for Wireless Sensor Networks“ P. Andreou, D. Zeinalipour-Yazti, A. Pamboris, P.K. Chrysanthis, G. Samaras, Information Systems (InfoSys), Elsevier Press, Volume 36, Issue 2, pp. 267-291, April 2011. • MicroPulse: Tuning the Waking Windows of QRTs • ETC: Balancing the QRT with Global Knowledge

  44. MINT: In-Network Views Kspot Architecture ``KSpot: Effectively Monitoring the K Most Important Events in a Wireless Sensor Network", P. Andreou, D. Zeinalipour-Yazti, M. Vassiliadou, P.K. Chrysanthis, G. Samaras, 25th International Conference on Data Engineering March (ICDE'09), Shanghai, China, May 29 - April 4, 2009. "Power Efficiency through Tuple Ranking in Wireless Sensor Network Monitoring“, P. Andreou, D. Zeinalipour-Yazti, P. Chrysanthis, G. Samaras,, Distributed and Parallel Databases (DAPD), Special Issue on Query Processing in Sensor Networks, Springer Press, Volume 29, Numbers 1-2, pp. 113-150, DOI: 10.1007/s10619-010-7072-5, January 2011.

  45. KSpot+ Architecture Open Source: http://kspot.cs.ucy.ac.cy/ "KSpot+: A Network-aware Framework for Energy-efficient Data Acquisition in Wireless Sensor Networks." Panayiotis Andreou, PhD Dissertation Award at European Wireless Sensor Networks conference 2012. (co-advised with G. Samaras)

  46. SenseSwarm: Mobile Sensor Networks Chemical Dispersion Sampling Identify the existence of toxic plumes. SensorFlock (U of Colorado Boulder) MilliBots (CMU) Micro Air Vehicles (UAV – Unmanned Aerial Vehicles) Ground Station Graphic courtesy of: J. Allred et al. "SensorFlock: An Airborne Wireless Sensor Network of Micro-Air Vehicles", In ACM SenSys 2007.

  47. SenseSwarm: Mobile Sensor Networks SenseSwarm: A mobile sensing framework where data acquisition is scheduled on perimeter sensors and storage at core nodes. s6 s4 s5 • Perimeter Algorithm (PA) Distributed • DRA: Data Replication Algorithm s7 s3 s2 s8 s1 [J12]"In-network data acquisition and replication in mobile sensor networks", Panayiotis Andreou, Demetrios Zeinalipour-Yazti, Panos K. Chrysanthis and George Samaras, Distrib. Parallel Databases (DAPD '11), Springer Press, Volume 29, Pages: 87--112, Hingham, MA, USA, 2011.

  48. Research Focus Data Management in Systems and Networks 2009 2010 2011 2012 2013 Sensor Data Management (MINT, MicroPulse, KSpot, ETC, SenseSwarm MHS, FlashSort, etc.) Smartphone Data Management (SmartTrace, Proximity, SmartLab, SmartP2P, Airplace, CrowdCast, BloomMap)

  49. 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.” [J16] "Intelligent search in social communities of smartphone users", Konstantinidis Andreas, Demetrios Zeinalipour-Yazti, Panayiotis Andreou, George Samaras, Panos K. Chrysanthis, Distributed and Parallel Databases (DAPD '12), Springer Press, Pages: 1-35, 2012. Website: http://smartp2p.cs.ucy.ac.cy/

  50. Airplace Indoor Positioning • A-GPS localization - Drawbacks: • negatively affected from the environment (e.g., cloudy days, forests) • does not work in indoor spaces • suffers from high-energy drain • RSS Localization with Airplace • Collaboration with KIOS lead to a prototype system for a well-known Taiwanese entertainment company! • BloomMap obfuscates user location with a bloom vector [C42]"The Airplace Indoor Positioning Platform for Android Smartphones", C. Laoudias, G. Constantinou, M. Constantinides, S. Nicolaou, D. Zeinalipour-Yazti, C. G. Panayiotou, "Demo at the 13th IEEE International Conference on Mobile Data Management (Best Demo Award!)" (MDM '12), IEEE Computer Society, Bangalore, India, 2012. [C40]"Towards In-Situ Localization on Smartphones with a Partial Radiomap", Andreas Konstantinidis, Georgios Chatzimilioudis, Christos Laoudias, Silouanos Nicolaou and Demetrios Zeinalipour-Yazti, "The 4th ACM International Workshop on Hot Topics in Planet-Scale Measurement, HotPlanet’12, in conjunction with the 10th ACM International Conference on Mobile Systems, Applications and Services" (MobiSys’12), Low Wood Bay, Lake District, UK,2012.