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Pervasive Location-Aware Computing
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  1. Pervasive Location-Aware Computing Hari Balakrishnan Networks and Mobile Systems Group MIT Laboratory for Computer Science http://nms.lcs.mit.edu/

  2. Why you should care • Location-awareness will be a key feature of many future mobile applications • Many scenarios in pervasive computing • Navigation • Resource discovery • Embedded applications, sensor systems • Monitoring and control applications • The design of good location-aware computing systems cuts across many areas of CS/EE • E.g., sensors, signal processing, networking, mobility, data management, graphics/visualization, planning, HCI, … • Most of the exciting stuff will happen in the next few years!

  3. Computing Input Output Processing

  4. Processing + communication Processing + communication Processing + communication Processing + communication Network Networked Computing

  5. Sensors Processing + communication Processing + communication Processing + communication Location information Processing + communication Resource information Network Actuators Networked, Context-Aware Computing Environmental Context

  6. Location-Aware Applications • Human-centric • “Finding” applications • Embedded • Sensors & actuators • Devices • Monitoring and control • System should support both forms

  7. This Talk • Cricket location infrastructure • Some applications • System architecture • Challenges for the future

  8. Cricket • Architecture for ubiquitous location-sensing • No single location-sensing technology works everywhere today, particularly indoors • Integrates variety of sensory information • GPS: wide-open outdoors • Wireless access info: coarse-grained info • RF + ultrasonic trilateration: indoors and in urban areas • Sensor-independent location API

  9. Desired Functionality • What space am I in? • Room 510, reception area, seminar room,… • How do I learn more about what’s in this space? • An application-dependent notion • What are my (x,y,z) coordinates? • “Cricket GPS” • Which way am I pointing? • “Cricket compass” • Goal: Linear precision of a few centimeters, angular precision of a few degrees

  10. Design Goals • Must determine: • Spaces: Good boundary detection is important • Position: With respect to arbitrary inertial frame • Orientation: Relative to fixed-point in frame • Must operate well indoors • Preserve user privacy: don’t track users • Must be easy to deploy and administer • Must facilitate innovation in applications • Low energy consumption

  11. info = “a2” info = “a1” Cricket Architecture Beacon Estimate distances to infer location Listener Autonomous: No central beacon control or tracking Passive listeners + active beacons facilitates privacy Straightforward deployment and programmability

  12. Machinery  B Beacons on ceiling SPACE=NE43-510 ID=34 COORD=146 272 0 MOREINFO= http://cricket.lcs.mit.edu/ Cricket listener Mobile device Mobile device Obtain linear distance estimates Pick nearest to infer “space” Solve for mobile’s (x, y, z) Determine  w.r.t. each beacon and deduce orientation vector

  13. RF data (spacename) Determining Distance Beacon • A beacon transmits an RF and an ultrasonic signal simultaneously • RF carries location data, ultrasound is a narrow pulse Ultrasound (pulse) Listener • The listener measures the time gap between the receipt of RF and ultrasonic signals • A time gap of x ms roughly corresponds to a distance of x feet from beacon • Velocity of ultrasound << velocity of RF

  14. Multiple Beacons Cause Complications Beacon A Beacon B • Beacon transmissions are uncoordinated • Ultrasonic signals reflect heavily • Ultrasonic signals are pulses (no data) These make the correlation problem hard and can lead to incorrect distance estimates Incorrect distance Listener t RF B RF A US B US A

  15. Solution • Carrier-sense + randomized transmission • Reduce chances of concurrent beaconing • Bounding stray signal interference • Envelop all ultrasonic signals with RF • Listener inference algorithm • Processing distance samples to estimate location

  16. RF A US A t Bounding Stray Signal Interference • Engineer RF range to be larger than ultrasonic range • Ensures that if listener can hear ultrasound, corresponding RF will also be heard

  17. S/b t r/v (max) S r b v Bounding Stray Signal Interference • No “naked” ultrasonic signal can be valid! S = size of space advertisement b = RF bit rate r = ultrasound range v = velocity of ultrasound (RF transmission time) (Max. RF-US separation at the listener)

