Semantic models for IoT sensing to infer environment–wellness relationships

Every time an Internet of Things (IoT) solution is deployed, every time a smartphone owner connects her/his wireless device to a wearable activity-tracker, every time groups of citizens use geo-mapping applications to move around the city, choosing the least crowded path, data are produced and information have to be exchanged appropriately via APIs. Even if novel added-value IoT-based applications appear on the market with increasing speed, true semantic interoperability is far from being achieved, thus limiting the large-scale exploitation, the scalability and the time-to-market of novel apps. Currently, connecting different data prosumers with multiple data sources is still hampered by the lack of standardized and sustainable solutions, especially due to the significant heterogeneity of IoT platforms. In such a landscape, ontologies come to the rescue, thanks to their formal semantics, knowledge representation formats, and shared vocabularies. In this paper we examine, from an ontological perspective, how to describe environmental sensing and wellness monitoring, two of the most popular application cases of Mobile Crowd Sensing (MCS) and IoT, respectively. To this purpose, an ontology of sensor-agnostic APIs is proposed, along with a set of MCS-dedicated ontology modules (and the supporting platform), leveraging on standard and reusable domain ontologies. Moreover, it will be shown how to properly combine the proposed ontologies in order to support complex functionalities based on inference rules addressing the environment–wellness relationships. Finally, specific semantic modeling patterns suitable for typical IoT and MCS scenarios will be discussed.