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Improving Energy-Efficiency of Location-Triggered Applications On Smartphones
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Numerous Location-Based Applications (LBAs) have been emerging in our smartphones over the past several years. Most of them are user-triggered LBAs which heavily depend on manual operations of end users, thereforeĀ  easy to lose their accessibility and usability when fail to get instructions in time. While periodic duty cycling of location sensing can guarantee the accuracy of the location and reliability of the desired triggered events, this pattern brings severe battery draining problem caused by power-intensive sensor, let alone redundant high-energy-consumption sensing operation from multiple LBAs without coordination.

In contrast, location-triggered applications take the responsibility to manage the location sensing intelligently. They bring betterĀ  user experiences by performing actions automatically when reaching predefined locations. This paper proposes adaptive approaches to design energy-efficient location-triggered applications and further models the middleware to build integrated application framework for multiple location-triggered applications in general for energy concern.

In the evaluation, business-independent location-triggered applications are implemented in the unified application framework and tested under various scenarios. The results have verified improved usability, higher customization capability and energy efficiency validity of the location-triggered applications and underlying application framework. The establishment of the unified location-triggered application framework provides the reference model for general intelligent energy-efficient LBA development in the future, while broadening the actual use of location-based service for mobile cloud computing.

Place, publisher, year, edition, pages
IT, 13 038
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-201921OAI: oai:DiVA.org:uu-201921DiVA: diva2:630023
Educational program
Master Programme in Computer Science
Available from: 2013-06-18 Created: 2013-06-18 Last updated: 2013-06-18Bibliographically approved

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