Abstract
As the number of connected devices is exponentially growing, the IoT community is investigating potential ways of overcoming the resulting heterogeneity to enable device compatibility, interoperability and integration. The Semantic Web technologies, frequently used to address these issues, have been employed to develop a number of ontological frameworks, aiming to provide a common vocabulary of terms for the IoT domain. Defined in Web Ontology Language – a language based on the Description Logics, and thus equipped with the ‘off-the-shelf’ support for formal reasoning – these ontologies, however, seem to neglect the built-in automated reasoning capabilities. Accordingly, this paper discusses the possibility of leveraging this idle potential for automated analysis in the context of defining and enforcing policies for the IoT. As a first step towards a proof of concept, the paper focuses on a simple use case and, using the existing IoT-Lite ontology, demonstrates different types of semantic classification to enable policy enforcement. As a result, it becomes possible to detect a critical situation, when a dangerous temperature threshold has been exceeded. With the proposed approach, IoT practitioners are offered an already existing, reliable and optimised policy enforcement mechanism. Moreover, they are also expected to benefit from support for policy governance, separation of concerns, a declarative approach to knowledge engineering, and an extensible architecture.
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Notes
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In this example, England and the United Kingdom are uniquely represented by their respective Wikipedia URLs.
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The notations ssn, iot, and dul are established shortcuts for imported OWL ontologies, where corresponding concepts are defined.
SSN ontology (ssn): http://purl.oclc.org/NET/ssnx/ssn
IoT-Lite ontology (iot): http://purl.oclc.org/NET/UNIS/fiware/iot-lite
DOLCE Upper Level Ontology (dul): http://www.loa.istc.cnr.it/ontologies/DOLCE-Lite.owl.
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Please note that there are two main reasoning methods, which define the order, in which axioms are considered for evaluation – namely, forward and backward chaining [21]. In the presented use case, forward chaining is assumed to be in place.
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Acknowledgements
The work presented in this paper was partially supported by the ERASMUS+ Key Action 2 (Strategic Partnership) project IOT-OPEN.EU (Innovative Open Education on IoT: improving higher education for European digital global competitiveness), reference no. 2016-1-PL01-KA203-026471. The European Commission support for the production of this publication does not constitute endorsement of the contents which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.
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Dautov, R., Veloudis, S., Paraskakis, I., Distefano, S. (2017). Policy Management and Enforcement Using OWL and SWRL for the Internet of Things. In: Puliafito, A., Bruneo, D., Distefano, S., Longo, F. (eds) Ad-hoc, Mobile, and Wireless Networks. ADHOC-NOW 2017. Lecture Notes in Computer Science(), vol 10517. Springer, Cham. https://doi.org/10.1007/978-3-319-67910-5_28
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