A Mobile Sensing Architecture for Massive Urban Scanning

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A Mobile Sensing Architecture for Massive Urban Scanning

  1. 1. A Mobile Sensing Architecture for Massive Urban ScanningJoão 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. 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! 2
  3. 3. Objectives 3
  4. 4. Architecture 4
  5. 5. Proposed System 5
  6. 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 6
  7. 7. Data characteristics Basic data set of just 200 Bps 7
  8. 8. DatabaseMySQLRelational DB  Many joins   GPS + …  Many Writes  Few reads 8
  9. 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 9
  10. 10. Initial Setup and Trials – Porto, Portugal GPS 0.5s delayed, 0.2Km/h of average speed difference Before you drive into tunnels 10
  11. 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 11
  12. 12. Fuel consumption analysis 12
  13. 13. Fuel Efficiency vs Speed 13
  14. 14. Coming Nov 201114
  15. 15. Mobile ApplicationCollect GPS and Accelerometerdata in real-timeFeedback about fuel consumptionand emissions in real-timeOBD data may be used to calibratemodel 15
  16. 16. Web InterfaceBe aware of own drivingprofileCalculate and compareconsumptions for own tripsDo I consume more or lessthan others in my vehiclecategory?Are there more efficientroutes? 16
  17. 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.“ 17
  18. 18. Can you convince me that our project would profit from “cloud computing”? How? 18

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