الفهرس 
A framework for semantic integration of heterogeneous sensor networks
ContentsOnly 14 pages are availabe for public view
 |  | from | 122 |  |  |
| | | from | 122 | | |
Abstract
Today, sensors are largely embedded in almost everyday life objects and will continue to do so in the upcoming years. Sensor-enabled devices together play a vital role in bridging the gap between physical and cyber worlds. Currently a group of sensor-enabled devices in a given setup is designed to be merely a source of data for the speci{uFB01}c applications they are developed for. The devices in such setups are mostly homogeneous and a speci{uFB01}c intended application is typically the sole user of data. The application developers must have a previous knowledge of the involved devices features in order to use their data. The ability to use sensor data integrated from multiple heterogeneous sources regardless of their underlying technical details is vital to be able to build real Internet of Things (IoT) applications. To this end, this thesis proposes a framework for Semantic InteGration of HeTerogeneous SEnsor Data (SIGHTED), a conceptual framework that exploits semantic Web technologies. It provides a layered structure as a reference for heterogeneous sensor data integration. Additionally, SIGHTED framework is instantiated to develop the DotThing platform. DotThing demonstrates the functionalities of SIGHTED layers. The DotThing platform makes use of vocabularies from existing known ontologies to provide a uni{uFB01}ed model to annotate and link data. DotThing provides horizontal meaningful data integration and discovery capabilities compared with existing platforms such as Xively. DotThing deals practically with multiple heterogeneous sources compared with single source in SemSense system that lacks system performance evaluation. It adds semantics to both sensor features and readings unlike sense2Web platfrom that focuses on sensor features