On the Evaluation of Proactive Location-Based Services

During the last years, location-based services (LBS) became one of the most popular features of mobile devices like smartphones or tablets. Basically, reactive LBS provide means to access location-specific content upon request by the user, whereas their proactive counterparts are used to push location-specific content to the user as soon as a dedicated zone - called geofence - is entered or left. The fields of application for proactive LBS are manifold, ranging from location-based reminding over location-based citizen participation to location-based marketing. However, evaluating and comparing proactive LBS regarding their performance and scalability are still cumbersome tasks, involving resource-intensive field trials and in-depth investigations of their underlying architectures. This paper identifies domain- and architecture-independent attributes of proactive LBS that are well-suited to characterize a proactive LBS regarding these non-functional properties. It also takes a deeper look into the key parameters which are mainly affecting the performance and scalability in the real world. While putting the focus on these key parameters and attributes, it is proposed to simulate a proactive LBS within an in-lab environment prior to its evaluation in the wild. For this purpose, a prototypical in-lab evaluation framework for proactive LBS is introduced which generates simulation-based reports regarding the functionality, performance and scalability of a proactive LBS in general.