293
Semantic Technologies for IoT
Follow the below steps to exploit the Smart Proxy toolset with the abovementioned
registrations:
• Ontology alignment task is carried out on the registrations as a whole, aligning classes LampType (label “Light”) and MotionDetectorSignalType (label
“Motion”) with app1:LightingDevice and app1:HasMovement and they are in
the ontology using subclass axioms, for example, LampType rdfs:subClassOf
app1:LightingDevice.
• Execute patterns of the iot:hasTarget-related SPARQL queries by performing the
matchmaking between the registered application and the smart entities.
• The message data format examples given with iot:hasTemplate are processed for
both the control entities’ services and the smart entity’s application by generating an
OWL ontology representation for a given XML or JSON so that “MotionDetected”
attribute in DetectionService’s output will get mapped with the “value“ XML tag
in the application LightService’s MovementDetectorResponse input.
13.7 Concluding Remarks
This chapter introduced key concepts in the semantic technology domain and key business applications in IoT. The chapter also touched upon emerging ontologies such as SSN
and IoT and the impact they will have on IoT smart solutions and future collaborative
interoperable applications. The chapter examines evolving standards and consortiums
that were instigated to advance standardization work and interoperability of IoT. Finally,
the chapter presents final remarks using a case study and guidelines to encourage readers
to innovate and build differentiated solution that contributes toward refining the quality
of lives and smart business practices in the domain of IoT.
References
Avancha, S., Patel, C. and Joshi, A., 2004. Ontology-driven adaptive sensor networks. In First Annual
International Conference on Mobile and Ubiquitous Systems, Networking and Services, 22–26 August,
Boston, MA (pp. 194–202).
Barnaghi, P., Wang, W., Henson, C. and Taylor, K., 2012. Semantics for the Internet of things: Early
progress and back to the future. International Journal on Semantic Web and Information Systems
(IJSWIS), 8(1), 1–21.
Berners-Lee, T., Hendler, J. and Lassila, O., 2001. The semantic web. Scientific American, 284(5), 28–37.
Brickley, D. and Miller, L., 2004. FOAF vocabulary specification. Namespace Document 2 Sept 2004,
FOAF Project, 2004. Available from: http://xmlns.com/foaf/spec/ updated on 2014.
Calder, M., Morris, R.A. and Peri, F., 2010. Machine reasoning about anomalous sensor data. Ecological
Informatics, 5(1), 9–18.
Eid, M., Liscano, R. and El Saddik, A., 2006, July. A novel ontology for sensor networks data. In
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and
Applications, 12–14 July, IEEE, Spain (pp. 75–79).
Semantic Technologies for IoT
Follow the below steps to exploit the Smart Proxy toolset with the abovementioned
registrations:
• Ontology alignment task is carried out on the registrations as a whole, aligning classes LampType (label “Light”) and MotionDetectorSignalType (label
“Motion”) with app1:LightingDevice and app1:HasMovement and they are in
the ontology using subclass axioms, for example, LampType rdfs:subClassOf
app1:LightingDevice.
• Execute patterns of the iot:hasTarget-related SPARQL queries by performing the
matchmaking between the registered application and the smart entities.
• The message data format examples given with iot:hasTemplate are processed for
both the control entities’ services and the smart entity’s application by generating an
OWL ontology representation for a given XML or JSON so that “MotionDetected”
attribute in DetectionService’s output will get mapped with the “value“ XML tag
in the application LightService’s MovementDetectorResponse input.
13.7 Concluding Remarks
This chapter introduced key concepts in the semantic technology domain and key business applications in IoT. The chapter also touched upon emerging ontologies such as SSN
and IoT and the impact they will have on IoT smart solutions and future collaborative
interoperable applications. The chapter examines evolving standards and consortiums
that were instigated to advance standardization work and interoperability of IoT. Finally,
the chapter presents final remarks using a case study and guidelines to encourage readers
to innovate and build differentiated solution that contributes toward refining the quality
of lives and smart business practices in the domain of IoT.
References
Avancha, S., Patel, C. and Joshi, A., 2004. Ontology-driven adaptive sensor networks. In First Annual
International Conference on Mobile and Ubiquitous Systems, Networking and Services, 22–26 August,
Boston, MA (pp. 194–202).
Barnaghi, P., Wang, W., Henson, C. and Taylor, K., 2012. Semantics for the Internet of things: Early
progress and back to the future. International Journal on Semantic Web and Information Systems
(IJSWIS), 8(1), 1–21.
Berners-Lee, T., Hendler, J. and Lassila, O., 2001. The semantic web. Scientific American, 284(5), 28–37.
Brickley, D. and Miller, L., 2004. FOAF vocabulary specification. Namespace Document 2 Sept 2004,
FOAF Project, 2004. Available from: http://xmlns.com/foaf/spec/ updated on 2014.
Calder, M., Morris, R.A. and Peri, F., 2010. Machine reasoning about anomalous sensor data. Ecological
Informatics, 5(1), 9–18.
Eid, M., Liscano, R. and El Saddik, A., 2006, July. A novel ontology for sensor networks data. In
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and
Applications, 12–14 July, IEEE, Spain (pp. 75–79).