Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Brad Donohoo
M.S. Final
Mar 23, 2012, 1:00 PM
ECE Conference Room - C101B Engineering
Machine Learning Techniques and Algorithms for Energy Optimization in Mobile Embedded Systems
Abstract: Mobile smartphones and other portable battery
operated embedded systems (PDAs, tablets) are
pervasive computing devices that have emerged in
recent years as essential instruments for
communication, business, and social interactions.
While performance, capabilities, and design are
all important considerations when purchasing a
mobile device, battery lifetime is one of the most
desirable attributes. Battery technology and
capacity has improved over the years, but it still
cannot keep pace with the power consumption
demands of today’s mobile devices. Until a new
battery technology is discovered, this key limiter
has led to a strong research emphasis on extending
battery lifetime by minimizing energy consumption,
primarily using software optimizations. This
thesis presents two strategies that attempt to
optimize mobile device energy consumption with
negligible impact on user perception and quality
of service (QoS).
The first strategy, is an application and
user interaction aware middleware framework that
takes advantage of user idle time between
interaction events of the foreground application
to optimize CPU and screen backlight energy
consumption. The framework dynamically classifies
mobile device applications based on their received
interaction patterns, then invokes a number of
different power management algorithms to adjust
processor frequency and screen backlight levels
The second strategy proposes the usage of
machine learning techniques to learn a user’s
mobile device usage pattern pertaining to
spatiotemporal and device contexts, and then
predict energy-optimal data and location interface
configurations. By learning where and when a
mobile device user uses certain power-hungry
interfaces (3G, WiFi, and GPS), the techniques,
which include variants of linear discriminant
analysis, linear logistic regression, non-linear
logistic regression, and k-nearest neighbor, are
able to dynamically turn off unnecessary
interfaces at runtime in order to save energy.
Adviser: Sudeep Pasricha
Co-Adviser: N/A
Non-ECE Member: N/A
Member 3: Charles Anderson, Computer Science
Addional Members: Anura P. Jayasumana, Electrical and Computer Engineering,N/A
B. K. Donohoo, C. Ohlsen, S. Pasricha, “AURA: An Application and User Interaction Aware Middleware Framework for Energy Optimization in Mobile Devices ” In ICCD ‘11, pp. 168-174, Oct. 2011.

B. K. Donohoo, C. Ohlsen, S. Pasricha, C. Anderson, “Exploiting Spatiotemporal and Device Contexts for Energy-Efficient Mobile Embedded Systems,” Accepted for publication in DAC ’12, Jun. 2012.
Program of Study: