Amulet: An Energy-Efficient, Multi-Application Wearable Platform

Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage. We introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime.

[1]  Mi Zhang,et al.  BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring , 2016, MobiSys.

[2]  Hojung Cha,et al.  Accurate Prediction of Available Battery Time for Mobile Applications , 2016, ACM Trans. Embed. Comput. Syst..

[3]  Haowei Wu,et al.  Static detection of energy defect patterns in Android applications , 2016, CC.

[4]  Youngki Lee,et al.  PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-installation Time , 2015, SenSys.

[5]  Seungchul Lee,et al.  Sandra helps you learn: the more you walk, the more battery your phone drains , 2015, UbiComp.

[6]  Ying Gao,et al.  ZOE: A Cloud-less Dialog-enabled Continuous Sensing Wearable Exploiting Heterogeneous Computation , 2015, MobiSys.

[7]  Eleni Stroulia,et al.  The power of system call traces: predicting the software energy consumption impact of changes , 2014, CASCON.

[8]  Prabal Dutta,et al.  Opo: a wearable sensor for capturing high-fidelity face-to-face interactions , 2014, SenSys.

[9]  Yue Chen,et al.  ARMlock: Hardware-based Fault Isolation for ARM , 2014, CCS.

[10]  Sasu Tarkoma,et al.  Carat: collaborative energy diagnosis for mobile devices , 2013, SenSys '13.

[11]  Denzil Ferreira,et al.  Revisiting human-battery interaction with an interactive battery interface , 2013, UbiComp.

[12]  Ramesh Govindan,et al.  Calculating source line level energy information for Android applications , 2013, ISSTA.

[13]  Thomas C. Schmidt,et al.  RIOT OS: Towards an OS for the Internet of Things , 2013, 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[14]  G. Voelker,et al.  eDoctor: automatically diagnosing abnormal battery drain issues on smartphones , 2013, NSDI 2013.

[15]  Ranveer Chandra,et al.  Empowering developers to estimate app energy consumption , 2012, Mobicom '12.

[16]  Hojung Cha,et al.  AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring , 2012, USENIX Annual Technical Conference.

[17]  Ming Zhang,et al.  Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof , 2012, EuroSys '12.

[18]  Bjorn De Sutter,et al.  ARMor: Fully verified software fault isolation , 2011, 2011 Proceedings of the Ninth ACM International Conference on Embedded Software (EMSOFT).

[19]  Bennet S. Yee,et al.  Adapting Software Fault Isolation to Contemporary CPU Architectures , 2010, USENIX Security Symposium.

[20]  Shyamal Patel,et al.  Mercury: a wearable sensor network platform for high-fidelity motion analysis , 2009, SenSys '09.

[21]  Philip Levis,et al.  Surviving sensor network software faults , 2009, SOSP '09.

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

[23]  Eric Eide,et al.  Efficient memory safety for TinyOS , 2007, SenSys '07.

[24]  Deepak Ganesan,et al.  Triage: balancing energy and quality of service in a microserver , 2007, MobiSys '07.

[25]  Muneeb Ali,et al.  Protothreads: simplifying event-driven programming of memory-constrained embedded systems , 2006, SenSys '06.

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

[27]  Matt Welsh,et al.  Simulating the power consumption of large-scale sensor network applications , 2004, SenSys '04.

[28]  Philip Levis,et al.  The nesC language: a holistic approach to networked embedded systems , 2003, SIGP.

[29]  James Cheney,et al.  Cyclone: A Safe Dialect of C , 2002, USENIX Annual Technical Conference, General Track.

[30]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[31]  David E. Culler,et al.  TinyOS: An Operating System for Sensor Networks , 2005, Ambient Intelligence.

[32]  David E. Culler,et al.  The nesC language: A holistic approach to networked embedded systems , 2003, PLDI.