The SensoryPhone project aims to develop an energy efficient phone software and hardware platform for sensor oriented tasks.
SensoryPhone takes a sensor rich mobile device as its starting point. While such a mobile devices possess a multitude of sensing capabilities in hardware, the actual software applications are highly restricted in using the sensors due to battery limitations. The SensoryPhone is a novel software and hardware architecture that enables extensive use of sensing resources in an energy efficient manner, to enable applications to maintain constant awareness of user context and inferred activity states.
- A-Loc: The current location as well as the past location trajectory of a mobile device is immensely useful for several applications, both on the mobile device, such as searching for nearby services, and for external online services such as optimizing home heating controls based on user's expected arrival. However, continuously sensing location using GPS or WiFi/Cell-ID based online location service lookup will drain the mobile device battery very quickly. We develop energy efficient methods that intelligently determine when to sense location and which location modality to use depending on expected sensor error and application accuracy requirements, in order to create the appearance that the mobile device always knows its location.
- LittleRock: The current phone architecture is not designed for continuously running the phone's sensors since the processor energy required to access and process the sensor data is prohibitively high. We have developed a new phone architecture, LittleRock, that enables always-on sensing without noticeably impacting battery life. A low power processor is introduced into the system to continuously sense at power levels that are lower than the phone's standby power, and intelligent software mechanisms are used to activate the primary phone processor only when necessary to take a significant action upon the sensor data.
- Falcon: Falcon uses context information to improve OS performance for multiple OS services and application tasks. As a specific example, we show how the use of context, such as user location and temporal behavior, can be used by the OS to pre-load applications into the memory and provide rapid application launch. Falcon buils system support to introduce context based user behavior learning into the OS and allows specifying OS and application actions for various contextual states.[More...] [News]
- LEAP: GPS is one of the most energy hungry sensors. A large part of the energy is spent on processing the received radio signal in the GPS chip. The LEAP project introduces new methods for GPS signal processing that leverage additional information available on smartphones as well as cloud offloading to reduce the overall energy cost of using GPS based location for latency insenstive location tracking scenarios.
- Hon Chu, Vijay Raman, Jeffrey Shen, Aman Kansal, Victor Bahl, and Romit Roy Choudhury, I am a Smartphone and I Know My User is Driving, in 6th International Conference on Communication Systems and Networks (COMSNETS), Other, 7 January 2014.
- Aman Kansal, Scott Saponas, AJ Brush, Kathryn McKinley, Todd Mytkowicz, and Ryder Ziola, The Latency, Accuracy, and Battery (LAB) Abstraction: Programmer Productivity and Energy Efficiency for Continuous Mobile Context Sensing, in OOPSLA, ACM, 31 October 2013.
- Xuan Bao, Aman Kansal, Paramvir Bahl, David Chu, Romit Roy Choudhury, and Alec Wolman, Helping Mobile Apps Bootstrap with Fewer Users, in The 14th International Conference on Ubiquitous Computing (Ubicomp 2012), ACM, 8 September 2012.
- Moo-Ryong Ra, Bodhi Priyantha, Aman Kansal, and Jie Liu, Improving Energy Efficiency of Personal Sensing Applications with Heterogeneous Multi-Processors, in The 14th International Conference on Ubiquitous Computing (Ubicomp 2012), ACM, 5 September 2012.
- Radhika Mittal, Aman Kansal, and Ranveer Chandra, Empowering Developers to Estimate App Energy Consumption, in ACM Mobicom, ACM, 26 August 2012.
- Tingxin Yan, David Chu, Deepak Ganesan, Aman Kansal, and Jie Liu, Fast App Launching for Mobile Devices Using Predictive User Context, in ACM MobiSys, ACM, 25 June 2012.
- Heitor S. Ramos, Tao Zhang, Jie Liu, Bodhi Priyantha, and Aman Kansal, LEAP: A Low Energy Assisted GPS for Trajectory-Based Services, in 13th ACM International Conference on Ubiquitous Computing (UbiComp), ACM, 17 September 2011.
- Hong Lung Chu, Vijay Raman, Jeffrey Shen, Romit Roy Choudhury, Aman Kansal, and Paramvir Bahl, Poster: You Driving? Talk to You Later, in Mobisys 2011: The Ninth International Conference on Mobile Systems, Applications, and Services. (BEST POSTER AWARD), ACM, 28 June 2011.
- David Chu, Aman Kansal, Jie Liu, and Feng Zhao, Mobile Apps: It’s Time to Move Up to CondOS, in 13th Workshop on Hot Topics in Operating Systems (HotOS XIII), USENIX, 9 May 2011.
- Jie Liu, Michel Goraczko, Aman Kansal, Dimitrios Lymberopoulos, Suman Nath, and Bodhi Priyantha, Subjective Sensing: Mission Statement and A Research Agenda, no. MSR-TR-2010-108, July 2010.