NDLI: Exploiting spatiotemporal and device contexts for energy-efficient mobile embedded systems

Content Provider	IEEE Xplore Digital Library
Author	Donohoo, B. Ohlsen, C. Pasricha, S. Anderson, C.
Copyright Year	2012
Description	Author affiliation: Dept. of Comput. Sci., Colorado State Univ., Fort Collins, CO, USA (Anderson, C.) \|\| Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA (Donohoo, B.; Ohlsen, C.; Pasricha, S.)
Abstract	Within the past decade, mobile computing has morphed into a principal form of human communication, business, and social interaction. Unfortunately, the energy demands of newer ambient intelligence and collaborative technologies on mobile devices have greatly overwhelmed modern energy storage abilities. This paper proposes several novel techniques that exploit spatiotemporal and device context to predict device interface configurations that can optimize energy consumption in mobile embedded systems. These techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression with neural networks, and k-nearest neighbor are explored and compared on synthetic and user traces from real-world usage studies. The experimental results show that up to 90% successful prediction is possible with neural networks and k-nearest neighbor algorithms, improving upon prediction strategies in prior work by approximately 50%. Further, an average improvement of 24% energy savings is achieved compared to state-of-the-art prior work on energy-efficient location-sensing.
Starting Page	1274
Ending Page	1279
File Size	706081
Page Count	6
File Format	PDF
ISBN	9781450311991
ISSN	0738100X
e-ISBN	9781450311991
DOI	10.1145/2228360.2228599
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Context Prediction algorithms Mathematical model Accuracy Machine learning algorithms Global Positioning System Neural networks Machine Learning Smartphone Energy Optimization
Content Type	Text
Resource Type	Article

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