Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge / /
IndustryTerm
energy error / energy consump / expected energy consumption / still saving energy / non-interactive background applications / application-layer protocol / radio taking energy consumption / estimated energy savings / carrier settings / estimated energy consumption / web browsing / 3G energy environment / given network carrier / software modules / approach work with existing applications / carrier parameters / energy consumption using knowledge / energy savings / network-carrier policy / energy / mobile Internet access / wireless interface / web documents / networked background applications / mobile applications / tail energy falls / energy consumption / maximum achievable energy savings / bank / machine learning algorithm / energy-saving results / 3G networks / power-saving algorithms / online method / wireless device / energy management / less energy / Wireless Energy Consumption Shuo Deng / cellular network operator / Wireless communication / background applications / energy savings increase / cellular networks / machine learning algorithms / 3G energy consumption / wireless network / tail energy / Online Prediction To / overall energy consumption / Wide-area cellular wireless protocols / delay-tolerant applications / energy difference / 3GPP networks / turn-off energy / 3G energy mitigation strategies / email server / 3G wireless energy consumption / 3G network / 3GPP2 networks / energy saving / energy savings range / energy model / learning algorithm / mobile device / at eliminating the tail energy falls / /
OperatingSystem
Android / /
Organization
Institute of Technology Cambridge / Massachusetts Institute of Technology / /
Traffic-Aware Techniques to Reduce 3G/LTE Wireless Energy Consumption Shuo Deng and Hari Balakrishnan Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA, USA