20160831 BEST Summer School

•  Ana Aguiar •  University of Porto, ECE Assistant Professor •  Ins6tuto de Telecomunicações Researcher

•  Wireless networked systems –  IoT Systems – 802.11 Wireless Networks – Data mining and signal processing for geographic data

Porto Living Lab: Urban Scale Sensing and Communica8on Infrastructure Ana Aguiar, [email protected] FEUP, IT

Medium sized city (≈250k inhabitants) Multi-modal transport system Fiber optics backbone ring Free WiFi hotspots Enterpreneurial tradition 41km2 of real life

Porto

Future Ci6es Project

Expand Centre of Competence in Future Ci6es of U. of Porto

Strengthen Inter-‐Disciplinary Research

Knowledge Transfer to Industry in Portuguese Northern Region

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Living Lab: 3 Infrastructures

•  BusNet & HarbourNet: vehicular network •  UrbanSense: environmental sensors •  SenseMyCity: crowdsensor

UrbanSense Pla,orm Tânia Calçada, Carlos Penichet, Yunior Luis, Bruno Fernandes, Tiago Lourenço, Diogo Guimarães, Ana Aguiar

UrbanSense Understand environmental and behaviour phenomena

Impact in the City •  City opera6ons • Iden6fy cri6cal urban areas

• Detect events automa6cally

• Evaluate impact of urban interven6ons

•  Companies • Test products • Validate business models

Research • Open data • Big data, data mining • Wireless networks • Cyber physical systems • Urban planning • Transporta6on • Climate • Environment • Health

Applica6ons • Pollu6on early warnings • Waste collec6on management

• Garden smart management • Smart parking • Localiza6on • Surveillance • Real estate

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Sensors in a Traffic light pole

Sensors in a Flowerpot

Architecture Overview

Sensor 1

Sensor 1

Sensor 1

Control Board RPi

Data Collec6on Unit

Fixed WiFi

BusNet

Cellular

Backhaul API

Storage

Backoffice

UrbanSense •  Data Collec6on Units •  Developed within the project

jointly with industrial partner •  Low cost sensors •  Processing board: Raspberry Pi •  Local processing and storage

–  Intermifent communica6ons –  Capability for local data analysis

•  Condi6oning circuit electronics –  Control Board

•  Custom made expansion board for RPi

–  Sensor board •  Exposed to elements

•  Enclosure and shield

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Noise

Air quality , RH,

temperature

Processing, storage and control

Solar Radia6on sensor

WiFi interfaces

Weather Sta6on

Data Collec6ng Units

Hardware architecture

2x USB 2.0

2x USB 2.0

Raspberry Pi Model B+ V1.2(C)Raspberry Pi 2014

EthernetRJ45

MicroUSB

Power in

CPU/GPUBroadcomBCM2835512MB SDRAM

Ethernetcontroller

LAN9514

4x USB +

Status LED's

Ethernet

40pins: 28x GPIO, I2C, SPI, UART

1

RUN

Mic

roU

SB

RJ11

RJ11

Power in

Pow

er

LED

's

40 pins: Power, UART

+5V

+12V

Reset SW

+12VDC

18

+5VDC

UARTWiFi 3G

Particles Sensor

CO

03 NO2T&RH

Wind Speed and Direction

Precipitation

SoHware architecture

Data Collector Service

Data Sender Service

Heterogeneous Communications

...Sensor 1 Sensor N

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UrbanSense Cloud •  UrbanSense cloud architecture

–  Based on the SenseMyCity •  Rela6onal database •  UrbanSense server

–  Receives JSON messages from DCUs

–  Process informa6on to write in DB

–  Sends acknowledge messages to DCUs

•  Then, DCUs delete the data from local DB

•  Run on cloud hosted by FEUP and managed by Future Ci6es Project

UrbanSense Server

Firewall

Cloud Server Database Server

UrbanSense database

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DCU US Server DB ServerBundle n

Auth and unbundling

DB insert

DB insertion

insert success

ACK n

Delete Bundle n

Opportunis6c Communica6ons

Low cost communica8ons to collect delay tolerant data Integrate UrbanSense and BusNet

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Data Collec8ng

Unit Road Side Unit

Data Collec8ng

Unit

CloudData Base

Fibe

r Op8

c

WiFi WiFi

WAVE

Sensors Overview Mobile DCUs

50 units

Sta8c DCUs T1: 15 units

Sta8c DCUs T2: 10 units

Counters 60

units

520 sensor

s

Air Quality

Par6cles ✓ ✓ ✓ 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 radia6on ✓ 10

