Power Aware Design Methodologies
Power Aware Design Methodologies
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
Predicting network availability using user context
Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Energy considerations for a wireless multi-homed environment
EUNICE'11 Proceedings of the 17th international conference on Energy-aware communications
Exploiting spatiotemporal and device contexts for energy-efficient mobile embedded systems
Proceedings of the 49th Annual Design Automation Conference
International Journal of Communication Networks and Distributed Systems
Enabling mobile distributed social networking on smartphones
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
A comparison of the cost and energy efficiency of prefetching and streaming of mobile video
Proceedings of the 5th Workshop on Mobile Video
Energy-budget-compliant adaptive 3D texture streaming in mobile games
Proceedings of the 4th ACM Multimedia Systems Conference
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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This paper proposes an energy-aware handoff algorithm based on energy consumption measurements of UMTS and 802.11 WLAN networks on an Android mobile phone. The handoff algorithm uses estimation of application traffic size to find the minimum energy cost alternative by comparing the cost of using UMTS with the cost of performing an opportunistic downward vertical handoff to a WLAN and using WLAN for the transfer and the eventual upward vertical handoff back to UMTS. Our experiments show that the energy cost of UMTS is nearly equal to WLAN as a function of transfer time, but for bulk transfers, transferring a byte of data over UMTS can be over a hundred times more expensive than over WLAN. Further, we discovered that the energy cost of the vertical handoff is quite high, comparable to downloading 0.12-0.67 MB of data over UMTS. To calculate the energy cost of data transfers before they take place, we propose and evaluate a distributed traffic estimation mechanism. The mechanism can predict how much data will be transferred due to a user action (i.e. clicking of an URL link). We provide initial results on the accuracy of the mechanism. Finally, we perform a numerical analysis on the the performance of the handoff algorithm and show that it can reduce the energy consumption significantly when compared with simple policies.