OAISIS: An ontological-based approach for interlinking CrowdSensing information systems

Nowadays, smartphones and wearable devices are endowed with several sensors, which can harvest large quantities of data about urban areas (location information, pollution levels, etc.), going through a list of personal and surrounding contexts such as noise level, traffic awareness, to name a few. Exploiting this wealth of information provided by the crowd allows developers to design and build several applications over the so-called Mobile CrowdSensing, such as traffic regulation, environmental monitoring, tourism recommendation, etc. However, for this to be possible, many barriers still have to be overcome such as collecting, handling, structuring and representing the crowd data in a suitable way. In this paper, we propose a three-fold solution to the problem of data management in CrowdSensing systems. Firstly, we structure the collected data based on semantic ontologies. Secondly, we enrich the data based on a novel contextual awareness data interlinking. Finally, we refine recommendations with contexts, through taking into consideration meta-information interlinked to the main information of interest. We have implemented our model in a tourism recommendation application as a proof of concept. The experimental evaluation - which we carried out - has shown very promising results.