  18. A B Actual distance (feet) 6 8 Mode (feet) 6 8 Mean (feet) 7.2 6.4 Majority 9 10 Estimation AlgorithmWindowed MinMode A Frequency B 5 Distance (feet) 5 10

  19. Orientation B Beacons on ceiling Orientation relative to B on horizontal plane  Cricket listener with compass hardware Mobile device (parallel to horizontal plane)

  20. d1 d2 z  Cricket Compass Trigonometry 101 Beacon Idea: Use multiple ultrasonic sensors and estimate differential distances sin  = (d2 - d1) / sqrt (1 - z2/d2) where d = (d1+d2)/2 Two terms need to be estimated: 1. d2 – d1 2. z/d (by estimating coordinates) Heading

  21. Beacon d1 d2 L t f = 2p (d2 – d1)/l Differential Distance Estimation • Problem: for reasonable values of parameters (d, z), (d2 - d1) must have 5mm accuracy • Well beyond all current technologies! Estimate phase difference between ultrasonic waveforms!

  22. vt1 vt2 vt3 vt4 Coordinate Estimation B Beacons on ceiling at known coordinates  (x,y,z) Four equations, four unknowns Velocity of sound varies with temperature, humidity Can be “eliminated” (or calculated!)

  23. Beacon Placement Totally arbitrary beacon placement won’t demarcate spaces correctly Room A Room B I am at B

  24. Correct Beacon Placement Room A Room B x x I am at A • Position beacons to detect the boundary • Multiple beacons per space are possible

  25. System Configuration & Administration • Password-based authentication for configuration • Currently, coordinates manually entered • Auto-configuration algorithm being developed • MOREINFO database centrally managed with Web front-end • Relational DBMS • Challenge: queries that don’t divulge device location, but yet are powerful

  26. Cricket v1 Prototype RF module (rcv) RF module (xmit) Ultrasonic sensor Ultrasonic sensor RF antenna Listener Beacon Atmel processor RS232 i/f Host software libraries in Java; Linux daemon (in C) for Oxygen BackPaq handhelds Several apps…

  27. Deployment

  28. Some Results • Linear distances to within 6cm precision • Spatial resolution of about 30cm • Coordinate estimation to within 6cm in each dimension • Orientation to within 3-5 degrees when angle to some beacon < 45 degrees • Several applications (built, or being built) • Stream redirection, active maps, Viewfinder, Wayfinder, people-locater • Scalable location-aware monitoring (SLAM) apps: MIT library book tracking, asset management, MIT physical plant maintenance

  29. Where am I?(Active map)

  30. What’s near me? Find this for me(Resource discovery) “Print map on a color printer,” and system sends data to nearest available free color printer and tells you how to get there Location by “intent”

  31. How do I get to Jorg’s office?

  32. Large-Scale Monitoring Scale (# sensors) Power, thermal Monitoring & control 107 Asset tracking Fire detection Assisted evacuation 106 Library usage Motion detection Leaks, floods Lab equipment monitoring Cricket network auto-configuration 105 Physical plant Repair orders HazMat response Local navigation 104 Personal safety Traffic, parking Irrigation Days/Hours Minutes Seconds Response time

  33. Requirements • Ubiquitous location-sensing • Heterogeneous sensor networking/comm. protocols • Resource discovery • Event handling • Query processing • Spatial databases • Mapping and representation • Navigation • User interfaces

  34. Strawman Architecture Cricket beacons (Pervasive) Events Tag reader Actions Tagged books, equipment Event-handling & resource discovery network Sensor Proxy Application event handlers (Distributed) Sensors & actuators Data stores Fixed sensor proxy (sensor integration, pruning) Mobile sensor proxy

  35. Summary • Location-aware computing poses numerous interesting challenges for CS • An important component of pervasive computing • Integrating real-world information • App spectrum from HCI  Embedded apps • Cricket provides location information for mobile, pervasive computing applications • Space, position, orientation • Flexible and programmable infrastructure • Deployment and management facilities

  36. Collaborators • Bodhi Priyantha • Allen Miu • Ken Steele • Rafael Nogueras • Seth Teller • Steve Garland • Dorothy Curtis • Omar Aftab • Erik Demaine • Mike Stonebraker http://nms.lcs.mit.edu/