IoT Data Semantics and Ontology Sample Clauses

IoT Data Semantics and Ontology. ‌ IoT devices are very diverse, providing heterogeneous data, legacy protocols, different data formatting and therefore any low-level access to the devices and data would prove to be very inefficient if not impossible task. Therefore, it is necessary to provide an abstraction layer capable to offer data (or resource) access using the semantic information model. The semantic and ontology can be applied to the well-defined and structured IoT information model. This model should detail various concepts and provide abstractions of the components and their attributes. One of the main goals of IoT is to extend the Internet into the physical world and from this perspective the information model defines a physical entity within the ambient environment. Different “things” (such as a human, an animal, a car, a store, a logistic chain item, an electronic appliance or a closed or an open environment) constitute entities which are the main focus of interactions. These interactions are made possible by a hardware component, a ‘device’, which either attaches to an entity or is a part of the environment of an entity so it can monitor it. The device allows the entity to be a part of the digital world by mediating the interactions. The actual software component that provides information on the entity or enables controlling of the device, is a ‘resource’ [32]. This model is depicted in Figure 11. resource models when dealing with semantics and ontology such as Web Ontology Language (OWL) [33]. It is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL provides three languages which are used depending on the required flexibility, namely OWL Lite, OWL DL and OWL Full. For example, the entity model applied in the IoT-A project [2] is built upon the OWL DL language which is providing maximum expressiveness while retaining computational completeness [33]. There are also other representation models such as OntoSensor [34], service-oriented ontology [35] and SensorData ontology [36] each with their strengths and weaknesses such as not being able to represent sensor data semantically. One of the approaches that has received consensus from the community is “Sensing as a Service” model which represents a scalable way to access the sensor data through standard service technologies. In other words, this approach represents resource and service ontology model supporting search and discovery mechanisms which...