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Learning the meaning of places

Learning the meaning of places. IfGi Location based Services SS 06. Milad Sabersamandari. Inhalt. Introduction Existing place learning algorithms Extracting Places from traces of locations Application with Bluetooth Advantages and disadvantages References. Introduction.

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Learning the meaning of places

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  1. Learning the meaning of places IfGi Location based Services SS 06 Milad Sabersamandari

  2. Inhalt • Introduction • Existing place learning algorithms • Extracting Places from traces of locations • Application with Bluetooth • Advantages and disadvantages • References

  3. Introduction • Location learning systems • Locations are expressed in 2 principal ways • Coordinates • Landmarks • Intrested in „places“ (e.g. home, work, cinema)

  4. Introduction • Define „places“ • Manually by hand • Rectangular region around an office represented in coordinates • Automatically • Spends a significant amout of time or/and visits frequently • -> Place learning algorithms

  5. Introduction • Locations based services • Location based reminder • Location based to-do list application • „Location based intelligent desicions service“

  6. Existing place learning algorithms • Ashbrook and Starner´s GPS Dropout Hierachical Clustering Algorithm (A&S) • The comMotion Recurring GPS Dropout Algorithm • The BeaconPrint Algorithm

  7. Ashbrook and Starner´s Clustering Algorithm (A&S) • Loss of GPS signal of at least t minutes • Indicates a speed of continuilly below 1 mile per hour • Positions are merged (variant k-means clustering algorithm)

  8. The comMotion Recurring GPS Dropout Algorithm • GPS is lost three or more times within a given radius • Merge the points to places

  9. The BeaconPrint Algorithm • Fingerprint algorithm • Input: sensor log from mobile device • List of places the device went (waypointlist) • GSM and 802.11

  10. The BeaconPrint Algorithm • 1. Segment a sensor log into times when the device was in a stable place and assign a waypoint. • 2. Merge waypoints which are captured from repeat visits to the same place. • Likewise, an effective recognition algorithm has two capabilities: • 1. Recognize when the device returns to a known place using a waypoint list. • 2. Recognize when the device is not in a place We refer to this state as mobile.

  11. Extracting Places from traces of locations • Uses Place Lab to collect traces of locations • In many cities and towns available • Place Lab works in urban areas aswell as indoors • Location recorded once per second • Places appear as clusters of locations

  12. Extracting Places from traces of locations • Place Lab • Uses that each WiFi access point broadcasts its unique MAC address • A database maps these addresses to longitude and latidute coordinates

  13. Existing clustering Algorithm • k-means Algorithm • Gaussian mixture model (GMM) • Require the number of clusters as a parameter • Require a significant amout of computation

  14. Time based clustering • Eliminate the intermediate locations between important places • Determine the number of clusters (important places) autonomously • Simple enough to run on a simple low battery mobile device

  15. Time based clustering • Basic idea is to cluster along the time axis • New measured location is compared with previous locations • Decide if the mobile device is moving • Parameter:distance d between the locations and a cluster´s time duration t

  16. Time based clustering • Parameter: distance d, time t • Current cluster cl • Pending location ploc • Significant places Places

  17. Time based clustering

  18. Time based clustering • Unlike other clustering algorithms this algorithm computes the clusters incrementally • The computation is simple • Easily supported on small battery mobile devices

  19. Application with Bluetooth • Bluetoothcell with radius r • Bool value for each cell • Short distance • Time duration of 11 seconds

  20. Application with Bluetooth

  21. Application with Bluetooth

  22. Application with Bluetooth • Replace • Measured location loc measured BTcell cell • Pending location ploc pending BTcell pcell • Current cluster cl as a set of BTcells

  23. Advantages and disadvantages • GPS (Advantages) • Standardized • Covers most of the earth´s surface • Continually decreasing in cost • GPS (Disadvantages) • Inability to function indoors • Occasional lack of geometry accuracy • Loss of signal in urban canyons and other „shadowed“ areas

  24. Advantages and disadvantages • Bluetooth (Advantages) • Standardized • 3 classes (different ranges) • Everywhere available (indoor) • Bluetooth (Disadvantages) • Short distance • Long time duration • Accuracy = 1 Bluetoothcell • Bad java support

  25. References • Jong Hee Kang, William Webourne, Benjamin Stewart, Gaetano Borrielo. Extracting Places from Traces of Locations • Jeffrey Hightower, Sunny Consolvo, Anthony LaMarca, Ian Smith, Jeff Hughes†. Learning and Recognizing the Places We Go • John Krumm, Ken Hinckley. The NearMe Wireless Proximity Server

  26. Vielen Dank für Ihre Aufmerksamkeit

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