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Workshop on Cyber-physical Systems Platforms – Tânia Calçada “UrbanSense Platform”

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Workshop on Cyber-physical Systems Platforms – Tânia Calçada “UrbanSense Platform”

  1. 1. UrbanSense Platform Porto Living Lab Tânia Calçada tcalcada@fe.up.pt Center of Competence for Future Cities of the University of Porto Instituto de Telecomunicações
  2. 2. Future Cities project goals Expand Centre of Competence in Future Cities of U. of Porto to Promote Inter-Disciplinary Research and Transfer Knowledge to Industry in Northern Region of Portugal 5/18/2015 2
  3. 3. 1: Form inter-disciplinary teams
  4. 4. 2. Build testbeds for urban-scale experiments
  5. 5. 3. Work closely with end users from day one
  6. 6. 4. Share data sets
  7. 7. 5. Work with Industry Partners 7 Companies Institutional Support
  8. 8. 6. Bring the results out to the world
  9. 9. Cloud Porto Living Lab
  10. 10. Porto Living Lab: On the ground
  11. 11. Crowdsensing application SenseMyCity SenseMyCity: Smart phone application designed to apply to different research projects • Gather data from choice of available sensors – GPS, magnetometer, accelerometer – Wi-Fi and Bluetooth devices • Supports external sensing devices – OBD to collect vehicle’s data – Heart wave and heart beat sensor • Secure transmission and storage • What to do with data? – Fuel consumption estimation – Mobility patterns – Route similarity (e.g. car sharing) – WiFi coverage maps
  12. 12. Vehicular Ad-hoc Network Vehicles connect to each other – V2V to the infrastructure - V2I • Largest vehicle-to-vehicle (V2V) testbed in the world – 600 vehicles • Two independent test beds – BusNet: buses and garbage trucks in the city of Porto – HarborNet: trucks and boats in Leixões harbor • Joint work with – Universidade do Porto – Universidade de Aveiro – VeniamWorks – Instituto das Telecomunicações 5/18/2015 12
  13. 13. Bus Net: free WiFi on board • Started Sep 2014 • 150k – Unique users • 14.4 TB – Internet traffic • 1.3M – Internet sessions
  14. 14. UrbanSense platform Large-scale infrastructure for local monitoring Data Collecting Units Environme ntal sensors Pedestrian counters Low cost devices Cover a restricted area Cloud data services Storage: database Processing: calibration Analysis: data fusion Sharing: open data 5/18/2015 14
  15. 15. UrbanSense goals and aplications Understand and get aware of environmental and behaviour phenomena Impact in the City • City operations • Identify critical urban areas • Detect events automatically • Evaluate impact of urban interventions • Companies • Test products • Validate business models Research • Open data • Big data, data mining • Wireless networks • Cyber physical systems • Urban planning • Transportation • Climate • Environment • Health Applications • Pollution early warnings • Waste collection management • Garden smart management • Smart parking • Localization • Surveillance • Real estate 5/18/2015 15
  16. 16. UrbanSense multi-disciplinar ongoing research • Health – asthma and air air pollution – Morbidity vs cold spells or heat waves • Traffic and urban planning – Act in traffic policies to reduce noise and/or air quality – Solar radiation vs coatings on roads and facades – Smart artistic lighting • Design and social sciences – Feed data to social meeting places – Sensor enclosure integration in urban environment 5/18/2015 16
