The evolving mobile network, which provides fast and flexible access to the Internet, has greatly changed our daily life. It leads to the blossom of various mobile Apps and services, such as mobile streaming video, mobile electronic payment and so on. However, constrained to the current technologies, the energy-inefficient mobile data transmission often hinders people from fully enjoying modern mobile techniques. In order to address this issue, in this thesis, we aim to solve some tightly-related challenges to achieve energy-efficient mobile data transmission and thus, narrow down the gap between our daily life and fast evolving mobile technologies.

First, we study the energy issues of uncontrolled App network activities in mobile phones. As more and more applications being ins...[ Read more ]

The evolving mobile network, which provides fast and flexible access to the Internet, has greatly changed our daily life. It leads to the blossom of various mobile Apps and services, such as mobile streaming video, mobile electronic payment and so on. However, constrained to the current technologies, the energy-inefficient mobile data transmission often hinders people from fully enjoying modern mobile techniques. In order to address this issue, in this thesis, we aim to solve some tightly-related challenges to achieve energy-efficient mobile data transmission and thus, narrow down the gap between our daily life and fast evolving mobile technologies.

First, we study the energy issues of uncontrolled App network activities in mobile phones. As more and more applications being installed, their competition for network resources incurs serious problems to battery life and thus, degrades users’ normal experiences. To address this issue, we make comprehensive measurements on users’ habits and propose a novel approach to orchestrate network activities of smartphone applications, based on user’s habits. The proposed algorithm is proven to be 1−∈/2 competitive with the optimal solution. We have implemented the algorithm as a middleware service and it achieves over 70% energy savings in network activities.

Then we move our focus onto a more essential factor, the variation of WiFi link quality. Nowadays, offloading mobile traffic from cellular to WiFi is widely recognized as a viable solution to improve the energy efficiency on mobile devices. However, through extensive field experiments, we find that WiFi offloading is not always energy efficient and even consumes more energy than cellular network due to link quality variation. On the other hand, we observe from our past experiences that practical data transmission deadline requirement and link utilization allow scheduling of data traffic to time periods with good link quality. Accordingly, we propose Q-offload, the first attempt towards energy efficient WiFi offloading with link dynamics. In Q-offload, we propose an iterative framework to achieve energy efficient WiFi offloading by exploiting good link quality while not affecting user experience. The results from extensive experiments show that Q-offload can achieve 33.5%∼55.7% energy efficiency improvement, compared with state-of-the-arts under different conditions.

Enlightened by the results of our prior works, we raise our focus onto users’ lifestyles and this constitutes our third work. In this work, we propose a context-based link quality estimation scheme for achieving energy-efficient data transmission in WiFi/mobile networks. Through extensive real-world measurements, we exploit that link quality is highly relevant with users’ lifestyle and can be extracted as fingerprints. Following this observation, we propose an individual-oriented system, Furion, for exploiting beneficial WiFi/mobile links based on users’ contexts. In Furion, we introduce a context-based link quality discrimination scheme and design a more practical probabilistic model to predict the energy efficiency of links. As a result, the accuracy of link quality estimation is further improved given limited hardware on mobile devices and it can also be extended to different environments. The prototype of Furion is implemented on Android platform, and the results demonstrate that Furion achieves a significant performance improvement compared with the state-of-the-arts.