Semantic Interoperability and Reusability in IoT: a Systematic Mapping Study

Internet of Things (IoT) enables different devices, sensors, or humans to connect via the Internet. However, various IoT devices generate heterogeneous data in different formats. This hinders IoT devices from integrating and exchanging data between them. Adding semantic web technologies to the Web of Things (WoT) has greatly enabled IoT devices to be semantically interoperable. Moreover, W3C has established a Semantic Sensor Network (SSN) ontology that can be reused in different IoT applications due to difficulties in developing new ontologies. Many systematic mapping reviews in the literature have addressed semantic interoperability in IoT. Although reusability can play a vital role in enhancing semantic interoperability, none of those studies has discussed reusability and semantic interoperability together in the IoT area. In this article, we are seeking to fill this gap in the current literature by conducting a systematic mapping review for 72 articles to point out semantic interoperability as well as reusability in IoT. Five research questions have been identified regarding challenges and possible solutions about both semantic interoperability and reusability. The reviewed articles are classified into four categories. There are 47 articles about semantic interoperability, 2 of which discuss reusability, 18 of which include semantic interoperability with reusability, and the last category includes surveys. Moreover, the research questions are also assigned to the related category that answers the questions. This article highlights important insights about semantic web techniques namely, RDF, ontology, SPARQL, and OWL. Additionally, this article concludes how these techniques are enhanced in diverse domains such as healthcare, smart cities, and the energy domain. On top of that, this systematic mapping shows how reused ontology plays a remarkable role in the IoT domain Finally, results that answer the research questions are figured out and deeply analyzed in the tables and graphs.