Power Saving in Mobile Devices Using Context-Aware Resource Control

We present an effective power reduction scheme for recent mobile devices, e.g., Android devices, which tend to have problems with battery life because some of their applications may be running continuous sensor operations. We propose a context-aware method to determine the minimum set of resources (processor cores and peripherals) that results in meeting a given level of performance. With it, unnecessary processor cores and peripherals can be switched-off without degrading overall performance. Our experimental results indicate that its use can result in a 45% reduction in total power consumption. Since our method does not require applications to be modified, it can even be used easily with downloaded applications.

[1]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[2]  Youngki Lee,et al.  SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments , 2008, MobiSys '08.

[3]  David Wetherall,et al.  Recognizing daily activities with RFID-based sensors , 2009, UbiComp.

[4]  M. Fukuma,et al.  A 1 GIPS 1 W single-chip tightly-coupled four-way multiprocessor with architecture support for multiple control flow execution , 2000, 2000 IEEE International Solid-State Circuits Conference. Digest of Technical Papers (Cat. No.00CH37056).

[5]  Kazuya Murao,et al.  A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption , 2008, Pervasive.

[6]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[7]  Giorgio C. Buttazzo,et al.  Adaptive Workload Management through Elastic Scheduling , 2002, Real-Time Systems.

[8]  Michael Goldfarb,et al.  Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis , 2010, IEEE Transactions on Biomedical Engineering.

[9]  Yi Wang,et al.  A framework of energy efficient mobile sensing for automatic user state recognition , 2009, MobiSys '09.

[10]  C.J. Cohen,et al.  A Surveillance System for the Recognition of Intent within Individuals and Crowds , 2008, 2008 IEEE Conference on Technologies for Homeland Security.

[11]  Qingzhong Li,et al.  User Interest Learning in Pervasive Computing Environment , 2008, 2008 Third International Conference on Pervasive Computing and Applications.

[12]  Daniel P. Siewiorek,et al.  A resource allocation model for QoS management , 1997, Proceedings Real-Time Systems Symposium.

[13]  Yoshida Yutaka,et al.  A 4320MIPS Four-Processor Core SMP/AMP with Individually Managed Clock Frequency for Low Power Consumption , 2007 .

[14]  Marília Curado,et al.  QoS Support for Multi-user Sessions in IP-based Next Generation Networks , 2008, Mob. Networks Appl..

[15]  Kuroda Ichiro Media Processing LSI Architectures for Automotives -- Challenges and Future Trends , 2006 .