Noise ✓ ✓ 25

Counters (based on video camera) ✓ 50 14/09/16 16

Meteorological Sensors

Installed in sta8c and mobile units

•  Temperature and Rela6ve Humidity

•  75 sensors (mobile and sta6c) •  Model: MaxDetect RHT03 •  Datasheet:

hfp://dlnmh9ip6v2uc.cloudfront.net/datasheets/Sensors/Weather/RHT03.pdf

•  Pluviometer, Wind Vane, and Anemometer

•  10 sensors (sta6c) •  Model: Argent Data Systems – Weather

Sensor Assembly •  Datasheet:

hfps://www.argentdata.com/files/80422_datasheet.pdf

•  Solar Radia6on •  10 sensors (sta6c) •  Model: AlphaOmega SQ-‐110 •  Datasheet:

hfp://www.alphaomega-‐electronics.com/pdf/Apogee/Sensores-‐Luz/Folleto-‐SQ-‐100-‐300.pdf

•  Luminosity

•  75 sensors (mobile and sta6c) •  Model: TSL25911 •  Datasheet:

hfp://pdf1.alldatasheet.com/datasheet-‐pdf/view/621672/AMSCO/TSL25911.html

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Sensors – air quality Installed in sta8c and mobile units

•  Nitrogen Dioxide (NO2) •  75 sensors (mobile and sta6c) •  Model: MICS-‐2714 •  Datasheet:

hfp://www.cdiweb.com/datasheets/e2v/mics-‐2714.pdf

•  Ozone (O3)

•  75 sensors (mobile and sta6c) •  Model: MICS-‐2614 •  Datasheet:

hfp://www.cdiweb.com/datasheets/e2v/1087_Datasheet-‐MiCS-‐2614.pdf

•  Total Suspended Par6cles (TSP) •  50 sensors (mobile and sta6c) •  Model: Shinyei PPD42NS •  Datasheet:

www.sca-‐shinyei.com/pdf/PPD42NS.pdf •  Carbon Monoxide (CO)

•  50 sensors (mobile and sta6c) •  Model: NAP -‐ 505 •  Datasheet:

hfp://www.nemoto.eu/nap-‐505-‐manual.pdf

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On-‐going work: Calibra6on

Compare measurements of UrbanSense sensors with reference sensors •  Use reference sensors

–  Expensive and homologated –  Typically used by environmental scien6sts –  Partners inside UP borrowed the

equipment •  Collect data in same loca6on and 6me •  Sensors calibrated from factory but

validated –  Temperature and humidity –  Weather sta6on and solar radia6on

•  Sensors calibrated easily –  Luminosity –  Noise

•  Sensors not yet calibrated –  Par6cles: ongoing work –  Carbon monoxide (CO): unavailable

reference sensor

•  Ozone (O3) and Nitrogen Dioxide (NO2) –  Difficult because of lack of detailed

manufacturer calibra6on informa6on

–  Calibra6on curves from manufacturer available only for 25ºC and 50% RH

•  Measurements not taken on these condi6ons

–  Reference equipment's condi6on the air before take measurements

–  Create a Machine learning model •  Input: Gas sensor measurements •  Input: Temperature and humidity •  Output: gas concentra6on

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Data Quality via Calibra6on

In the lab On the street

Deployment Loca6ons

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Deployment Status Jan 2016

Data Availability

UrbanSense Ongoing Research

•  Health – Asthma and air air pollu6on -‐ 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 radia6on vs coa6ngs on roads and facades – DEC-‐FEUP: Cecilia Rocha

14/09/16 25

Reference

Yunior Luis, Pedro M. Santos, Tiago Lourenço, Carlos Perez-‐Penichet, Tânia Calçada, Ana Aguiar. “UrbanSense: an Urban-‐scale Sensing Plauorm for the Internet of Things”, in Proc. of the 2ns IEEE Smart Ci6es Conference 2016. Trento, Italy, 2016

Air Quality using Smartphones

•  OpenSense, Zurich (CH):hfp://www.opensense.ethz.ch/trac/wiki/Public/Publica6ons

•  EcoSensor, Valencia (ES): doi: 10.1109/WoWMoM.2016.7523519

•  Air-‐Cloud (CN): doi: 10.1145/2487575.2488188

Urban Crowdsensor

What is a crowdsensor?

