Published at : 30 Jan 2016
Volume : IJtech
Vol 7, No 1 (2016)
DOI : https://doi.org/10.14716/ijtech.v7i1.1980
Muthohar, M.F., Nugraha, I.G.D., Choi, D., 2016. Exploring Significant Motion Sensor for Energy-efficient Continuous Motion and Location Sampling in Mobile Sensing Application. International Journal of Technology. Volume 7(1), pp.38-49
Muhammad Fiqri Muthohar | School of Electronics & Computer Engineering, Chonnam National University, Gwangju 61186, South Korea |
I Gde Dharma Nugraha | School of Electronics & Computer Engineering, Chonnam National University, Gwangju 61186, South Korea |
Deokjai Choi | School of Electronics & Computer Engineering, Chonnam National University, Gwangju 61186, South Korea |
The significant motion sensor is a new sensor that promises motion detection at low power consumption. Despite that promise, no known research has explored the usage of this sensor, especially in mobile sensing research. In this study, we explore the utilization of this significant motion sensor for continuous motion and location sampling in a mobile sensing application. A location sensor is known for its expensive power consumption in retrieving the location data, and continuously sampling from it will quickly deplete a smartphone battery. We experiment with two sampling strategies that utilize this significant motion sensor to achieve low power consumption during continuous sampling. One strategy involves utilizing the sensor naively, while the other involves combining with the duty cycle. Both strategies achieve low energy consumption, but the one that combines with the duty cycle achieves lower energy consumption. By utilizing this sensor, mobile sensing research especially that samples data from location or motion sensors, will be able to achieve lower energy consumption.
Adaptive sampling, Mobile sensing, Significant motion sensor, Smartphone sensor
Android Open Source Project, 2015. Sensor Types, Available at: https://source.android.com/devices/sensors/sensor-types.html#significant_motion. Accessed on August 8, 2015
Ben Abdesslem, F., Phillips, A., Henderson, T., 2009. Less is More: Energy-efficient Mobile Sensing with Senseless. In: the Proceedings of the 1st ACM Workshop on Networking, Systems, and Applications for Mobile Handhelds, Barcelona, Spain: ACM, pp. 61–62
Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., Srivastava, M.B., 2006. Participatory Sensing. In: the Proceedings of First Workshop on World-Sensor-Web: Mobile Device Centric Sensory Networks and Applications
Cardone, G., Cirri, A., Corradi, A., Foschini, L., Montanari, R., 2014. Activity Recognition for Smart City Scenarios: Google Play Services vs. MoST Facilities. IEEE Symposium on Computers and Communication (ISCC), Funchal: IEEE, pp. 1–6
Google Android, 2015a. Android Dashboard, Available at: http://developer.android.com/about/dashboards/index.html, Accessed on August 6, 2015
Google Android, 2015b. Sensors Overview, Available at: http://developer.android.com/guide/topics/sensors/sensors_overview.html, Accessed on August 6, 2015
Han, Q., Liang, S., Zhang, H., 2015. Mobile Cloud Sensing, Big Data, and 5G Networks Make an Intelligent and Smart World. IEEE Network (March–April 2015), pp. 40–45
Hoang, T., Choi, D., 2014. Secure and Privacy Enhanced Gait Authentication on Smart Phone. The Scientific World Journal, Volume 8, pp. 1–9
Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T., 2010. A Survey of Mobile Phone Sensing. Communications Magazine, IEEE (Sept. 2010), pp. 140–150
Mafrur, R., Nugraha, I.G.D., Choi, D., 2015. Modeling and Discovering Human Behavior from Smartphone Sensing Life-log Data for Identification Purpose. Human-centric Computing and Information Sciences, Volume 5, pp. 1–18
Narendra, K.S., Thathachar, M.A., 1989. Learning Automata: An Introduction. Prentice-Hall, Inc
OpenSignal, 2015. Android Fragmentation Visualized, Available at: http://opensignal.com/reports/2015/08/android-fragmentation/, Accessed on August 8, 2015
Rachuri, K.K., Mascolo, C., Musolesi, M., Rentfrow, P.J., 2011. Sociable Sense: Exploring the Trade-offs of Adaptive Sampling and Computation Offloading for Social Sensing. In: the Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, Mobicom '11, pp. 73–84
Raja, A., Tridane, A., Gaffar, A., Lindquist, T., Pribadi, K., 2014. Android and ODK Based Data Collection Framework to Aid in Epidemiological Analysis. Online Journal of Public Health Informatics, Volume 5(3), pp. 1–27