skip to main content
10.1145/2517351.2517372acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities

Published: 11 November 2013 Publication History

Abstract

Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations.
To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy/quality trade-offs.
We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90% depending on the scenario) compared to a representative collection of existing approaches.

References

[1]
Amazon mechanical turk. http://mturk.com.
[2]
AnTuTu. http://www.antutulabs.com/AnTuTu-Benchmark.
[3]
Apple iOS Location SERVICE. support.apple.com/kb/HT4995.
[4]
NenaMark. http://nena.se/nenamark.
[5]
NeoCore. http://play.google.com/store//apps/details?id=com.qualcomm.qx.neocore
[6]
Skyhook wireless. http://www.skyhookwireless.com/.
[7]
Stringfly. www.stringfly.com/.
[8]
CMU Sphinx Speech Recognition Engine. http://cmusphinx.sourceforge.net/.
[9]
Waze. http://www.waze.com/.
[10]
Windows azure. http://www.windowsazure.com/en-us/.
[11]
T. Abdelzaher, Y. Anokwa, P. Boda, J. Burke, D. Estrin, L. Guibas, A. Kansal, S. Madden, J. Reich. Mobiscopes for human spaces. IEEE Pervasive Computing, 6(2):20--29, 2007.
[12]
P. Bahl, V. N. Padmanabhan. Radar: An in-building rf-based user location and tracking system. In INFOCOM '00.
[13]
R. K. Balan, K. X. Nguyen, L. Jiang. Real-time trip information service for a large taxi fleet. In MobiSys '11.
[14]
C. M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, August 2006.
[15]
J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, M. B. Srivastava. Participatory sensing. In WSW '06.
[16]
A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, R. A. Peterson. People-centric urban sensing. In WICON '06.
[17]
Q. Cao, T. Abdelzaher, T. He, J. Stankovic. Towards optimal sleep scheduling in sensor networks for rare-event detection. In IPSN '05.
[18]
Y. Chon, N. D. Lane, F. Li, H. Cha, F. Zhao. Automatically characterizing places with opportunistic crowdsensing using smartphones. In UbiComp '12.
[19]
T.-M.-T. Do, D. Gatica-Perez. By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In MUM '10.
[20]
S. B. Eisenman, N. D. Lane, A. T. Campbell. Techniques for improving opportunistic sensor networking performance. In DCOSS '08.
[21]
J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, H. Balakrishnan. The pothole patrol: using a mobile sensor network for road surface monitoring. In MobiSys '08.
[22]
Z. Fang, Z. Guoliang, S. Zhanjiang. Comparison of different implementations of mfcc. J. Comput. Sci. Technol., 16(6):582--589, 2001.
[23]
Y. Freund, R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In EuroCOLT '95.
[24]
P. Jacko. Dynamic Priority Allocation in Restless Bandit Models. Lambert Academic Publishing, 2010.
[25]
P. Jacko, J. Nino-Mora. Time-constrained restless bandits and the knapsack problem for perishable items. Electronic Notes in Discrete Mathematics, 28:145--152, 2007.
[26]
E. Koukoumidis, L.-S. Peh, M. R. Martonosi. Signalguru: leveraging mobile phones for collaborative traffic signal schedule advisory. In MobiSys '11.
[27]
A. Krause, E. Horvitz, A. Kansal, F. Zhao. Toward community sensing. In IPSN '08.
[28]
N. D. Lane, M. Mohammod, M. Lin, X. Yang, H. Lu, S. Ali, A. Doryab, E. Berke, T. Choudhury, A. Campbell. BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing. In PervasiveHealth '11.
[29]
F. Li, C. Zhao, G. Ding, J. Gong, C. Liu, F. Zhao. A reliable and accurate indoor localization method using phone inertial sensors. In UbiComp '12.
[30]
F. Lin, P. Chiu. A near-optimal sensor placement algorithm to achieve complete coverage-discrimination in sensor networks. Communications Letters, IEEE, 9(1):43--45, 2005.
[31]
H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, A. T. Campbell. The Jigsaw Continuous Sensing Engine for Mobile Phone Applications. In SenSys '10.
[32]
N. C. Oza, S. Russell. Online bagging and boosting. In In Artificial Intelligence and Statistics 2001, pages 105--112. Morgan Kaufmann, 2001.
[33]
J. Papastavrou, S. Rajagopalan, A. J. Kleywegt. The dynamic and stochastic knapsack problem with deadlines. Operations Research, 42:1706--1718, 1996.
[34]
R. K. Rana, C. T. Chou, S. S. Kanhere, N. Bulusu, W. Hu. Ear-phone: an end-to-end participatory urban noise mapping system. In IPSN '10.
[35]
S. Reddy, J. Burke, D. Estrin, M. Hansen, and M. Srivastava. Determining transportation mode on mobile phones. In ISWC '08.
[36]
K. Ross, D. Tsang. The stochastic knapsack problem. Communications, IEEE Transactions on, 37(7):740--747, 1989.
[37]
C. Shin, J.-H. Hong, A. K. Dey. Understanding and prediction of mobile application usage for smart phones. In UbiComp '12.
[38]
A. Thiagarajan, L. R. Sivalingam, K. LaCurts, S. Toledo, J. Eriksson, S. Madden, H. Balakrishnan. VTrack: Accurate, Energy-Aware Traffic Delay Estimation Using Mobile Phones. In Sensys '09.
[39]
L. von Ahn, B. Maurer, C. Mcmillen, D. Abraham, M. Blum. reCAPTCHA: Human-Based Character Recognition via Web Security Measures. Science, pages 1160379+, August 2008.
[40]
B. Yan, G. Chen. Appjoy: personalized mobile application discovery. In MobiSys '11.
[41]
T. Yan, V. Kumar, D. Ganesan. Crowdsearch: exploiting crowds for accurate real-time image search on mobile phones. In MobiSys '10.
[42]
C. Yoon, D. Kim, W. Jung, C. Kang, H. Cha. Appscope: application energy metering framework for android smartphones using kernel activity monitoring. In ATC'12.

