Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Significant Motion-Based Adaptive Sampling Module for Mobile Sensing Framework

  • Muthohar, Muhammad Fiqri (Dept. of Electronics and Computer Engineering, Chonnam National University) ;
  • Nugraha, I Gde Dharma (Dept. of Electronics and Computer Engineering, Chonnam National University) ;
  • Choi, Deokjai (Dept. of Electronics and Computer Engineering, Chonnam National University)
  • Received : 2015.11.13
  • Accepted : 2017.03.16
  • Published : 2018.08.31
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Abstract

Many mobile sensing frameworks have been developed to help researcher doing their mobile sensing research. However, energy consumption is still an issue in the mobile sensing research, and the existing frameworks do not provide enough solution for solving the issue. We have surveyed several mobile sensing frameworks and carefully chose one framework to improve. We have designed an adaptive sampling module for a mobile sensing framework to help solve the energy consumption issue. However, in this study, we limit our design to an adaptive sampling module for the location and motion sensors. In our adaptive sampling module, we utilize the significant motion sensor to help the adaptive sampling. We experimented with two sampling strategies that utilized the significant motion sensor to achieve low-power consumption during the continuous sampling. The first strategy is to utilize the sensor naively only while the second one is to add the duty cycle to the naive approach. We show that both strategies achieve low energy consumption, but the one that is combined with the duty cycle achieves better result.

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References

  1. T. Faetti and R. Paradiso, "A novel wearable system for elderly monitoring," Advances in Science and Technology, vol. 85, pp. 17-22, 2013.
  2. P. Pierleoni, L. Pernini, A. Belli, and L. Palma, "An android-based heart monitoring system for the elderly and for patients with heart disease," International Journal of Telemedicine and Applications, vol. 2014, article no. 10, 2014.
  3. A. Raja, A. Tridane, A. Gaffar, T. Lindquist, and K. Pribadi, "Android and ODK based data collection framework to aid in epidemiological analysis," Online Journal of Public Health Informatics, vol. 5, no. 3, article no. 228, 2014.
  4. S. Kumar, W. Nilsen, M. Pavel, and M. Srivastava, "Mobile health: revolutionizing healthcare through transdisciplinary research," Computer, vol. 46, no. 1, pp. 28-35, 2013. https://doi.org/10.1109/MC.2012.392
  5. L. Tong, Q. Song, Y. Ge, and M. Liu, "HMM-based human fall detection and prediction method using tri-axial accelerometer," IEEE Sensors Journal, vol. 13, no. 5, pp. 1849-1856, 2013. https://doi.org/10.1109/JSEN.2013.2245231
  6. O. Aziz, E. J. Park, G. Mori, and S. N. Robinovitch, "Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers," Gait & Posture, vol. 39, no. 1, pp. 506-512, 2014. https://doi.org/10.1016/j.gaitpost.2013.08.034
  7. P. Zhou, Y. Zheng, and M. Li, "How long to wait? Predicting bus arrival time with mobile phone based participatory sensing," in Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, Low Wood Bay, UK, 2012, pp. 379-392.
  8. R. LiKamWa, Y. Liu, N. D. Lane, and L. Zhong, "Can your smartphone infer your mood," in Proceedings of International Workshop on Sensing Applications on Mobile Phone (PhoneSense), Seattle, WA, 2011, pp. 1-5.
  9. A. Bogomolov, B. Lepri, and F. Pianesi, "Happiness recognition from mobile phone data," in Proceedings of 2013 International Conference on Social Computing (SocialCom), Alexandria, VA, 2013, pp. 790-795.
  10. G. Chittaranjan, J. Blom, and D. Gatica-Perez, "Mining large-scale smartphone data for personality studies," Personal and Ubiquitous Computing, vol. 17, no. 3, pp. 433-450, 2013. https://doi.org/10.1007/s00779-011-0490-1
  11. V. K. Singh, L. Freeman, B. Lepri, and A. S. Pentland, "Predicting spending behavior using socio-mobile features," in Proceedings of 2013 International Conference on Social Computing, Alexandria, VA, 2013, pp. 174-179.
  12. N. Maisonneuve, M. Stevens, M. E. Niessen, and L. Steels, "NoiseTube: measuring and mapping noise pollution with mobile phones," in Information Technologies in Environmental Engineering. Heidelberg: Springer, 2009, pp. 215-228.
  13. H. M. Thang, V. Q. Viet, N. D. Thuc, and D. Choi, "Gait identification using accelerometer on mobile phone," in Proceedings of 2012 International Conference on Control, Automation and Information Sciences (ICCAIS), Ho Chi Minh, Vietnam, 2012, pp. 344-348.
  14. T. Hoang and D. Choi, "Secure and privacy enhanced gait authentication on smart phone," The Scientific World Journal, vol. 2014, article ID. 438254, 2014.
  15. J. A. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava, "Participatory sensing," in Proceedings of the 1st Workshop on World-Sensor-Web: Mobile Device Centric Sensory Networks and Applications (WSW 2006), Boulder CO, 2006.
  16. N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell, "A survey of mobile phone sensing," IEEE Communications Magazine, vol. 48, no. 9, pp. 140-150, 2010. https://doi.org/10.1109/MCOM.2010.5560598
  17. Q. Han, S. Liang, and H. Zhang, "Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world," IEEE Network, vol. 29, no 2, pp. 40-45, 2015. https://doi.org/10.1109/MNET.2015.7064901
  18. K. K. Rachuri, C. Mascolo, M. Musolesi, and P. J. Rentfrow, "Sociablesense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing," in Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, Las Vegas, NV, 2011, pp. 73-84.
  19. Google Developers, "About Android," [Online]. Available: https://developer.android.com/about/.
  20. GitHub Inc., "funf-core-android," 2016 [Online]. Available: https://github.com/funf-org/funf-coreandroid/wiki/Configuration.
  21. K. Katevas, H. Haddadi, and L. Tokarchuk, "Poster: Sensingkit: a multi-platform mobile sensing framework for large-scale experiments," in Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, Maui, HI, 2014, pp. 375-378.
  22. G. Cardone, A. Cirri, A. Corradi, L. Foschini, and D. Maio, "MSF: an efficient mobile phone sensing framework," International Journal of Distributed Sensor Networks, vol. 9, no. 3, article no. 538937, 2013.
  23. G. Cardone, A. Cirri, A. Corradi, L. Foschini, and R. Montanari, "Activity recognition for smart city scenarios: Google play services vs. MoST facilities," in Proceedings of 2014 IEEE Symposium on Computers and Communication (ISCC), Funchal, Portugal, 2014, pp. 1-6.
  24. Google Developers, "Sensors overview," [Online]. Available: https://developer.android.com/guide/topics/sensors/sensors_overview.
  25. Google Developers, "Sensor types," [Online]. Available: https://source.android.com/devices/sensors/sensor-types#significant_motion.
  26. Google Developers, "Android dashboard," [Online]. Available: https://developer.android.com/about/dashboards/.
  27. OpenSignal, "Android fragmentation (August 2015)," [Online]. Available: https://opensignal.com/reports/2015/08/android-fragmentation/.
  28. K. S. Narendra and M. A. Thathachar, Learning Automata: An Introduction. Mineola, NY: Dover Publications, 2012.