THESIS
2017
xii, 75 pages : illustrations ; 30 cm
Abstract
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...[
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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.
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