Um serviço de offloading de dados contextuais com suporte à privacidade

Abstract

Mobile devices became a common tool in our daily routine. Mobile applications are demanding access to contextual information increasingly. For instance, applications require user’s environment data as well as their profiles in order to adapt themselves (interfaces, services, content) according to this context data. Mobile applications with this behavior are known as context-aware applications. Several software infrastructures have been created to help the development of this applications. However, it was verified that most of them do not store history of the contextual data, since mobile devices are resource constrained. They are not built taking into account the privacy of contextual data either, due the fact that applications may expose contextual data without user consent. This dissertation addresses these topics by extending an existing middleware platform that help the development of mobile context-aware applications. This work present a service named COP (Contextual data Offloading service with Privacy Support) and is based in: (i) a context model, (ii) a privacy policy and (iii) synchronization policies. The COP aims to store and process the contextual data generated from several mobile devices, using the computational power of the cloud. To evaluate this work we developed an application that uses both the migration and the privacy mechanism of the contextual data of the COP. Other two experiments were made. The first experiment evaluated the impact of contextual filter processing in mobile device and remote environment, in which the processing time and energy consumption were measured. In this experiment was possible to conclude 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.