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

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

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

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

Introduction Locations based services Locations based services Location based reminderLocation based reminder Location based to-do list applicationLocation based to-do list application „Location based intelligent desicions service“„Location based intelligent desicions service“

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

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

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

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

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

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

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

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

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

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

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

Time based clustering

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

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

Application with Bluetooth

Replace Replace Measured location locMeasured location loc  measured BTcell cell Pending location plocPending location ploc  pending BTcell pcell Current cluster cl Current cluster cl as a set of BTcells as a set of BTcells

Advantages and disadvantages GPS (Advantages) GPS (Advantages) StandardizedStandardized Covers most of the earth´s surfaceCovers most of the earth´s surface Continually decreasing in costContinually decreasing in cost GPS (Disadvantages) GPS (Disadvantages) Inability to function indoorsInability to function indoors Occasional lack of geometry accuracyOccasional lack of geometry accuracy Loss of signal in urban canyons and other „shadowed“ areasLoss of signal in urban canyons and other „shadowed“ areas

Advantages and disadvantages Bluetooth (Advantages) Bluetooth (Advantages) StandardizedStandardized 3 classes (different ranges)3 classes (different ranges) Everywhere available (indoor)Everywhere available (indoor) Bluetooth (Disadvantages) Bluetooth (Disadvantages) Short distanceShort distance Long time durationLong time duration Accuracy = 1 BluetoothcellAccuracy = 1 Bluetoothcell Bad java supportBad java support

References 1. Jong Hee Kang, William Webourne, Benjamin Stewart, Gaetano Borrielo. Extracting Places from Traces of Locations 2. Jeffrey Hightower, Sunny Consolvo, Anthony LaMarca, Ian Smith, Jeff Hughes†. Learning and Recognizing the Places We Go 3. John Krumm, Ken Hinckley. The NearMe Wireless Proximity Server

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