Supporting Energy-Efficient Uploading Strategies for Continuous Sensing Applications on Mobile Phones
Continuous sensing applications (e.g., mobile social networking applications) are appearing on new sensor-enabled mobile phones such as the Apple iPhone, Nokia and Android phones. These applications present significant challenges to the phone’s operations given the phone’s limited computational and energy resources and the need for applications to share real-time continuous sensed data with back-end servers. System designers have to deal with a trade-off between data accuracy (i.e., application fidelity) and energy constraints in the design of uploading strategies between phones and back-end servers. In this paper, we present the design, implementation and evaluation of several techniques to optimize the information uploading process for continuous sensing on mobile phones. We analyze the cases of continuous and intermittent connectivity imposed by low-duty cycle design considerations or poor wireless network coverage in order to drive down energy consumption and extend the lifetime of the phone. We also show how location prediction can be integrated into this forecasting framework. We present the implementation and the experimental evaluation of these uploading techniques based on measurements from the deployment of a continuous sensing application on 20 Nokia N95 phones used by 20 people for a period of 2 weeks. Our results show that we can make significant energy savings while limiting the impact on the application fidelity, making continuous sensing a viable application for mobile phones. For example, we show that it is possible to achieve an accuracy of 80% with respect to ground-truth data while saving 60% of the traffic sent over-the-air.
Unable to display preview. Download preview PDF.
- 1.CRAWDAD Project, http://www.crawdad.org
- 3.Brémaud, P.: Markov Chains, Gibbs Fields, Monte Carlo Simulation, and Queues. Springer, Heidelberg (1998)Google Scholar
- 4.Campbell, A.T., Eisenman, S.B., Lane, N.D., Miluzzo, E., Peterson, R., Lu, H., Zheng, X., Musolesi, M., Fodor, K., Ahn, G.-S.: The Rise of People-Centric Sensing. IEEE Internet Computing Special Issue on Mesh Networks (June/July 2008)Google Scholar
- 6.Consolvo, S., Everitt, K., Smith, I., Landay, J.A.: Design requirements for technologies that encourage physical activity. In: Proceedings of CHI 2006, pp. 457–466. ACM Press, New York (2006)Google Scholar
- 10.Kjærgaard, M.B., Langdal, J., Godsk, T., Toftkjær, T.: Entracked: energy-efficient robust position tracking for mobile devices. In: Proceedings of MobiSys 2009, pp. 221–234. ACM, New York (2009)Google Scholar
- 13.Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Building Personal Maps from GPS Data. In: Proceedings of IJCAI Workshop on Modeling Others from Observation (2005)Google Scholar
- 15.Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R.A., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T.: Sensing Meets Mobile Social Networks: the Design, Implementation and Evaluation of the CenceMe Application. In: Proceedings of SenSys 2008, November 2008, pp. 337–350 (2008)Google Scholar
- 17.Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M.H., Howard, E., West, R., Boda, P.: PEIR, the Personal Environmental Impact Report, as a Platform for Participatory Sensing Systems Research. In: Proceedings of MobiSys 2009, pp. 55–68 (2009)Google Scholar
- 18.Musolesi, M., Miluzzo, E., Lane, N.D., Eisenman, S.B., Campbell, A.T.: The Second Life of a Sensor: Integrating Real-world Experience in Virtual Worlds using Mobile Phones. In: Proceedings of HotEmNets 2008, Charlottesville, Virginia, USA. ACM Press, New York (2008)Google Scholar
- 20.Nokia. Nokia Energy Profiler 1.1, http://www.forum.nokia.com
- 21.Olston, C., Widom, J.: Efficient monitoring and querying of distributed, dynamic data via approximate replication. IEEE Data Engineering Bulletin 28(1), 11–18 (2005)Google Scholar
- 23.Song, L., Deshpande, U., Kozat, U.C., Kotz, D., Jain, R.: Predictability of WLAN Mobility and its Effects on Bandwidth Provisioning. In: Proceedings of INFOCOM 2006 (April 2006)Google Scholar
- 24.Song, L., Kotz, D.: Evaluating Location Predictors with Extensive Wi-Fi Mobility Data. In: Proceedings of INFOCOM 2004, pp. 1414–1424 (2004)Google Scholar