Dejavu: An accurate Energy-Efficient Outdoor Localization System sigspatial '13

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Dejavu:An accurate Energy-Efficient Outdoor Localization System



  • Outdoor localization system

  • Provides accurate and energy-efficient outdoor localization

  • Uses only energy-efficient sensors or sensors which are already running

  • leverages road landmarks like moving over potholes, bumps, tunnels etc


  • GPS considered as de facto standard for outdoor localization

  • Dejavu's approach is based on low-energy sensors (accelerometer,gyroscope,compass)

  • Using array of sensors it identifies landmarks (anchors)

  • crowdsourcing to build database

System overview

Raw sensor information

  • System collects raw sensor information

  • cellular network information (RSS and associated cell tower ID)

  • opprtunistically leverags the Wifi chip to collect surrounding Wifi APs

Error Resetting

  • uses linear accelaration combined with direction of motion to compute displacement

  • uses Vincenty's formula

  • to limit accumulated error system uses physical and virtual anchors

Physical and virtual anchors

  • Dejavu uses two types of anchors

  • physical anchors mapped to road features like bridges, tunnels, speed-bumps

  • extracted from map or through pror knowledge

  • virtual anchors detected automatically

  • includes points with unique GSM or Wifi RSS

  • learned through crowd-sourcing

Anchor detection

  • large number of road features can be identified on their unique signature

Physical anchors

Physical anchors

  • Curves and turns -

    • road curvature forces car to change its direction which results in big variance of phone's orientation
  • Tunnels -

    • drop in cellular signal
    • large variance in the ambient magnetic field in x-direction
  • Bridges -

    • cars go up at the start of the bridge and go down at the end
    • reflected in x-gravity or y-gravity acceleration
  • Road anomalies-

    • cat's eye does not cause high variance in y or z-axis gravity accleration
    • speed bumps usually have highest variance
    • railway crossing leads to medium variance

Virtual anchors

  • uses un-supervised learning techniques to identify virtual anchors

  • anomaly detection techniques are used to identify anomalies in sensor readings.

  • they are clustered into sensor space to identify candidate clusters

  • points of each cluster are spatially clustered to identify the location of each anchor

Feature selection

Anomaly detection

  • h - bandwidth

  • n - sample size

  • K - kernel function

Two stage clustering

  • Cluster feature space using hierarchical clustering in vector feature space

  • Clustering will group similar anomalies

  • spatial clustering of points in clusters obtained previously

  • cluster is accepted if the number of points is above a threshold

Computing anchor location


  • Anchors aliasing

    • classes of anchors can be confused with other anchors
    • leverages map context information
  • Efficient matching

    • limits search space to small area around the user location
  • Processing location

    • can be split into a client-server architecture
  • Other sensors

    • other sensors of the phone such as camera, mic could be used
  • Handling heterogeneity


Virtual anchor detection accuracy

Anchor localization accuracy

Effect of anchor density on accuracy

Dejavu against other systems

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