Energy consumption model for mobile wireless communication

Marcu, Marius; Tudor, Dacian

doi:10.1145/2069131.2069168

Cited by 5 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mittal et al [47] present an energy emulation tool that allows developers to estimate the energy consumption of a mobile app during its development. Marcu and Tudor [45] also explores this aspect to measure the energy consumption with a special device emulator. On the contrary, we based our experiments on a popular android device, the Galaxy Nexus.…”

Section: Related Workmentioning

confidence: 99%

Investigating the energy impact of Android smells

Carette

Younes

Hecht

et al. 2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)

View full text Add to dashboard Cite

Android code smells are bad implementation practices within Android applications (or apps) that may lead to poor software quality. These code smells are known to degrade the performance of apps and to have an impact on energy consumption. However, few studies have assessed the positive impact on energy consumption when correcting code smells. In this paper, we therefore propose a tooled and reproducible approach, called HOT-PEPPER, to automatically correct code smells and evaluate their impact on energy consumption. Currently, HOT-PEPPER is able to automatically correct three types of Android-specific code smells: Internal Getter/Setter, Member Ignoring Method, and HashMap Usage. HOT-PEPPER derives four versions of the apps by correcting each detected smell independently, and all of them at once. HOT-PEPPER is able to report on the energy consumption of each app version with a single user scenario test. Our empirical study on five open-source Android apps shows that correcting the three aforementioned Android code smells effectively and significantly reduces the energy consumption of apps. In particular, we observed a global reduction in energy consumption by 4,83% in one app when the three code smells are corrected. We also take advantage of the flexibility of HOT-PEPPER to investigate the impact of three picture smells (bad picture format, compression, and bitmap format) in sample apps. We observed that the usage of optimised JPG pictures with the Android default bitmap format is the most energy efficient combination in Android apps. We believe that developers can benefit from our approach and results to guide their refactoring, and thus improve the energy consumption of their mobile apps.

show abstract

Section: Related Workmentioning

confidence: 99%

Investigating the energy impact of Android smells

Carette

Younes

Hecht

et al. 2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)

View full text Add to dashboard Cite

show abstract

Energy-Aware Mobile Learning:Opportunities and Challenges

Moldovan

Weibelzahl

Muntean

2014

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

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.

show abstract