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Mobile Information Systems
Volume 2017, Article ID 1856329, 9 pages
Research Article

Offloading Method for Efficient Use of Local Computational Resources in Mobile Location-Based Services Using Clouds

1Department of Computer Science and Engineering, Dongguk University, 3-26 Pil-dong, Jung-gu, Seoul 100-715, Republic of Korea
2Department of Computer Engineering, Seokyeong University, 16-1 Jungneung-dong, Sungbuk-ku, Seoul 136-704, Republic of Korea

Correspondence should be addressed to Yangsun Lee;

Received 9 December 2016; Revised 6 February 2017; Accepted 20 February 2017; Published 14 March 2017

Academic Editor: Subramaniam Ganesan

Copyright © 2017 Yunsik Son and Yangsun Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of mobile computing, location-based services (LBSs) have been developed to provide services based on location information through communication networks or the global positioning system. In recent years, LBSs have evolved into smart LBSs, which provide many services using only location information. These include basic services such as traffic, logistic, and entertainment services. However, a smart LBS may require relatively complicated operations, which may not be effectively performed by the mobile computing system. To overcome this problem, a computation offloading technique can be used to perform certain tasks on mobile devices in cloud and fog environments. Furthermore, mobile platforms exist that provide smart LBSs. The smart cross-platform is a solution based on a virtual machine (VM) that enables compatibility of content in various mobile and smart device environments. However, owing to the nature of the VM-based execution method, the execution performance is degraded compared to that of the native execution method. In this paper, we introduce a computation offloading technique that utilizes fog computing to improve the performance of VMs running on mobile devices. We applied the proposed method to smart devices with a smart VM (SVM) and HTML5 SVM to compare their performances.