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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