Workshop on Vehicular Networks and Sustainable Mobility Testbed

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. UrbanSense platform
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Large-scale
infrastructure for 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 project goals
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
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4. Future Cities project methodology
Bring the results out to the world
Work with Industry Partners
Share data sets
Work closely with end users from day one
Build world-class city-scale testbeds
Form inter-disciplinary teams
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5. Cloud
Porto Living Lab

6. Porto Living Lab: On the ground

7. 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
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8. 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
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9. 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
– Relational database stored in the cloud
– Open data: API to access data
• Based on REST
• Based on Fi-Ware
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10. Data Collecting Units overview
• 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
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Noise
Air quality , RH,
temperature
Processing, storage
and control
Solar
Radiation
sensor
WiFi interfaces
Weather
Station

11. Data Collecting Units architecture
Hardware architecture Software architecture
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12. UrbanSense cloud architecture
• UrbanSense cloud architecture
– Based on the SenseMyCity
• Relational database
• UrbanSense server
– Receives JSON messages from DCUs
– Process information to write in DB
– Sends acknowledge messages to
DCUs
• Then, DCUs delete data from local DB
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13. Opportunistic communications: data mules
Low cost communications to collect data that is tolerant so some delay
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Data
Collecting
Unit Road
Side
Unit
Data
Collecting
Unit
Cloud
Data
Base
FiberOptic
WiFi
WiFi
WAVE

14. Mobile Data collection units
Average time that data takes to be delivered in to a RSU (or too the cloud)
• Data collected by
static devices
• Carried by on buses
• Data delivered to
devices connected to
the cloud through
fiber optics
• Most data is delivery
within less than 2
housr
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15. Proof of concept
Developed in the context of a Master thesis
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Sense Unit Road Side Unit
On-Board Unit

16. Demo at EWSN 2015
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17. Sensors configuration overview
Mobile DCUs
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
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18. Calibration Methodology
Compare measurements of 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 but
validated
– Temperature and humidity
– Weather station and solar radiation
• Sensors calibrated easily
– Luminosity
– Noise
• Ozone (O3) and Nitrogen Dioxide (NO2)
– Difficult because of lack of detailed
manufacturer calibration information
– Calibration curves from manufacturer
available only for 25ºC and 50% RH
• Measurements not taken on these conditions
– 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
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19. Garbage containers covered by bus network
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20. Garbage filling level sensor integration
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21. Sensors Location
Roof of buses Traffic lights Balconies
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Picture by EPFL

22. 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
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23. 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

24. Sensors in a Flowerpot at R. Flores, Porto
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25. 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
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26. Sensors in a Traffic light pole
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27. Sensors in a video surveillance pole

28. Mobile Data collection units
Average frequency of bus network at the city of Porto
• Data collected by
devices in buses
• Data delivered to
devices connected to
the cloud through
fiber optics
• Most spots are visited
every 30 minutes or
less
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29. Next steps
• Finish calibration models implementation
• Develop monitoring system
• Develop automatic software update tool
• Deployment of static DCUs
• Development of mobile DCUs enclosure
• Deployment of mobile DCUs
• Design and development of open data platform
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