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Nericell: Rich Road and Traffic Monitoring using Mobile Smartphones
Prashanth Mohan Venkat Padmanabhan Ram Ramjee Microsoft Research India, Bangalore Presented by Ali Khodaei
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Traffic Monitoring Developed Countries Developing Countries
GPS based tracking is adequate Infrastructure support exists Developing Countries Bangalore ≠ <your favorite US city>
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What’s Different? Potholes Road bumps Varied vehicle types
Liberal honking Chaotic intersections …
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Why Rich Monitoring? Find least stressful route
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Widespread distribution of mobile phones
Road and Traffic Monitoring Without deployed infrastructure Using existing mass of mobile phones
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Mobile Phones ~3 billion phones worldwide ~300 million phones in India
115 million of 1 billion phones sold worldwide in 2007 were smartphones 7 million added in India each month
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Mobile Phones Mobility Computing + communication + sensing
Far more capable & ubiquitous than special purpose sensors
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Nericell Overview Using sensors individually
Accelerometer drive quality Microphone honking GSM radio and GPS localization Using sensors in combination Virtual reorientation of accelerometer Distinguishing pedestrians from stop-and-go traffic Triggered sensing
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Energy Challenge Measurements made on HP iPAQ hw6965 9
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Accelerometer-based Sensing
Advantage: low energy cost Challenge: “disorientation” Analyses: braking detection bump/pothole detection pedestrian versus stop-and-go traffic
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Braking Detection Braking impacts drive quality Two approaches:
GPS: high energy cost (600 mW on iPAQ hw6965) Accelerometer: much cheaper (2 mW + 30mW) Accelerometer-based braking detection:
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Braking Detection braking would cause a surge in aX because the accelerometer would experience a force pushing it to the front. To detect the incidence of braking compute the mean of aX over a sliding window N seconds wide. If the mean exceeds a threshold T, that event is declared as a braking event
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Virtual Reorientation
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Virtual Reorientation
Euler Angles: Any orientation of the accelerometer can be represented by Z-Y-Z (and other equivalent) rotations Three Unknowns (angles): pre-rotation (φpre ), pre tilt (θtilt), post-rotation (ψpost) Knowns: Gravity along Z Braking along X
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Euler Angles
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Virtual ReorientationUsing Gravity
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Using Braking
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Automatic Virtual Reorientation
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Results: Virtual Reorientation
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Braking detection with Virtual Reorientation
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Differentiating pedestrians from stop-and-go traffic
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Road bump detection
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Locating a Road Bump Accelerometer is cheap, so keep on continuously
When bump is detected, use GSM tower matching to estimate location median error: 130 m, 90th percentile: 610 m Send bump report to server If several reports in same vicinity, server triggers GPS on other phones for location fix Sample result: GPS turned on only 3.2% of the time on a 20 km drive with one point of interest
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Pothole Detection
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Pothole Detection High speed (≥ 25 kmph)
z-peak: look for significant spike
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Pothole Detection
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Pothole Detection Low speed (< 25 kmph)
z-sus: look for sustained dip
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Results: Pothole Detection
Training data: 5km long drive with 44 bumps Test data: 35km long drive with 101 bumps
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Microphone-based Sensing
Advantage: ubiquity Challenge: energy, privacy Analyses: honk detection: triggered when accelerometer indicates a lot of braking vehicle type: exposed versus enclosed vehicle
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Honk Detection Efficient detector suitable for mobiles
discrete Fourier transform on 100 ms of audio look for spikes in the kHz range spikes at least T times the mean, where T ranges between 5 and 10 Performance: 5.8% of CPU on the HP iPAQ
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Honk Detection
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Triggered Sensing Use cheap sensors to trigger the activation of expensive sensors when needed Examples: Virtual Reorientation: accelerometer -> GPS Localization: GSM -> GPS Honk detection: accelerometer -> audio
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Resources Trafficsense: Rich monitoring of road and traffic conditions using mobile smartphones P Mohan, VN Padmanabhan, R Ramjee - SenSys'08
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Questions?!
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