Turkish Journal of Electrical Engineering and Computer Sciences




Recently, the use of sensor-based systems in many areas has led to an exponential increase in the raw sensor data. However, the lack of neither syntactic nor semantic integrity between these sensor data limited their sharing, reusability, and interpretation. These inabilities can cause some problems. For example, different wireless sensor networks may not work together due to the subtle variations in their sensing methods, operating systems, syntax, and data structure. In recent years, to cope with these inabilities, the semantic sensor web approach, which enables us to enrich the meaning of sensor data, has been seen as the critical technology in solving these problems by some researchers. The primary purpose of this study is to create a laboratory environment parameters sensor ontology (LEPSO) that provides a standard data model for heterogeneous sensor data from different platforms by expanding semantic sensor networks (SSN). A case study was conducted using the real-time data collected from Bolu Abant İzzet Baysal University, Scientific Industrial Technological Application and Research Center in order to demonstrate that the proposed LEPSO can be used in similar sensor-based applications. A series of semantic queries have been performed on the collected sensor data to evaluate the proposed sensor ontology. The results showed that sensor data, which are heterogeneous by nature, provide benefit results in sensor-based monitoring systems when enriched with semantic web technologies and ontologies. Besides, this study proves that the proposed semantic sensor ontology, which used the semantic sensor network framework, has the capability to provide a common infrastructure for many sensor-based applications. The proposed ontology has the potential to become a more comprehensive ontology by adding different platforms, different sensors, different environments such as school, factory. In the next study, it is aimed to expand the scope of this semantic sensor network, which is formed by including this ontology in the intensive care unit of a hospital.


Semantic sensor network, ontology modeling, stream data, real-time monitoring, heterogeneous sensor data

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