ABSTRACT
The widespread use of sensor-equipped smartphones and wearables has enabled the rapid growth of personal sensing applications that monitor user behavior. Examples include continuously sampling the microphone for the detection of stressed speech or processing the accelerometer sensor stream for transportation mode detection. Deploying multiple of these applications on the mobile device is a rich source of behavioral insights but poses a significant strain on the battery life. The aim of the dissertation work is to make full use of the heterogeneous resources available in off-the-shelf smartphones and wearables (viz. CPU, low-power co-processors and wireless) to efficiently and fairly orchestrate the execution of multiple interacting sensing applications. This requires building an abstraction scheduling layer that transparently distributes sensor processing tasks and optimizes the utilization of shared computing resources to meet individual app requirements.
- Google Nexus 6. https://www.google.com/nexus/6/.Google Scholar
- iPhone 6 M8 Motion Coprocessor. https://www.apple.com/iphone-6/technology/.Google Scholar
- Qualcomm Hexagon DSP. https://developer.qualcomm.com/mobile-development/maximize-hardware/multimedia-optimization-hexagon-sdk/hexagon-dsp-processor.Google Scholar
- E. Boutin, J. Ekanayake, W. Lin, B. Shi, J. Zhou, Z. Qian, M. Wu, and L. Zhou. Apollo: Scalable and coordinated scheduling for cloud-scale computing. In OSDI'14. Google ScholarDigital Library
- Z. Chen, M. Lin, F. Chen, N. Lane, G. Cardone, R. Wang, T. Li, Y. Chen, T. Choudhury, and A. Campbell. Unobtrusive sleep monitoring using smartphones. In PervasiveHealth'13. Google ScholarDigital Library
- C. Delimitrou and C. Kozyrakis. Paragon: Qos-aware scheduling for heterogeneous datacenters. In ASPLOS '13. Google ScholarDigital Library
- P. Georgiev, N. D. Lane, K. Rachuri, and C. Mascolo. DSP.Ear: Leveraging co-processor support for continuous audio sensing on smartphones. In SenSys'14. Google ScholarDigital Library
- M. Isard, V. Prabhakaran, J. Currey, U. Wieder, K. Talwar, and A. Goldberg. Quincy: Fair scheduling for distributed computing clusters. In SOSP '09. Google ScholarDigital Library
- N. D. Lane, P. Georgiev, C. Mascolo, and Y. Gao. ZOE: A cloud-less dialog-enabled continuous sensing wearable exploiting heterogeneous computation. In MobiSys'15. Google ScholarDigital Library
- H. Lu, A. J. B. Brush, B. Priyantha, A. K. Karlson, and J. Liu. Speakersense: Energy efficient unobtrusive speaker identification on mobile phones. In Pervasive'11. Google ScholarDigital Library
- H. Lu, D. Frauendorfer, M. Rabbi, M. S. Mast, G. T. Chittaranjan, A. T. Campbell, D. Gatica-Perez, and T. Choudhury. Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In UbiComp '12. Google ScholarDigital Library
- H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. T. Campbell. The jigsaw continuous sensing engine for mobile phone applications. In SenSys '10. Google ScholarDigital Library
- O. Moreira, F. Valente, and M. Bekooij. Scheduling multiple independent hard-real-time jobs on a heterogeneous multiprocessor. In EMSOFT '07. Google ScholarDigital Library
- C. Shen, S. Chakraborty, K. R. Raghavan, H. Choi, and M. B. Srivastava. Exploiting processor heterogeneity for energy efficient context inference on mobile phones. In HotPower '13. Google ScholarDigital Library
- D. Shin and J. Kim. Power-aware scheduling of conditional task graphs in real-time multiprocessor systems, 2003.Google ScholarDigital Library
- C. Xu, S. Li, G. Liu, Y. Zhang, E. Miluzzo, Y.-F. Chen, J. Li, and B. Firner. Crowd++: Unsupervised speaker count with smartphones. In UbiComp '13. Google ScholarDigital Library
Index Terms
- Energy Efficient and Fair Management of Sensing Applications on Heterogeneous Resource Mobile Devices
Recommendations
A framework of energy efficient mobile sensing for automatic user state recognition
MobiSys '09: Proceedings of the 7th international conference on Mobile systems, applications, and servicesUrban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, continuously capturing this contextual information on ...
A Case for Lease-Based, Utilitarian Resource Management on Mobile Devices
ASPLOS '19: Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating SystemsMobile apps have become indispensable in our daily lives, but many apps are not designed to be energy-aware that they may consume the constrained resources on mobile devices in a wasteful manner. Blindly throttling heavy resource usage, while helps ...
Towards energy-efficient streaming system for mobile hotspots
SIGCOMM '11: Proceedings of the ACM SIGCOMM 2011 conferenceModern mobile devices have become an important part of our daily life but the performance of multimedia applications still suffers from the constrained energy supply and communication bandwidth of the mobile devices. In this work, we develop an energy-...
Comments