Profiling Energy Efficiency of Mobile Crowdsensing Data Collection ...

IEEE Mobile Cloud 2018

Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications Mattia Tomasoni

University of Trento

Andrea Capponi

University of Luxembourg

Claudio Fiandrino

Imdea Networks Institute

Dzmitry Kliazovich

ExaMotive

Fabrizio Granelli Pascal Bouvry

University of Trento University of Luxembourg March 27, 2018

Mobile Crowdsensing I

Appealing paradigm for sensing and collecting data I

Monitoring phenomena in smart cities

I

Sensing as a Service (S2 aaS) business model

I

Sensors commonly available in mobile and IoT devices

Figure: Diffusion of mobile devices Andrea Capponi | IEEE Mobile Cloud 2018 | Profiling Energy Efficiency of MCS Data Collection Frameworks

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Mobile Crowdsensing I

Appealing paradigm for sensing and collecting data I

Monitoring phenomena in smart cities

I

Sensing as a Service (S2 aaS) business model

I

Sensors commonly available in mobile and IoT devices

Figure: Diffusion of mobile devices Andrea Capponi | IEEE Mobile Cloud 2018 | Profiling Energy Efficiency of MCS Data Collection Frameworks

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Mobile Crowdsensing Scenario

S2 aaS Cloud Collector Crowd iFi W

Da

ta E LT

IoT Devices Accelerometer

Gyroscope

Microphone

Dual Camera

Andrea Capponi | IEEE Mobile Cloud 2018 | Profiling Energy Efficiency of MCS Data Collection Frameworks

Temperature

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MCS Data Collection Frameworks (DCFs)

I

General purpose Vs. application specific

I

Data reporting mechanisms

I

Set of rules depending on design of each DCF

I

Objective: Develop a methodology to assess and evaluate a DCF measuring the energy consumption and the amount of acquired data


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DCFs under analysis I

I

I

DETERMINISTIC DISTRIBUTED FRAMEWORK (DDF) I

Continuous data reporting

I

Stopping mechanism

PIGGYBACK CROWDSENSING (PCS) I

Delayed data reporting

I

Smartphone opportunities

PROBABILISTIC DISTRIBUTED ALGORITHM (PDA) I

Probabilistic data reporting

I

Dynamic probability threshold

DDF: A. Capponi et al. “A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures”. In: IEEE Transactions on Sustainable Computing 2.1 (Jan. 2017) PCS: Nicholas D. Lane et al. “Piggyback CrowdSensing (PCS): Energy Efficient Crowdsourcing of Mobile Sensor Data by Exploiting Smartphone App Opportunities”. In: 11th ACM Conference on Embedded Networked Sensor Systems. SenSys. PDA: Federico Montori, Luca Bedogni, and Luciano Bononi. “Distributed Data Collection Control in Opportunistic Mobile Crowdsensing”. In: Proc. of the 3rd Workshop on Experiences with the Design and Implementation of Smart Objects. SMARTOBJECTS. Andrea Capponi | IEEE Mobile Cloud 2018 | Profiling Energy Efficiency of MCS Data Collection Frameworks

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Experimental set-up

I

Android application to emulate data reporting mechanisms

I

Data delivery through Eduroam WiFi network

I

Energy measurements with Power Monitor

I

Network-related measurements with Wireshark


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Data Reporting Mechanisms (DRMs)

I

DELAYED-DRM

I

CONTINUOUS-DRM

I

PROBABILISTIC-DRM


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Energy Consumption and Network Traffic

I

Energy consumption PRO

Network traffic

DEL

CON

1.00

1.00

0.75

0.75

CDF

CDF

CON

I

0.50 0.25

0.50 0.25

0.00

0.00 0

50

100

150

200

250

0

Current (mA) I

I

DEL

PRO

Delayed (DEL): ~40 mA for 75% of the time Continuous (CON): values until 150 mA for 50% of the time

20

40

60

80

100

120

Packets/sec I

During 75% of the transmission: I

I

Both DEL and CON exhibit packet transmission rate as high as 40 packets/s PRO achieves 10 packets/s at max


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CrowdSenSim I

Custom simulator for crowdsensing activities I

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

I

Contact: [email protected]


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Evaluation Settings I

Cities:

(a) Luxembourg I

(b) Turin

(c) Washington DC

Parameters:

I

Num of users: 20,000

I

Sensors: acc, prox, GPS

I

Speed: [1,1.5] m/s

I

Battery level: [80 − 90]%

I

Walking period: [20,40] min

I

Battery capacity: [2,200-3,300] mAh

I

Coordinate granularity: 3 meters

I

Reporting technology: WiFi

P. Vitello, A. Capponi, C. Fiandrino, P. Giaccone, D. Kliazovich, and P. Bouvry, "High-Precision Design of Pedestrian Mobility for Smart City Simulators", in IEEE International Conference on Communications (ICC), Kansas City, MO, USA, May 2018.


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Energy Consumption in City-wide Scenario DDF

PDA

PCS

1.00

DCFs per-user battery drain in Luxembourg City

0.75

CDF

I

0.50 0.25

I

Design of DCFs impact on results

0.00 0

10

20

30

40

50

60

70

Battery Drain (mAh)


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Amount of Collected Data

I

DDF shows less amount of data in the center compare to the PDA due to the stopping mechanism

I

PDA achieves the highest spatial distribution

I

PCS achieves the lowest amount of contributed data. Indeed, the reporting fully depends on the probability of performing phone calls


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User trajectories

I

DDF: users stop generating after exceeding a threshold

I

PDA: users generate data following a probabilistic model

I

PCS: users report data when have smartphone opportunities


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Real measurements

PDA PCS DDF 1 500 1 000 500 0 0

10

20

30

40

50

Battery Drain (mAh) I

Simulation based on: I I

I I

60

70

150 Theoretical values I

200 150 100

50

50 0 0

10

30

40

50

Simulation based on:

0 I

I

I

20

Battery Drain (mAh)

0 I

Power monitor for energy Wireshark for network measures

Linear behavior for DDF High variability for PDA e PCS

PCS PDA DDF

100 Data Contribution (KByte)

Data Contribution (KByte)

I

Data Contribution (KB

200 Energy vs. Amount of Data

10

2

Datasheets for sensing WiFi models for reporting

Batte

Linear behavior for PDA, PCS, DDF


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Conclusion The proposed methodology I

Android application to emulate data reporting mechanisms

I

Energy and network measures with Power Monitor and Wireshark

I

Comparison between different DCFs

CrowdSenSim I

Performance evaluation in large realistic urban environments

I

Custom simulator for crowdsensing activities I

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

I

Contact: [email protected]


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Thank You! Andrea Capponi

Augment OSM Precision - AOP Algorithm


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Evaluation Settings

PtxW = ρid + ρtx · τtx + γxg · λg


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