Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A framework of energy efficient mobile sensing for automatic user state recognition
Proceedings of the 7th international conference on Mobile systems, applications, and services
Predicting the location of mobile users: a machine learning approach
Proceedings of the 2009 international conference on Pervasive services
Less is more: energy-efficient mobile sensing with senseless
Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds
Energy-aware network selection using traffic estimation
Proceedings of the 1st ACM workshop on Mobile internet through cellular networks
Energy consumption in mobile phones: a measurement study and implications for network applications
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Introduction to Machine Learning
Introduction to Machine Learning
Energy-delay tradeoffs in smartphone applications
Proceedings of the 8th international conference on Mobile systems, applications, and services
Energy-accuracy trade-off for continuous mobile device location
Proceedings of the 8th international conference on Mobile systems, applications, and services
Energy-efficient rate-adaptive GPS-based positioning for smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Improving energy efficiency of location sensing on smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Movement detection for power-efficient smartphone WLAN localization
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
Energy-Efficient Location Logging for Mobile Device
SAINT '10 Proceedings of the 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet
Power Saving in Mobile Devices Using Context-Aware Resource Control
ICNC '10 Proceedings of the 2010 First International Conference on Networking and Computing
Context-aware device self-configuration using self-organizing maps
Proceedings of the 2011 workshop on Organic computing
TOP: Tail Optimization Protocol For Cellular Radio Resource Allocation
ICNP '10 Proceedings of the The 18th IEEE International Conference on Network Protocols
ICCD '11 Proceedings of the 2011 IEEE 29th International Conference on Computer Design
iLauncher: an intelligent launcher for mobile apps based on individual usage patterns
Proceedings of the 28th Annual ACM Symposium on Applied Computing
| Hi-index | 0.00 |
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.