Workshop on Large-scale Sensing For Future Cities'13 / Tânia Calçada/Daniel Moura: "UrbanSense Platform"

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The UrbanSense platform uses a network of 600 fixed and mobile sensor units to collect environmental and mobility data across Porto, Portugal in real-time. The sensors measure variables like air quality, noise, weather, and traffic patterns. 550 units are installed on public buses to scan the citywide area. Data is transmitted wirelessly and processed locally to anonymously count people and vehicles. The goals are to understand urban phenomena, identify critical areas, detect events, evaluate interventions, and create an open data and wireless testbed for researchers and city planners.

Technology Business

Characteristics
Sensor network – fixed and mobile
• 600 units
• 550 Mobile units
• 50 fixed units

• Units with heterogeneous set of sensors
• 600 environmental sensors
• 60 Video cameras
• 500 GPS, accelerometers, and On Board Devices

• Wireless communications
• Real-time transmission
• Opportunistic communications for delay tolerant data

• Local processing capacity
• Count people and vehicles locally
• Adaptive sampling rate

Goals
UrbanSense platform aims to improve the understanding and awareness
of environmental and behaviour phenomena
• Impact for the city

• Research platform

• Identify critical urban areas

• Open data

• Detect events in real time and automatically

• Wireless networks testbed

• Evaluate the impact of urban interventions

• Data analysis and pattern recognition
• Urban planning

Sensors
UrbanSense includes 600 sensor units. Hererogeneous sets of sensors.
Meteorological
• Temperature
Relative Humidity
•

75 sensors (mobile and fixed)

•

10 sensors (fixed)

•

•

75 sensors (mobile and fixed)

• Solar Radiation
•

10 sensors (fixed)

50 sensors (mobile and fixed)

• Azote Dioxide
75 sensors (mobile and fixed)

•
•

•

•

50 sensors (mobile and fixed)

• Carbon Dioxide
•

•

• GPS and Accelerometer
•

50 sensors (mobile and fixed)

500 sensors (mobile)

• OBD – On Board Device

50 sensors (mobile and fixed)

• Carbon Monoxide

25 sensors (fixed)

Mobility

75 sensors (mobile and fixed)

• Particles PM10

High precision
1 sensor (fixed)

• Embebed

• Ozone (O3)

•

• Luminosity

• Stand alone

• VOC

•

• Pluviometer,
Wind Vane
Anemometer

Noise

Air Pollution

Video
• Cameras
•

60 sensors (mobile and fixed)

Sensors Location
Mobile sensor units located in buses: inside bus or at the roof.
Static units located in traffic lights.

Wireless Communications
Wi-fi Sensor network for interoperability with existing hotspots.
• Mobile units
• Vehicular Adhoc Network

• Static units
• Wi-fi hotspots
• Opportunistic communications – data mules

Deployment plan
2013
500 vehicles – Installed
• Buses – STCP
• Trucks – Leixões harbour

Video Cameras
• Pattern recognition software development
• Acquisition process completed

Environmental sensors
• Requirements gathering
• Public consultation for hardware acquisition

1st quarter 2014
Environmental sensors
• Hardware and software development
• Installation on buses and fixed locations

Video Cameras
• Pattern recognition software development
• installation

Perceiving people and vehicles
anonymously
Contours computed with RPi
Local Processing
=
Anonymity
&
Light Communication

At time 15:01
8 leaved
10 entered
•
•
•
•

No video streaming
No video storage
Images described by statistics (descriptors)
Low bandwidth requirements

Perceiving people and vehicles
anonymously
Sensing unit prototype
What can be done?
• Counting people / vehicles
• Classifying vehicles
• Detecting patterns (e.g. crowding)

Where?
• Streets
• Buildings
• Public transports

Buses as City Scanners
Frequency map
Buses have city-wide coverage
• Scanning the city using sensors
• Detecting and predicting traffic jams
• Characterizing mobility

B

A
#vehicles / day
>= 47
40 to 46
34 to 39
27 to 33
21 to 26
14 to 20
8 to 13
1 to 7

(Sample: 108 buses, Wed 27/Nov/2013)

Buses as City Scanners
Time map
Buses have city-wide coverage
• Scanning the city using sensors
• Detecting and predicting traffic jams
• Characterizing mobility

#vehicles x hours
>= 3.5
3.0 to 3.4
2.5 to 2.9
2.0 to 2.4
1.5 to 1.9
1.0 to 1.4
0.5 to 0.9
0.0 to 0.4

(Sample: 108 buses, Wed 27/Nov/2013)

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