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GET2016 - UrbanSense Platform. Porto Living Lab
The Urbansense platform is part of the Porto Living Lab initiative aimed at large-scale environmental monitoring using low-cost data collecting units in Porto. It focuses on interdisciplinary research to understand urban behaviors and environmental impacts, contributing to urban planning and public health. The project involves various applications including pollution management, smart amenities, and involves ongoing research in health, traffic planning, and sensor integration.
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GET2016 - UrbanSense Platform. Porto Living Lab
- 1. UrbanSense Platform Porto LivingLab Cecília Rocha Research Center for Territory, Transports and Environment Sofia Sousa Laboratório de Engenharia de Processos, Ambiente, Biotecnologia e Energia Tânia Calçada Center of Competence for Future Cities of the University of Porto; Instituto de Telecomunicações
- 2. UrbanSense platform 04/01/2016 2 Large-scale infrastructurefor local monitoring Environment and Behavioural Low cost In-depth monitoring Data fusion from multiple sources Data Collecting Units DCUs Static or mobile Cover a restricted area Part of Porto Living Lab from Future Cities project
- 3. Future Cities projectgoals Expand Centre of Competence in Future Cities of U. of Porto to Strengthen Inter-Disciplinary Research and Knowledge Transfer to Industry in Portuguese Northern Region 04/01/2016 3
- 4. Porto Living Lab:On the ground 04/01/2016 4
- 5. UrbanSense goals andaplications 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 04/01/2016 5
- 6. UrbanSense multi-disciplinar ongoingresearch • Health – asthma and air air pollution - LEPABE-FEUP: Sofia Sousa – Morbidity vs cold spells or heat waves - CHERG-FLUP: Ana Monteiro • Traffic and urban planning – Act in traffic policies to reduce noise and/or air quality – DEC-FEUP: Cecilia Rocha – Solar radiation vs coatings on roads and facades – DEC-FEUP: Cecilia Rocha – Smart artistic lighting – KTH: Jose Nuno Sampaio • Design / social – Feed data to social meeting points – PINC: Heitor Alvelos / Paula Trigueiros – Sensor enclosure integration in urban environment – EAUM: Paula Trigueiros 04/01/2016 6
- 7. Data Collecting Unitsoverview • Developed within the project: SW and HW • Processing board: Raspberry Pi • Conditioning circuit electronics – Control Board • Custom made expansion board for Rpi – Sensor board • 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 • Local processing and storage – Local data analysis – Intermittent communications 04/01/2016 7 Noise Air quality , RH, temperature Processing, storage and control Solar Radiation sensor WiFi interfaces Weather Station
- 8. Sensors configuration overview MobileDCUs 50 units Static DCUs T1: 15 units Static DCUs T2: 10 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 04/01/2016 8
- 9. Sensors – noise,video • Noise standalone data logger • 1 sensor (static) • Model: ACOEM 01dB Duo Smart Noise Monitor • Dynamic range: 20-137 dB (A, B) • Frequency range: 8 Hz – 20 KHz • Direction: 0° and 90° • Waterproof, wifi communications, GPS • Embedded noise sensors • 25 sensors (static) • range: 40-130 dB • frequency range: human earing • direction: Omnidirecional Installed in static and mobile units • Video cameras • 60 sensors (mobile or static) • To count people and vehicles • Resolution: 5MP (2592×1944 pixels), fixed focus module • Video: 1080p30, 720p60 and 640x480p60/90 04/01/2016 9
- 10. Sensors – meteorological Installedin static and mobile units • Temperature and Relative Humidity • 75 sensors (mobile and static) • Model: MaxDetect RHT03 • Pluviometer, Wind Vane, and Anemometer • 10 sensors (static) • Model: Argent Data Systems – Weather Sensor Assembly • • Solar Radiation • 10 sensors (static) • Model: AlphaOmega SQ-110 • Luminosity • 75 sensors (mobile and static) • Model: TSL25911 04/01/2016 10
- 11. Sensors – airquality Installed in static and mobile units • Nitrogen Dioxide (NO2) • 75 sensors (mobile and static) • Model: MICS-2714 • Ozone (O3) • 75 sensors (mobile and static) • Model: MICS-2614 • Total Suspended Particles (TSP) • 50 sensors (mobile and static) • Model: Shinyei PPD42NS • Carbon Monoxide (CO) • 50 sensors (mobile and static) • Model: NAP - 505 04/01/2016 11
- 12. Calibration Methodology Compare measurementsof UrbanSense sensors with reference sensors • Use reference sensors – Expensive and homologated – Typically used by environmental scientists – Partners inside UP borrowed the equipment • Collect data in same location and time • Sensors calibrated from factory and validated – Temperature and humidity – Weather station and solar radiation 04/01/2016 12 • Sensors with a less complex calibration procedure – Luminosity – Noise • Sensors not yet calibrated – Particles: ongoing work – Carbon monoxide (CO): unavailable reference sensor
- 13. Calibration Methodology Compare measurementsof UrbanSense sensors with reference sensors 04/01/2016 13 • Ozone (O3) and Nitrogen Dioxide (NO2) – Difficult because of lack of detailed manufacturer calibration information – Reference equipment's condition the air before take measurements – Create a Machine learning model • Input: Gas sensor measurements • Input: Temperature and humidity • Output: gas concentration
- 14. Deployment locations • Coveredzones – Industrial – Park – Traffic – Touristic – Waterside • 70 DCUs – 23 deployed 04/01/2016 14
- 15. Deployment locations • Coveredzones – Industrial – Park – Traffic – Touristic – Waterside • 70 DCUs – 23 deployed 04/01/2016 15
- 16. Open Issues -Deployments • 23 environmental units deployed – 19 currently working – Not working • WiFi problems • no WiFi coverage & 3G hotspots malfunctioning • Proximity to the sea • 50 DCUs delivered – 25 DCUs will be installed in the near future – 25 DCUs will be kept for scheduled work • 23 Pedestrian counters deployed – 14 currently working (7Ago) • Not all calibrated 04/01/2016 16
- 17. Open Issues -Calibration Sensor calibration framework status • Models – Don’t need calibration • Temperature, humidity, wind direction – Preliminary calibration models available • Ozone, NO2, luminosity – Ongoing work for the first approach • Noise, particles – Not addressed (but validated) • CO, Solar Radiation, rain, wind speed • Software – Proof of concept running – Software architecture under discussion People involved • Tiago Lourenço – Software development – Gather sensor data • Sérgio Crisóstomo – Methodology – Regression models – Framework software (API) • Sofia Sousa – Air quality and meteorological sensors • Cecilia Rocha – Noise data evaluation 04/01/2016 17
- 18. Open Issues –Energy and Solar Power • Energy consumption of individual DCU components – Measured using a current sensor – Preliminary results • Power the DCU using solar energy – Use solar panel + battery + charge controller – Charge controller provides battery and solar panel info to Rpi – First setup deployed with success at FEUP-DEEC rooftop 04/01/2016 18
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- 21. Rua das Flores2014: Le 04/01/2016 21
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- 23. Rua das Flores:media global diária 04/01/2016 23
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