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

Learning the meaning of Learning the meaning of placesplaces

IfGiIfGi

Location based ServicesLocation based Services

SS 06SS 06

Milad SabersamandariMilad Sabersamandari

InhaltInhalt

IntroductionIntroduction Existing place learning algorithmsExisting place learning algorithms Extracting Places from traces of Extracting Places from traces of

locationslocations Application with BluetoothApplication with Bluetooth Advantages and disadvantagesAdvantages and disadvantages ReferencesReferences

IntroductionIntroduction

Location learning systemsLocation learning systems Locations are expressed in 2 Locations are expressed in 2

principal waysprincipal ways• CoordinatesCoordinates• LandmarksLandmarks

Intrested in „places“ (e.g. home, Intrested in „places“ (e.g. home, work, cinema)work, cinema)


Define „places“Define „places“ Manually by handManually by hand

• Rectangular region around an office Rectangular region around an office represented in coordinatesrepresented in coordinates

AutomaticallyAutomatically• Spends a significant amout of time Spends a significant amout of time

or/and visits frequentlyor/and visits frequently -> Place learning algorithms-> Place learning algorithms


Locations based servicesLocations based services• Location based reminderLocation based reminder• Location based to-do list applicationLocation based to-do list application• „„Location based intelligent desicions Location based intelligent desicions

service“service“

Existing place learning algorithmsExisting place learning algorithms

Ashbrook and Starner´s GPS Dropout Ashbrook and Starner´s GPS Dropout Hierachical Clustering Algorithm Hierachical Clustering Algorithm (A&S)(A&S)

The comMotion Recurring GPS The comMotion Recurring GPS Dropout AlgorithmDropout Algorithm

The BeaconPrint AlgorithmThe BeaconPrint Algorithm

Ashbrook and Starner´s Clustering Ashbrook and Starner´s Clustering Algorithm (A&S)Algorithm (A&S)

Loss of GPS signal of at least Loss of GPS signal of at least tt minutesminutes

Indicates a speed of continuilly below Indicates a speed of continuilly below 1 mile per hour1 mile per hour

Positions are merged (variant k-Positions are merged (variant k-means clustering algorithm)means clustering algorithm)

The comMotion Recurring GPS The comMotion Recurring GPS Dropout AlgorithmDropout Algorithm

GPS is lost three or more times GPS is lost three or more times within a given radiuswithin a given radius

Merge the points to placesMerge the points to places


Fingerprint algorithmFingerprint algorithm Input: sensor log from mobile deviceInput: sensor log from mobile device List of places the device went List of places the device went

(waypointlist)(waypointlist) GSM and 802.11GSM and 802.11


1. Segment a sensor log into times when 1. Segment a sensor log into times when the device was in a stable place and the device was in a stable place and assign a waypoint.assign a waypoint.

2. Merge waypoints which are captured 2. Merge waypoints which are captured from repeat visits to the same place.from repeat visits to the same place.

Likewise, an effective recognition Likewise, an effective recognition algorithm has two capabilities:algorithm has two capabilities:• 1. Recognize when the device returns to a 1. Recognize when the device returns to a

known place using a waypoint list.known place using a waypoint list.• 2. Recognize when the device is not in a place 2. Recognize when the device is not in a place

We refer to this state as mobile.We refer to this state as mobile.

Extracting Places from traces of Extracting Places from traces of locationslocations

Uses Place Lab to collect traces of Uses Place Lab to collect traces of locationslocations

In many cities and towns available In many cities and towns available Place Lab works in urban areas Place Lab works in urban areas

aswell as indoorsaswell as indoors Location recorded once per secondLocation recorded once per second Places appear as clusters of locationsPlaces appear as clusters of locations

Extracting Places from traces of Extracting Places from traces of locationslocations

Place LabPlace Lab• Uses that each WiFi access point Uses that each WiFi access point

broadcasts its unique MAC addressbroadcasts its unique MAC address• A database maps these addresses to A database maps these addresses to

longitude and latidute coordinates longitude and latidute coordinates

Existing clustering AlgorithmExisting clustering Algorithm

k-means Algorithmk-means Algorithm Gaussian mixture model (GMM)Gaussian mixture model (GMM) Require the number of clusters as a Require the number of clusters as a

parameterparameter Require a significant amout of Require a significant amout of

computationcomputation

Time based clusteringTime based clustering

Eliminate the intermediate locations Eliminate the intermediate locations between important placesbetween important places

Determine the number of clusters Determine the number of clusters (important places) autonomously(important places) autonomously

Simple enough to run on a simple low Simple enough to run on a simple low battery mobile devicebattery mobile device


Basic idea is to cluster along the time Basic idea is to cluster along the time axisaxis

New measured location is compared New measured location is compared with previous locationswith previous locations

Decide if the mobile device is movingDecide if the mobile device is moving Parameter:distance Parameter:distance d d between the between the

locations and a cluster´s time locations and a cluster´s time duration duration tt


Parameter: distance Parameter: distance dd, time , time tt Current cluster Current cluster clcl Pending location Pending location plocploc Significant places Significant places PlacesPlaces



Unlike other clustering algorithms Unlike other clustering algorithms this algorithm computes the clusters this algorithm computes the clusters incrementallyincrementally

The computation is simpleThe computation is simple Easily supported on small battery Easily supported on small battery

mobile devicesmobile devices

Application with BluetoothApplication with Bluetooth

Bluetoothcell with radius Bluetoothcell with radius rr Bool value for each cellBool value for each cell Short distanceShort distance Time duration of 11 secondsTime duration of 11 seconds




ReplaceReplace• Measured location Measured location loc loc

measured BTcell measured BTcell cellcell• Pending locationPending location ploc ploc

pending BTcellpending BTcell pcell pcell Current clusterCurrent cluster cl cl

as a set of BTcellsas a set of BTcells

Advantages and disadvantagesAdvantages and disadvantages

GPS (Advantages)GPS (Advantages)• Standardized Standardized • 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 Loss of signal in urban canyons and

other „shadowed“ areasother „shadowed“ areas

Advantages and disadvantagesAdvantages and disadvantages

Bluetooth (Advantages)Bluetooth (Advantages)• Standardized Standardized • 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

ReferencesReferences1.1. Jong Hee Kang, William Webourne, Benjamin Stewart, Gaetano Jong Hee Kang, William Webourne, Benjamin Stewart, Gaetano

Borrielo. Borrielo. Extracting Places from Traces of LocationsExtracting Places from Traces of Locations2.2. Jeffrey Hightower, Sunny Consolvo, Anthony LaMarca, Ian Smith, Jeffrey Hightower, Sunny Consolvo, Anthony LaMarca, Ian Smith,

Jeff Hughes†. Jeff Hughes†. Learning and Recognizing the Places We GoLearning and Recognizing the Places We Go3.3. John Krumm, Ken Hinckley. John Krumm, Ken Hinckley. The NearMe Wireless Proximity The NearMe Wireless Proximity

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