Cited By

View all
  • (2024)Energy Optimization for Federated Learning on Consumer Mobile Devices With Asynchronous SGD and Application Co-ExecutionIEEE Transactions on Mobile Computing10.1109/TMC.2024.337923623:11(10235-10250)Online publication date: Nov-2024
  • (2023)Social Welfare-Based Task Assignment in Mobile CrowdsensingInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.32613416:3(1-28)Online publication date: 20-Jul-2023
  • (2023)Techniques for Improving the Energy Efficiency of Mobile Apps: A Taxonomy and Systematic Literature Review2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA60479.2023.00051(286-292)Online publication date: 6-Sep-2023
  • Show More Cited By

Index Terms

  1. Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
      November 2013
      443 pages
      ISBN:9781450320276
      DOI:10.1145/2517351
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 11 November 2013

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. crowdsourcing
      2. smartphone sensing

      Qualifiers

      • Research-article

      Conference

      Acceptance Rates

      SenSys '13 Paper Acceptance Rate 21 of 123 submissions, 17%;
      Overall Acceptance Rate 198 of 990 submissions, 20%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)34
      • Downloads (Last 6 weeks)5
      Reflects downloads up to 14 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Energy Optimization for Federated Learning on Consumer Mobile Devices With Asynchronous SGD and Application Co-ExecutionIEEE Transactions on Mobile Computing10.1109/TMC.2024.337923623:11(10235-10250)Online publication date: Nov-2024
      • (2023)Social Welfare-Based Task Assignment in Mobile CrowdsensingInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.32613416:3(1-28)Online publication date: 20-Jul-2023
      • (2023)Techniques for Improving the Energy Efficiency of Mobile Apps: A Taxonomy and Systematic Literature Review2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA60479.2023.00051(286-292)Online publication date: 6-Sep-2023
      • (2023)Dynamic Scheduling for Quality of Information Maximization in Location-aware Opportunistic Mobile Crowdsensing2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)10.1109/PIMRC56721.2023.10293890(1-6)Online publication date: 5-Sep-2023
      • (2023)Green Dueling BanditsICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279518(5129-5134)Online publication date: 28-May-2023
      • (2023)Mobile crowdsensing with energy efficiency to control road congestion in internet cloud of vehicles: a reviewMultimedia Tools and Applications10.1007/s11042-023-17611-z83:18(53949-53974)Online publication date: 28-Nov-2023
      • (2023)Research on Cost Control of Mobile Crowdsourcing Supporting Low Budget in Large Scale Environmental Information MonitoringComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-2385-4_11(148-163)Online publication date: 13-May-2023
      • (2022)GoComfort: Comfortable Navigation for Autonomous Vehicles Leveraging High-Precision Road Damage CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2022.3198089(1-18)Online publication date: 2022
      • (2022)Optimizing Mobile Crowdsensing Platforms for Boundedly Rational UsersIEEE Transactions on Mobile Computing10.1109/TMC.2020.302375721:4(1305-1318)Online publication date: 1-Apr-2022
      • (2022)OPAT: Optimized Allocation of Time-Dependent Tasks for Mobile CrowdsensingIEEE Transactions on Industrial Informatics10.1109/TII.2021.309452718:4(2476-2485)Online publication date: Apr-2022
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media