Context-for-wireless: context-sensitive energy-efficient wireless data transfer

By Ahmad Rahmati, Lin Zhong. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2007

311 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/646a78a48551d7acb1ad0139fa8c43bcc464553a

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

DORA: Optimizing Smartphone Energy Efficiency and Web Browser Performance under Interference (2018, background): “And, as the technology and system management techniques for handheld display [4]–[9] and communication fabrics [10] offer higher performance while consuming less power, the performance and energy efficiency of processors on mobile SoCs become more critical to the overall smartphone energy efficiency”
Improving energy efficiency of location sensing on smartphones (2010, background): “In addition, work [31] selects between two data services driven by history, which bears the idea of subs tit tion.”
BreadCrumbs: forecasting mobile connectivity (2008, background): “Rahmati and Zhong [23] investigated the problem of choosing between WiFi and cellular data networks, given that a large number of mobile devices now feature both radios (e.”
Augmenting mobile 3G using WiFi (2010, background): “[18] estimate the power consumption of different interfaces for various network activities.”
LiveLab (2011, background): “For mobile users, a fine-grained coverage map that provides the performance of available networks at a location and time can help the client selects the network interface properly [6], and makes judicious tradeoffs between energy efficiency and communication delay [5].”
Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings (2013, unknown): “[43] and Agarwal et al.”
A Survey of Green Mobile Networks: Opportunities and Challenges (2011, background): “Targeting those multi-radio smartphones, the work in [ 60 ] quantitatively measures and estimates the power consumption of devices with different radios for various network activities and application contents, and then devises algorithms to learn and predict available network resources in order to a”