Internet of Things Crowdsensor

Leverage the power of the crowd to sense large-‐scale human processes

•  App: What the par6cipant sees –  Collect data automa6cally –  Upload data opportunis6cally, efficiently and securely –  Bafery-‐efficient –  Configurable user interface

IoT CrowdSensor

•  Backoffice: where it all comes together

IoT CrowdSensor

AnonymisedRaw-Data-

…-

Average-Speed-per-Street-

Fuel--Consump:on-

Bike-Metrics--per-Street-

Mood-Mapping-

Replicated*on-premise*

WiFi--Coverage-

Microservices*

Extracted-Informa:on-

API*

ETSI-M2M-

FIFWARE-

REST-

Data Privacy

•  Par6cipants fill out informed consent form •  Par6cipants can access their own data •  Data removed ayer 3 years •  OpenID authen6ca6on •  We store info necessary to enable user to access his own data – We cannot iden6fy the par6cipant (hashed email)

•  We only allow anonymised datasets –  K-‐anonymity – No simultaneous 6me and loca6on

Data Ownership

•  Full access to own data – Visualise – Download – Delete… Yes, really

•  Any6me, over web interface

Personal Perspec6ve

On an Urban Scale

Origin-‐Des6na6on, by Daniel Moura

On an Urban Scale

Ecological Footprint, by João Rodrigues

A Research Tool

•  Procedure and plauorm result from long itera6ve design involving engineers, psychologists and par6cipants

Demographic ques6onnaires •  Age •  Weight •  Experience Vital Jacket

•  Real-‐6me, medical grade electrocardiogram (ECG) •  500Hz sampling rate •  8 bits/sample •  3-‐axis accelerometer •  Event push bufon

Recall

Recall Support

On an Urban Scale

Fuel Consump6on, by Daniel Moura Bus driver stress by João Rodrigues

A Research Tool

•  What about the ci6zen’s mood? – With Cris6na Queirós, FPCEUP

Some Background

•  Paul Ekman, 1970 •  6 universal emotions •  Hapiness, sadness,

fear, surprise, disgust, anger

•  Estado geral de “felicidade” (happiness)

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On an Urban Scale

Mood map, by João Rodrigues

MoodSensor Numbers

•  190 unique users •  871h •  13250km •  1919812 Loca6on Points

•  974616 Fuel consump6on data points

•  205502 WiFi data points

What were people doing?

wai6ng for transport 2%

passing by 10%

leisure 29% work or study

44%

other (home, lunch, non specified)

15%

Does situa6on impact experienced emo6ons?

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

joy sadness fear surprise disgust anger hapiness

Mean

wai6ng for transport

passing by

leisure

work or study

other

Does 6me of day play a role?

0

1

2

3

4

5

6

6 8 10 12 14 16 18 20 22 24

Hapine

ss

Time of day

Work

Leisure

InTransit

SenseMyCity Datasets SenseMyCity SenseMyMood

Trips 8929 8145

Hours 414740 404443

Unique users 182 194

GPS points 15656043 2979585

New campaign under prepara6on for FEUP

Incen6ves are cri6cal

Incen6ves and Sample

SenseMyCity: no incen6ve MoodSensor: emo6onal engagement SenseMyFEUP: material incen6ves

Sample

Lessons Learned: Loca6on Errors

References •  Joao GP Rodrigues, Ana Aguiar, Fausto Vieira, Joao Barros, Joao P

Silva Cunha. “A mobile sensing architecture for massive urban scanning”, in Proc. 14th Interna6onal IEEE Conference on Intelligent Transporta6on Systems (ITSC) 2011. Funchal, Portugal. 2011

•  João GP Rodrigues, Mariana Kaiseler, Ana Aguiar, João P Silva Cunha, João Barros. “A mobile sensing approach to stress detec6on and memory ac6va6on for public bus drivers”, IEEE Transac6ons on Intelligent Transporta6on Systems, 16 (6), pp 3294-‐3303. 2015

•  João GP Rodrigues, Ana Aguiar, Cris6na Queirós. “Opportunis6c Mobile Crowdsensing as a Transporta6on Systems Tool”, in Proc. 19th IEEE Intelligent Transporta6on Systems Conference (ITSC) 2016. Rio de Janeiro, Brazil. 2016

So… what now?

•  Sustainable Mobility: SenseMyFeup

•  UrbanSense + SenseMyMood

SenseMyFEUP

•  Study FEUP’s mobility sustainability –  Joint work with Cecília Silva, CITTA/ DEC, and Comissariado para a Sustentabilidade

– Tradi6onal ques6onnaires – Crowdsensor

•  OD matrices: dura6ons, distances, frequencies, mode

A Lookout…

Thank you!

Ana Aguiar [email protected]

Thank You!

Ana Aguiar [email protected]

20160831 BEST Summer School

Engineering

Transcript of 20160831 BEST Summer School