Energy-as-a-Service (EaaS): On the Efficacy of Multimedia Cloud Computing to Save Smartphone Energy

Chen

2014

Wireless Pers Commun

160

As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.

Section: Mobile Multimedia-as-a-servicementioning

confidence: 99%

Section: Code/computation Offloadingmentioning

confidence: 99%

A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges

Chen

2014

Wireless Pers Commun

160

IEEE Commun. Surv. Tutorials

“…Such a solution, proposed by Zhao et al [163] was shown to reduce the energy consumed during web browsing by more than 45%, along with reducing the delay by more than 80%. Altamimi et al [164], as opposed have showed that using a Mobile Cloud Computing service to encode videos to a format supported by the mobile device, can save as much as 70% of the energy required to encode the clip locally on the device.…”

Section: ) Content Delivery-related Solutionsmentioning

confidence: 99%

Energy-Aware Mobile Learning:Opportunities and Challenges

Moldovan

Weibelzahl

Muntean

2014

Abstract-As mobile devices are becoming more powerful and affordable they are increasingly used for mobile learning activities. By enabling learners' access to educational content anywhere and anytime, mobile learning has both the potential to provide online learners with new opportunities, and to reach less privileged categories of learners that lack access to traditional e-learning services. Among the many challenges with mobile learning, the battery-powered nature of mobile devices and in particular their limited battery life, stands out as one issue that can significantly limit learners' access to educational content while on the move. Adaptation and personalisation solutions have widely been considered for overcoming the differences between learners and between the characteristics of their mobile devices. However, while various energy saving solutions have been proposed in order to provide mobile users with extended device usage time, the areas of adaptive mobile learning and energy conservation in wireless communications failed to meet under the same umbrella. This paper bridges the two areas by presenting an overview of adaptive mobile learning systems as well as how these can be extended to make them energy-aware. Furthermore, the paper surveys various approaches for energy measurement, modelling and adaptation, three major aspects that have to be considered in order to deploy energy-aware mobile learning systems. Discussions on the applicability and limitations of these approaches for mobile learning are also provided.

“…Consequently, they consume much energy and drain smartphone battery very quickly [4]. An investigation has proved the feasibility of Multimedia Cloud Computing to provide the Energy-asa-Service (EaaS), so that can reduce the local processing [5]. But the overhead of large data transfer has not been solved yet.…”

Section: Introductionmentioning

confidence: 99%

A Method of Power Aware Large Data Download on Smartphone

Ren

Gao

IEICE Trans. Inf. ^|^ Syst.

et al. 2015

SUMMARYThe endurance time of smartphone still suffer from the limited battery capacity, and smartphone apps will increase the burden of the battery if they download large data over slow network. So how to manage the download tasks is an important work. To this end we propose a smartphone download strategy with low energy consumption which called CLSA (Concentrated Download and Low Power and Stable Link Selection Algorithm). The CLSA is intended to reduce the overhead of large data downloads by appropriate delay for the smartphone, and it based on three major factors: the current network situation, the length of download requests' queue and the local information of smartphone. We evaluate the CLSA using a music player implementation on ZTE V880 smartphone running the Android operation system, and compare it with the other two general download strategies, Minimum Delay and WiFi Only. Experiments show that our download algorithm can achieve a better trade-off between energy and delay than the other two.