1. A Mobile Sensing Architecture
for Massive Urban Scanning
João G. P. Rodrigues, Ana Aguiar, Fausto Vieira, João
Barros, João P. Silva Cunha
Massive Information Scavenging
© 2010, IT - Instituto de Telecomunicações. Todos os direitos reservados.

2. Objectives and Motivation
Massive Urban Scanning
 Hundreds of public transportation vehicles, can give us a
constant and reliable stream of information
 Private vehicles complement the information with more
roads and traffic awareness
Increasing Safety and Energy Awareness on ITS
 By teaching drivers and companies after data analysis
If you want to sense the environment…
Roads are everywhere (that matters)
 Smartphones and sensors too!
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3. Objectives
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4. Architecture
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5. Proposed System
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6. Data Gathering: Sensors
 GPS -> Position
 WiFi -> Open spots, security analysis
 Bluetooth -> Population density
 Accelerometers -> Driving aggressiveness
 Video and audio -> Road events
 On-Board Diagnostics (OBD)
 Speed, RPM, Fuel Consumption, Accelerator pedal position
 Wearable Technologies – VitalJacket
 ECG -> HeartRate -> Stress
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7. Data characteristics
Basic data set of just 200 Bps
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8. Database
MySQL
Relational DB
 Many joins
 GPS + …
 Many Writes
 Few reads
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9. Initial Setup and Trials – Porto, Portugal
10 Probe Buses of the main Porto Carrier
 Data from GPS, Wifi, Bluetooth, Accelerometer and ECG
500 Taxis
 Real-time 1 Hz GPS traces and taxi status (On service,
waiting, etc…)
Volunteers with private cars
 Data from GPS, Wifi, Bluetooth, Accelerometer, OBD
and ECG
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10. Initial Setup and Trials – Porto, Portugal
GPS 0.5s delayed, 0.2Km/h of average speed difference
Before you drive into tunnels
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11. Visualization
Google Earth or Maps:
 KML file-based – Placemarks’ XML
 Very user friendly
 Spatial and Time correlations
 Many ways to show other data:
 Color of placemark
 Height
 Type
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12. Fuel consumption analysis
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13. Fuel Efficiency vs Speed
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14. Coming Nov 2011
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15. Mobile Application
Collect GPS and Accelerometer
data in real-time
Feedback about fuel consumption
and emissions in real-time
OBD data may be used to calibrate
model
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16. Web Interface
Be aware of own driving
profile
Calculate and compare
consumptions for own trips
Do I consume more or less
than others in my vehicle
category?
Are there more efficient
routes?
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17. NIST Cloud Definition
 “Cloud computing is a model for enabling ubiquitous,
convenient, on-demand network access to a shared
pool of configurable computing resources (e.g.,
networks, servers, storage, applications, and services) that
can be rapidly provisioned and released with minimal
management effort or service provider interaction.“
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18. Can you convince me that our
project would profit from
“cloud computing”? How?
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