Efficiently Running Continuous Monitoring Applications on Mobile Devices using Sensor Hubs

Smartphone applications that continuously monitor user context are becoming popular for applications ranging from health-care to lifestyle monitoring to participatory sensing. Unfortunately, these emerging apps make poor use of the already scarce energy resources because they continually wake the CPU and other mobile device components for periodic sensing, computation, and communication. We report measurements of three representative Android smartphone apps that show 77% of the battery is wasted by constantly waking up the device components. Moreover, we find that existing power optimization techniques provide only modest benefits for this new class of apps. Using a trace-driven study of the three apps, we estimate that reducing 3G overhead, offloading to the cloud, offloading 3G traffic to WiFi, and using sensor duty cycling, can reduce only 5-10% of the energy costs. For greater energy savings, we make the case for platform support in the form a sensor hub. Sensor hubs are dedicated subsystems that interface with the sensors and radios; they use a micro-controller to support low cost sensing and computation. Our trace-driven analysis shows that sensor hubs can reduce the power consumption of continuous monitoring apps by 61%.

[1]  Jie Liu,et al.  LittleRock: Enabling Energy-Efficient Continuous Sensing on Mobile Phones , 2011, IEEE Pervasive Computing.

[2]  Yaoxue Zhang,et al.  TailTheft: leveraging the wasted time for saving energy in cellular communications , 2011, MobiArch '11.

[3]  Romit Roy Choudhury,et al.  EnLoc: Energy-Efficient Localization for Mobile Phones , 2009, IEEE INFOCOM 2009.

[4]  Andrew T. Campbell,et al.  Bewell: A smartphone application to monitor, model and promote wellbeing , 2011, PervasiveHealth 2011.

[5]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[6]  Zhen Wang,et al.  Reflex: using low-power processors in smartphones without knowing them , 2012, ASPLOS XVII.

[7]  Feng Qian,et al.  TOP: Tail Optimization Protocol For Cellular Radio Resource Allocation , 2010, The 18th IEEE International Conference on Network Protocols.

[8]  Paramvir Bahl,et al.  Wake on wireless: an event driven energy saving strategy for battery operated devices , 2002, MobiCom '02.

[9]  Arun Venkataramani,et al.  Augmenting mobile 3G using WiFi , 2010, MobiSys '10.

[10]  Mark D. Corner,et al.  Turducken: hierarchical power management for mobile devices , 2005, MobiSys '05.

[11]  Paramvir Bahl,et al.  Fine-grained power modeling for smartphones using system call tracing , 2011, EuroSys '11.

[12]  Ramesh Govindan,et al.  Odessa: enabling interactive perception applications on mobile devices , 2011, MobiSys '11.

[13]  Deborah Estrin,et al.  PEIR, the personal environmental impact report, as a platform for participatory sensing systems research , 2009, MobiSys '09.

[14]  Arun Venkataramani,et al.  Energy consumption in mobile phones: a measurement study and implications for network applications , 2009, IMC '09.

[15]  Paramvir Bahl,et al.  Somniloquy: Augmenting Network Interfaces to Reduce PC Energy Usage , 2009, NSDI.