Indoor navigation has become more important these days due to the current situation worldwide in the aftermath of the outbreak of the COVID-19 pandemic, posing an unparalleled threat amounting to a humanitarian crisis on a global scale. Indoor navigation employs a variety of technologies, including Wi-Fi, Bluetooth, and RFID. Support for these technologies requires accurate information and appropriate processing and modeling to help and direct users of the optimal route to desired destinations and to monitor crowd density in order to maintain social distancing. This research will present a semantic indoor ontology model for indoor navigation and the reduction of human density in indoor space to ensure social distancing and prevent transmission. The proposed system is based on semantic representations of the components of navigation paths which, in turn, enable reasoning functionality. Despite the system’s complexity, the evaluation revealed that it functions well.
Semantic-Linked Data Ontologies for Indoor Navigation System in Response to COVID-19