On the Evaluation of a Contextual Sensitive Data Offloading Service: the COP case

Abstract

Mobile and context-aware applications are now a reality thanks to the increased capabilities of mobile devices. Frequently, this kind of software characterizes user's situation as well as their profiles to adapt themselves (interfaces, services, content) according to user's contextual data. In the last twenty years, researchers had proposed several software infrastructures to help the development of context-aware applications. However, we verified that most of them do not store contextual data history because researchers have considered mobile devices as resource-constrained devices. Also, few of these infrastructures take into account the privacy of contextual data due to the fact those applications may expose contextual data without user's consent. This article presents a service named COP (Contextual data Offloading service with Privacy support) to mitigate these problems. Its foundations are: (i) a context model; (ii) a privacy model; and (iii) a list of synchronization policies. The COP aims at storing and processing the contextual data generated from several mobile devices, using the computational power of the cloud. We have implemented two performance evaluation experiments of COP. The first experiment evaluated the impact of contextual filter processing in the mobile device and the remote environment. In this experiment, we measured the processing time and the energy consumption of COP approach. The analysis detected that the migration of data from mobile device to a remote environment is advantageous. The second experiment evaluated the energy consumption to send contextual data. The analysis detected that sending contextual data periodically is the best way to save energy.