April 20, 2008Emmett Nicholas ECE 256 1 Drive-by Localization of Roadside WiFi Networks Anand Prabhu...
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Transcript of April 20, 2008Emmett Nicholas ECE 256 1 Drive-by Localization of Roadside WiFi Networks Anand Prabhu...
![Page 1: April 20, 2008Emmett Nicholas ECE 256 1 Drive-by Localization of Roadside WiFi Networks Anand Prabhu Subramanian, Pralhad Deshpande, Jie Gao, Samir R.](https://reader035.fdocuments.in/reader035/viewer/2022062407/56649da15503460f94a8c861/html5/thumbnails/1.jpg)
April 20, 2008 Emmett NicholasECE 256
1
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
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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)
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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
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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
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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>
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April 20, 2008 Emmett NicholasECE 256
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Experimental Scenarios
Parking lot(2 APs)
Apartment complex(17 APs)
Office building(2 APs)
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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
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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
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April 20, 2008 Emmett NicholasECE 256
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Non-zero Beamwidth
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April 20, 2008 Emmett NicholasECE 256
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Reflections
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April 20, 2008 Emmett NicholasECE 256
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Understanding Reflections
Parking lot Office building
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April 20, 2008 Emmett NicholasECE 256
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Understanding Reflections
Parking lot Office building
Interesting observation:CLUSTERING
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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
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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
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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.
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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
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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.
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April 20, 2008 Emmett NicholasECE 256
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Performance Evaluation
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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”
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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”
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April 20, 2008 Emmett NicholasECE 256
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Impact of GPS Accuracy
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April 20, 2008 Emmett NicholasECE 256
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Impact of Car Speed
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April 20, 2008 Emmett NicholasECE 256
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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?
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April 20, 2008 Emmett NicholasECE 256
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Thank You