April 20, 2008 Emmett NicholasECE 256

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Drive-by Localization of Roadside WiFi Networks

Anand Prabhu Subramanian, Pralhad Deshpande, Jie Gao, Samir R. Das

Accepted in INFOCOM 2008, Phoenix, Arizona, April 2008

April 20, 2008 Emmett NicholasECE 256

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Motivation

• Learn about the nature of WiFi networks– Density, connectivity,

interference properties– LOCATION of the APs– Provide datasets for

research on Internet topology

• Localization of infrastructure nodes (APs)

April 20, 2008 Emmett NicholasECE 256

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Existing Technologies• GPS

– Not available on most wireless clients today• RADAR

– Uses WIFI fingerprints for indoor localization• VORBA

– Rotating directional antennas are used in APs– Signal strength and angle of arrival (AoA) used to localize clients

indoors• War-driving databases

– Locations where APs are heard with a sniffer• MobiSteer

– Steerable beam directional antenna with a WiFi client mounted on a moving car

April 20, 2008 Emmett NicholasECE 256

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Drive-by Localization (DriveByLoc)

• Use MobiSteer– Gather frames from roadside APs on different

directional beams– Estimate the AoA of the frames– Many samples are collected from different

locations• Passive approach

– Based on “sniffing”– APs are unaware of localization effort

April 20, 2008 Emmett NicholasECE 256

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Hardware/Software Setup

• Multi-beam 2.4 GHz antenna– 1 omnidirectional beam– 16 directional beams

• 45⁰ half-power beam-width• Rotated 22.5⁰ with respect to adjacent beam

– Electronically steerable• GPS receiver• Each received frame is logged with the tuple:

<AP, location, orientation, beam, SNR>

April 20, 2008 Emmett NicholasECE 256

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Experimental Scenarios

Parking lot(2 APs)

Apartment complex(17 APs)

Office building(2 APs)

April 20, 2008 Emmett NicholasECE 256

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Data Collection

• Ideally, measurements for each AP are taken on all beams at many points– Beam with highest SNR is pointing towards AP

• Complications…– Each channel/beam combination takes ≈ 100ms– Determining orientation– Non-zero beamwidth– Reflections

April 20, 2008 Emmett NicholasECE 256

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Localization Algorithm

• Estimate AoA of frames from a given AP at each measurement point– Average SNR for frames on each directional beam

• Beam with strongest average SNR is expected to point directly to AP– Orientation information & strongest beam used to

position AP– Sum-square of angular error from all strongest

beam directions is minimized

April 20, 2008 Emmett NicholasECE 256

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Non-zero Beamwidth

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Reflections

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Understanding Reflections

Parking lot Office building

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Understanding Reflections

Parking lot Office building

Interesting observation:CLUSTERING

April 20, 2008 Emmett NicholasECE 256

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Understanding Reflections

Parking lot Office building

New approach…1. Use the k-means algorithm to group the measurement points into k clusters2. Determine which one these k images is the real AP

April 20, 2008 Emmett NicholasECE 256

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Modeling Reflections by k-Means Clustering

• For any given value of k, assume L1,…,Lk are the k locations of the AP (real and the images)

– Li’s are chosen randomly within “feasible region”

– Each measurement mapped to some Li that provides minimum angular error

April 20, 2008 Emmett NicholasECE 256

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Modeling Reflections by k-Means Clustering

1. Compute a point for each cluster, Ci, in the feasible region that minimizes intra-cluster sum-square of angular errors

2. Ci’s become new Li’s

3. Go to Step 1.

April 20, 2008 Emmett NicholasECE 256

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Choosing Real AP Location from k Images

• Impossible to know for sure• But a certain heuristic helps:

– Each measurement ranks k images based on distance to itself– “The nearest image is ranked 1st and the next is ranked 2nd and so on.”– Choose image with least sum of ranks

April 20, 2008 Emmett NicholasECE 256

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Learning k for Clustering

• Use the idea from the G-means algorithm• Start with k=1, and successively increment k

– Perform k-means clustering for each k– Check whether error values in each cluster satisfy

statistical test for normality• If YES, stop.• If NO, increment k and repeat.

April 20, 2008 Emmett NicholasECE 256

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Performance Evaluation

April 20, 2008 Emmett NicholasECE 256

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Benefit of Using Directional Antennas and AOA

“DrivebyLoc is about an order of magnitude better than trilateration”

April 20, 2008 Emmett NicholasECE 256

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Benefit of Modeling Reflection Using Clustering

“Overall it should be recommended that DrivebyLoc be used with modeling beamwidth”

April 20, 2008 Emmett NicholasECE 256

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Impact of GPS Accuracy

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Impact of Car Speed

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Conclusions

• Contributions– Completely passive– Realization that signal reflections can cause

significant localization errors• Development of clustering method to solve this

problem

• Enables accurate WiFi map of urban APs with minimum effort

• What about 3D?

April 20, 2008 Emmett NicholasECE 256

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Thank You