Machine Learning
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Minimizing energy for wireless web access with bounded slowdown
Proceedings of the 8th annual international conference on Mobile computing and networking
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Self-tuning wireless network power management
Proceedings of the 9th annual international conference on Mobile computing and networking
An introduction to variable and feature selection
The Journal of Machine Learning Research
CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces
Proceedings of the 4th international conference on Mobile systems, applications and services
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Towards energy efficient VoIP over wireless LANs
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Micro power management of active 802.11 interfaces
Proceedings of the 6th international conference on Mobile systems, applications, and services
Interactive wifi connectivity for moving vehicles
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
NAPman: network-assisted power management for wifi devices
Proceedings of the 8th international conference on Mobile systems, applications, and services
Catnap: exploiting high bandwidth wireless interfaces to save energy for mobile devices
Proceedings of the 8th international conference on Mobile systems, applications, and services
Avoiding the rush hours: WiFi energy management via traffic isolation
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
SiFi: exploiting VoIP silence for WiFi energy savings insmart phones
Proceedings of the 13th international conference on Ubiquitous computing
E-MiLi: energy-minimizing idle listening in wireless networks
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof
Proceedings of the 7th ACM european conference on Computer Systems
Storage-aware smartphone energy savings
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Effective WiFi power management can strongly impact the energy consumption on Smartphones. Through controlled experiments, we find that WiFi power management on a wide variety of Smartphones is a largely autonomous process that is processed completely at the driver level. Driver level implementations suffer from the limitation that important power management decisions can be made only by observing packets at the MAC layer. This approach has the unfortunate side effect that each application has equal opportunity to impact WiFi power management to consume more energy, since distinguishing between applications is not feasible at the MAC layer. The power cost difference between WiFi power modes is high (a factor of 20 times when idle), therefore determining which applications are permitted to impact WiFi power management is an important and relevant problem. In this paper we propose SAPSM: Smart Adaptive Power Save Mode. SAPSM labels each application with a priority with the assistance of a machine learning classifier. Only high priority applications affect the client's behavior to switch to CAM or Active mode, while low priority traffic is optimized for energy efficiency. Our implementation on an Android Smartphone improves energy savings by up to 56% under typical usage patterns.