Abstract
As smartphones become ubiquitous, their energy consumption remains one of the most important issues. Mobile devices operate in a dynamically changing context, and their embedded sensors can be used to extract the relevant context needed for resource optimization. In this paper, we present a context-aware power management system implemented as a widely-applicable middleware application. Fuzzy inference is used to represent a high-level description of context, which is provided as a service. We test our approach using actual periodic and streaming applications on a mobile phone. Our results show energy reduction of 13-50% for periodic applications, and 18-36% for streaming applications.
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References
Balasubramanian, N., Balasubramanian, A., Venkataramani, A.: Energy Consumption in Mobile Phones: A measurement Study and Implications for Network Applications. In: Proc. of 9th Conf. on Internet Measurement, pp. 280–293 (2009)
Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing 6(2), 161–180 (2010)
Carroll, A., Heiser, G.: An analysis of power consumption in a smartphone. In: Proc. USENIX, p. 21 (2010)
Ciaramella, A., Cimino, M., Lazzerini, B., Marcelloni, F.: Situation-Aware Mobile Service Recommendation with Fuzzy Logic and Semantic Web. In: Proc. 9th Int. Conf. on Intelligent Systems Design and Applications, pp. 1037–1042 (2009)
Chen, G., Kotz, D.: Solar: An Open Platform for Context-Aware Mobile Application. In: 1st Int. Conf. on Pervasive Computing, pp. 41–47 (2002)
Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in smartphone usage. In: Proc. 8th Int. Conf. on Mobile Systems, Applications and Services, pp. 179–194 (2010)
Gündüz, Ş., Özsu, M.T.: A Poisson Model for User Accesses to Web Pages. In: Yazıcı, A., Şener, C. (eds.) ISCIS 2003. LNCS, vol. 2869, pp. 332–339. Springer, Heidelberg (2003)
Hong, J., Suh, E., Kim, S.: Context-aware systems: A literature review and classification. Expert Systems with Applications 36(4), 8509–8522 (2009)
International Electrotechnical Commission. IEC 1131- Programmable Controllers Part 7 - Fuzzy Control Programming (1997)
Kim, K., Min, A., Gupta, D., Mohapatra, P., Singh, J.: Improving Energy Efficiency of Wi-Fi Sensing on Smartphones. In: Proc. of IEEE Int. Conf. on Computer Communications (2011)
Korpipaa, P., Mantyjarvi, J., Kela, J., Keranen, H., Malm, E.: Managing Context Information in Mobile Devices. IEEE Pervasive Computing 2(3), 42–51 (2003)
Lemlouma, T., Layaida, N.: Context-aware adaptation for mobile devices. In: Proc. of IEEE Int. Conf. Mobile on Data Management, pp. 106–111 (2004)
Lin, K., Kansal, A., Lymberopoulos, D., Zhao, F.: Energy-Accuracy Trade-off for Continuous Mobile Device Location. In: Proc. 8th Int. Conf. on Mobile Systems Applications and Services, pp. 285–297 (2010)
Mahesh, M., Calder, M.: Batch Scheduling of Recurrent Applications for Energy Savings on Mobile Phones. Sensor Mesh and Ad Hoc Communications and Networks, 1–3 (2010)
Mäntyjärvi, J., Seppänen, T.: Adapting applications in handheld devices using fuzzy context information. Interacting with Computers 15(4), 521–538 (2003)
MartÃnez, J., John, R., Hissel, D., Péra, M.: A survey-based type-2 fuzzy logic system for energy management in hybrid electrical vehicles. Information Sciences 190(1), 192–207 (2012)
Musolesi, M., Piraccini, M., Fodor, K., Corradi, A., Campbell, A.T.: Supporting Energy-Efficient Uploading Strategies for Continuous Sensing Applications on Mobile Phones. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 355–372. Springer, Heidelberg (2010)
Paek, J., Kim, J., Govindan, R.: Energy-Efficient Rate-Adaptive GPS-based Positioning for Smartphones. In: Proc. 8th Int. Conf. on Mobile Systems Applications and Services, pp. 299–314 (2010)
Ravi, N., Scott, J., Lu, H., Iftode, L.: Context-aware Battery Management for Mobile Phones. Pervasive Computing and Communications, 224–233 (2008)
Rahmati, A., Zhong, L.: Context-for-Wireless: Context-Sensitive Energy-Efficient Wireless Data Transfer. In: Proc. 8th Int. Conf. on Mobile Systems Applications and Services, pp. 165–178 (2007)
Shye, A., Scholbrock, B., Memik, G.: Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures. In: Proc. 42nd Int. Sym. on Microarchitecture, pp. 168–178 (2009)
Weiss, G., Kwapisz, J., Moore, S.: Activity Recognition using Cell Phone Accelerometers. ACM SIGKDD Explorations, 74–82 (2010)
Zhang, L., Tiwana, B., Qian, Z., Wang, Z., Dick, R., Mao, Z., Yang, L.: Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proc. 8th Int. Conf. on Hardware/Software Codesign and System Synthesis, pp. 105–114 (2010)
Zhuang, Z., Kim, K., Singh, J.: Improving Energy Efficiency of Location Sensing on Smartphones. In: Proc. 8th Int. Conf. on Mobile Systems Applications and Services, pp. 315–330 (2010)
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Moghimi, M., Venkatesh, J., Zappi, P., Rosing, T. (2013). Context-Aware Mobile Power Management Using Fuzzy Inference as a Service. In: Uhler, D., Mehta, K., Wong, J.L. (eds) Mobile Computing, Applications, and Services. MobiCASE 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 110. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36632-1_18
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DOI: https://doi.org/10.1007/978-3-642-36632-1_18
Publisher Name: Springer, Berlin, Heidelberg
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