Abstract
To facilitate the efficient use of information generated from the Internet of Things (IoT) by sensors and automatically drive entities to meet the needs of human beings, this paper presents an information processing framework using semantic web technologies. The framework represents the entities as semantic web services and automatically creates service sequences as transactions by analyzing the information need in specific queries. Moreover, the framework ensures the effective management and control of information collected by mobile sensors during the transaction. The foundation of this framework lies in a set of ontologies we proposed: goal ontology, role ontology, constraint ontology, message ontology, status ontology, space-time ontology, and activity ontology. In addition to their respective definitions, we also present a detailed exploration of the relationships between them. Finally, we validate the effectiveness of the framework and the ontologies with a case study of the logistics system. The evaluation shows that the ontologies support the framework well and our approach increases the logistics system efficiency with lower operating costs.
Highlights
The Internet of Things (IoT) vision [1] promotes the ubiquitous computing paradigm on a global scale
With the development of the semantic web [3], the use of ontologies is becoming common as a formalism to describe knowledge and information in a way that can be shared on the web is becoming common
Ontologies currently have been widely used in knowledge engineering (KE) and artificial intelligence (AI) to structure the concepts of a domain
Summary
The Internet of Things (IoT) vision [1] promotes the ubiquitous computing paradigm on a global scale. In ubiquitous and pervasive contexts, intelligence is embedded into objects and physical locations by means of a relatively large number of heterogeneous microdevices; for example, sensors are used in applications ranging from meteorology to medical care, environmental monitoring and security, and surveillance. This increase is accompanied by an increasing volume of data, as well as increasing heterogeneity of devices, data formats, and measurement procedures. Ontologies currently have been widely used in knowledge engineering (KE) and artificial intelligence (AI) to structure the concepts of a domain. Ontology and the individual instances of the classes constitute a knowledge base
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