Assessing Performance of IoT-based Mobile Crowdsensing Systems ...

IEEE CLOUDCOM 2016

Assessing Performance of IoT-based Mobile Crowdsensing Systems for Sensing as a Service Applications in Smart Cities Andrea Capponi

University of Luxembourg

Claudio Fiandrino

Imdea Networks Institute

Christian Franck Ulrich Sorger

University of Luxembourg

Dzmitry Kliazovich Pascal Bouvry

December 15, 2016

Smart Cities: Introduction I

50% of worldwide population lives in cities

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Cities account for I

80% of worldwide gas consumption

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75% of global energy consumption

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60% of residential water use

N. B. Grimm, S. H. Faeth, N. E. Golubiewski, C. L. Redman, J. Wu, X. Bai, and J. M. Briggs, “Global change and the ecology of cities,” in Science, vol. 319, no. 5864, 2008, pp. 756–760.

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Sensing as a Service (S2 aaS) for IoT Sensors

Consumers Cloud/Fog Providers

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Mobile Crowdsensing

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Appealing paradigm for sensing and collecting data I

Monitoring phenomena in smart cities

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Sensing as a Service (S2 aaS) business model

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Sensors commonly available in mobile and IoT devices

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Mobile Crowdsensing I

Appealing paradigm for sensing and collecting data I

I I

Monitoring phenomena in smart cities

Sensing as a Service (S2 aaS) business model Sensors commonly available in mobile and IoT devices

Figure: Diffusion of mobile devices Andrea Capponi | IEEE CLOUDCOM 2016 | Assessing Performance of IoT-based MCS Systems in Smart Cities

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Mobile Crowdsensing I

Appealing paradigm for sensing and collecting data I

I I

Monitoring phenomena in smart cities

Sensing as a Service (S2 aaS) business model Sensors commonly available in mobile and IoT devices

Figure: Diffusion of mobile devices Andrea Capponi | IEEE CLOUDCOM 2016 | Assessing Performance of IoT-based MCS Systems in Smart Cities

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Simulations in Crowdsensing Simulators for Crowdsensing

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Network-oriented:

Mobility-oriented

- NS-3 (1) - CupCarbon (2)

- Sumo (3) - Parking (4)

Communications: tradeoff precision/scalability Mobility: tradeoff realistic/simple environments

1. C. Tanas and J. Herrera-Joancomarti, “Crowdsensing simulation using NS-3,” Citizen in Sensor Networks: Second International Workshop, CitiSens 2013 2. K. Mehdi, M. Lounis, A. Bounceur, and T. Kechadi, “Cupcarbon: A multi-agent and discrete event wireless sensor network design and simulation tool,” in 7th International ICST Conference on Simulation Tools and Techniques, 2014 3. SUMO: https://sourceforge.net/projects/sumo/ 4. K. Farkas and I. Lendak, “Simulation environment for investigating crowd-sensing based urban parking,” in International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), June 2015 Andrea Capponi | IEEE CLOUDCOM 2016 | Assessing Performance of IoT-based MCS Systems in Smart Cities

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Design Principles

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Scalability I

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MCS systems require a large number of participants Performance should not change from small to big cities

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Design Principles

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Scalability I

I

I

MCS systems require a large number of participants Performance should not change from small to big cities

Realistic Urban Environment I

Flexible and adaptable in any city

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Data analytics for municipalities

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Design Principles

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Scalability I

I

I

MCS systems require a large number of participants Performance should not change from small to big cities

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User Mobility I

Design mobility patterns

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Meeting social behaviours

Realistic Urban Environment I

Flexible and adaptable in any city

I

Data analytics for municipalities

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Design Principles

I

Scalability I

I

I

MCS systems require a large number of participants Performance should not change from small to big cities

Realistic Urban Environment I

I

I

Flexible and adaptable in any city Data analytics for municipalities

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User Mobility I

Design mobility patterns

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Meeting social behaviours

Communication Technologies I

LTE/4G

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WiFi

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Bluetooth

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Key Performance Indicators

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Data Generation I

Sensors work with different sample frequency and size

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Mobile devices sample and deliver information

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In S2 aaS revenue models are proportional to the amount of collected data

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Key Performance Indicators

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Data Generation I

Sensors work with different sample frequency and size

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Mobile devices sample and deliver information

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In S2 aaS revenue models are proportional to the amount of collected data

Cost Evaluation I

Energy spent for sensing purposes

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Energy spent for reporting purposes

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CrowdSenSim I

Exploiting presented design principles

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Custom simulator for crowdsensing activities I

Access and download: http://crowdsensim.gforge.uni.lu

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Contact: [email protected]

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CrowdSenSim: Features and Architecture I

Large scale (time-space)

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Realistic urban environments City Layout

List of Events

CrowdSensing Inputs

User Mobility

S IMULATOR

Results

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Evaluation Settings

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Luxembourg City center

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Users: 10 000

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Walking speed: U [1 − 1.5] m/s

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Walking period: U [10 − 30] min

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Simulation period: 8 AM - 2 PM

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Performance Evaluation

Energy consumption for sensing 2 000 1 750 1 500 1 250 1 000 750 500 250 0 0-50 50-100 100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500 500-550 550-600 600-650 650-700 700-750 750-800 800-850

Num. of Participants

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Current drain (µAh)

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Performance Evaluation Energy consumption for reporting

75-80

65-70

70-75

55-60

60-65

50-55

45-50

40-45

35-40

30-35

25-30

20-25

15-20

5-10

10-15

2 000 1 750 1 500 1 250 1 000 750 500 250 0 0-5

Num. of Participants

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Energy (J)

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Conclusion

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MCS systems for S2 aaS applications in smart cities

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Performance evaluation in large scale realistic urban environments

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CrowdSenSim: simulator for Mobile Crowdsensing I

Access and download: http://crowdsensim.gforge.uni.lu

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Contact: [email protected]

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Thank You! Andrea Capponi