Towards Mobile Phone Localization without War-Driving
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Towards Mobile Phone Localization without War-Driving
Ionut Constandache, Romit Roy Choudhury, Injong Rhee
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Location is an IP addressLocation is an IP addressfor content delivery
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Location-Based Applications Location-Based Applications (LBAs)(LBAs)
Examples:
Location-based recommendations, geo-tagging
GeoLife: shopping list when near a grocery store
TrafficSense: real-time traffic conditions
Location expresses context of user◦Facilitates content delivery
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Is GPS the solution?
Pros: Good accuracy Cons: Poor battery lifetime
Is GSM the solution?
Pros: Long battery lifetime Cons: Poor accuracy
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What about WiFi Localization?
E.g., SkyHook:
Basic Idea:1. Several trucks war-drive a place2. Create Radio map = <Location: WiFi IDs>3. Distribute map to phones4. Phone user goes to war-driven region, overhears
WiFi IDs5. Reverse Look Up IDs against radio map6. Obtains location
Basic Idea:1. Several trucks war-drive a place2. Create Radio map = <Location: WiFi IDs>3. Distribute map to phones4. Phone user goes to war-driven region, overhears
WiFi IDs5. Reverse Look Up IDs against radio map6. Obtains location
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Is Skyhook the solution?
Middle GroundLower Accuracy than GPS, Longer
Battery lifetime Better Accuracy than GSM, Shorter
Battery lifetimebut …
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Is Skyhook the solution?
Middle GroundLower Accuracy than GPS, Longer Battery
lifetime Better Accuracy than GSM, Shorter Battery
lifetimebut …
At the cost of: Degraded location accuracy: walking paths ~
60m Reliance on infrastructure (APs) War-driving ($$ + carbon footprint) “NYTimes: Skyhook fleet 500 trucks/drivers”
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ContentsContentsCompAcc
Evaluation
Limitations and Future Work
Conclusion
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ContentsContentsCompAcc
Evaluation
Limitations and Future Work
Conclusion
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GoalsGoals No War-Driving
Cannot drive walking paths (campus, parks, …)
Expensive / Environment unfriendly
No reliance on WiFi infrastructure Rural regions / developing countries
Good accuracy (~GPS)
Improve energy-efficiency Better than Skyhook, GPS
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CompAcc: Basic IdeaCompAcc: Basic Idea Direction(compass) +
Displacement(accelerometer) = User’s directional trailDirectional Trail
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CompAcc: Basic IdeaCompAcc: Basic Idea Direction(compass) +
Displacement(accelerometer) = User’s directional trail
Compute path signatures ◦ Derived from a local electronic map (Google Maps)
Path Signature …
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CompAcc: Basic IdeaCompAcc: Basic Idea Direction(compass) + Displacement(accelerometer) =
User’s directional trail
Compute path signatures ◦ Derived from a local electronic map (Google Maps)
Compare directional trail with path signatures◦ Best match provides the user location
Directional Trail
Path Signature …
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Path Signature … Directional Trail
Correct location errors at turns
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Path Signature … Directional Trail
Correct location errors at turns
Directional Trail
Path Signature …
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ArchitectureArchitectureTile
DatabaseTile
Database
6. Current location(lat A, long B)
2. Report initial location(lat X, long Y)
Tile
4. Direction(Compass)
5. Displacement(Accelerometer)
1. Initial location GPS:(lat X, long Y)
CompAcc
Initial location Directional trail Current location
3. Obtain paths in the user vicinity
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Sample Tile:Sample Tile:
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Directional trail: Directional trail: displacementdisplacementAccelerometer based step countdisplacement = step_count *
step_size
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Directional trail: Directional trail: displacementdisplacementAccelerometer based step countdisplacement = step_count *
step_size
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Directional trail: directionDirectional trail: direction
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Directional trail: directionDirectional trail: direction
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Path SignaturePath SignatureExtract from Google MapsGeodesic formulas
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Matching Directional Trail Matching Directional Trail with Path Signatureswith Path Signatures
Dissimilarity Metric:
ci = compass readingspi = path computed directionN = directional trail size
Directional Trail
Path Signature
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Fallback Mechanism: A-Fallback Mechanism: A-GPSGPSWhat if the dissimilarity metric is
large?◦Trigger A-GPS
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Fallback MechanismFallback Mechanism
Estimated Location
7th Street
5th Street
Main
St.
AA
EE
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Fallback MechanismFallback Mechanism
Estimated Location
7th Street
5th Street
Main
St.
AA
EE
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ContentsContentsCompAcc
Evaluation
Limitations and Future Work
Conclusion
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ResultsResultsCompared 3 localization schemes
◦CompAcc◦Skyhook◦Wifi-War-Walk (We war-droved walking
paths in campus)Metrics Instantaneous Error = distance(estimated, real)
Average Localization Error (ALE) = Average Instantaneous Error
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CompAcc Instantaneous CompAcc Instantaneous ErrorError
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ResultsResults Average ALE
GPS: 10mCompAcc: 11m
WiFi-War-Walk: 30mSkyhook: 70m
Energy GPS: 10h
CompAcc: 23hWiFi-War-Walk:16h
Skyhook:16h
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Directional trail: Directional trail: displacementdisplacementStep count/displacement
accuracy
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Trail/Path SizeTrail/Path Size
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ContentsContentsCompAcc
Evaluation
Limitations and Future Work
Conclusion
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Limitations and Future Limitations and Future WorkWork
Map Generation Manually mark footpaths
User Position Estimated along the Path Apply particle filters to accommodate wide
roads
Multiplexing between Localization Methods Hand-off to Skyhook/GPS when driving Extend to vehicular movement
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ContentsContentsCompAcc
Evaluation
Limitations and Future Work
Conclusion
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CompAccCompAccToday’s localization technologies
limited Energy- Efficiency Coverage/Accuracy
Rely on simple localization mechanism Need: Compass, Accelerometer and Maps
Evaluation results: ALE: 11m Battery: 23h
CompAcc scales to any mapped part of the world
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AdvantagesAdvantages No war-driving No reliance on WiFi infrastructure
Maps available ubiquitously
Improves battery lifetime GPS ~10h Skyhook ~16h Accelerometer ~ 39h Compass ~48h