  17. 17. UrbanSense platform architecture • Data collection Units (DCUs) – Sensors • Pedestrian counters • Environmental sensors – Mobile and static DCUs locations • 50 in the roof of buses • 25 environmental in static locations • 60 pedestrian counters in static locations • Data communications: WiFi – Large availability around the city – City hotspots: Porto Digital or Eduroam – Mobile hotspots offered by vehicular network – Use buses as data mules • Data storage, processing, analysis and sharing – Relational database – Open data: API to access data • Based on REST • Based on Fi-Ware 5/18/2015 17
  18. 18. Data Collecting Units • SW and HW developed within the project • Processing board: Raspberry Pi – Local data analysis and storage – Manage intermittent communications • Conditioning circuit electronics – Control Board • Custom made expansion board for Rpi – Sensor board • Host embedded sensors but exposed to elements • Low cost sensors • 2 Wi-Fi interfaces (1st phase static DCUs) – City hotspots: Management (and data upload) – Opportunistic communications: Data upload • Enclosure and shield – Costume made 5/18/2015 18 Noise Air quality , RH, temperature Processing, storage and control Solar Radiation sensor WiFi interfaces Weather Station
  19. 19. UrbanSense cloud architecture 5/18/2015 19
  20. 20. Opportunistic communications: data mules Low cost communications to collect data that is tolerant so some delay 5/18/2015 20 Data Collecting Unit Road Side Unit Data Collecting Unit Cloud Data Base FiberOptic WiFi WiFi WAVE
  21. 21. Proof of concept Developed in the context of a Master thesis 5/18/2015 21 Sense Unit Road Side Unit On-Board Unit
  22. 22. Demo for MOBICOM 2015 http://paginas.fe.up.pt/~dee09026/ mobicom_dtn/mobicom_dtn.mp4 https://www.youtube.com/watch?v= Hqjx28hpuT8 5/18/2015 22
  23. 23. Sensors configuration overview DCUs T1: 15 units DCUs T2: 10 units DCUs T3: 50 units Counters 60 units 520 sensors Air Quality Particles ✓ ✓ ✓ 75 Carbon monoxide (CO) ✓ ✓ ✓ 50 Ozone (O3) ✓ ✓ ✓ 75 Nitrogen dioxide (NO2) ✓ ✓ ✓ 75 Meteoro logical Temperature & Humidity (RH) ✓ ✓ ✓ 75 Luminosity ✓ ✓ ✓ 75 Anemometer, pluviometer, wind vane ✓ 10 Solar radiation ✓ 10 Noise ✓ ✓ 25 Counters (based on video camera) ✓ 50 5/18/2015 23
  24. 24. Calibration and Validation Methodology Compare measurements of UrbanSense sensors with reference sensors • Use reference sensors – Expensive and homologated devices – Typically used by environmental scientists – Provided by U. Porto research groups • Collect UrbanSense and reference data – Same location and time • Create a Machine learning model – Inputs: • Gas sensor measurements • Temperature and humidity – Output: • Gas concentration 5/18/2015 24 Provided by LEPABE Data Mining model
  25. 25. Sensors Location Traffic lights Balconies 5/18/2015 25 Picture by EPFL
  26. 26. Static deployment location plan • Covered zones – Industrial – Park – Traffic – Touristic – Waterside • 25 static DCUs – 2 DCUs installed in Summer 2014 – 23 DCUs planned to Spring 2015 5/18/2015 26
  27. 27. 1ST DCU AT R. FLORES ON THE 22TH JULY 2014 2ND DCU AT R. DAMIÃO DE GOIS ON THE 12TH AUGUST 2014 3RD DCU AT FEUP ON THE 23TH APRIL 2015 UrbanSense Platform Deployment
  28. 28. Sensors in a Flowerpot at R. Flores, Porto 5/18/2015 28
  29. 29. The Sensors A – Wind direction B – Wind speed C – Precipitation D – Temperature and humidity E – Noise F – Solar radiation C A D E F BE F 5/18/2015 29
  30. 30. Sensors in a Traffic light pole 5/18/2015 30
  31. 31. Sensors in a video surveillance pole
  32. 32. Workshop on cyber-physical systems • 09h30 – Future Cities Project - UrbanSense Platform Tânia Calçada, FEUP/IT • 10h00 – Sigfox network in Portugal Pedro Costa, NarrowNet • 10h30 – COFFEE BREAK AND NETWORKING • 11h00 – Citibrain Rui Costa, Ubiwhere Rui Rebelo, Micro I/O • 12h00 – Smart Water Metering Customer Consumption Francisco Cardoso, Águas do Porto 5/18/2015 32

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