الفهرس 
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Abstract
In the Internet of Things (IoT) paradithesgm, many things are con- nected to the Internet. Data integration enables combining data from various data sources in a standard format. It was relevant to numerous applications including enterprise information integration, medical in- formation management, geographical information systems, smart home, agriculture, industry, and E-Commerce applications. IoT applications used ontology approaches to provide a machine- understandable conceptualization of a domain. Ontology approaches have been adequately used in data integration applications because they provide an explicit and machine-understandable conceptualization of a domain. There are several approaches to the problem of data integration for the IoT. These approaches include ontologies like SOSA (sensor, ob- servation, sample, and actuation), the sensor network model (SSN), and Geospatial (GEO), which have been introduced into a partial schema. This thesis proposes a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This research proposes a middleware consisting of an ontology- based approach that collectes data from different devices and a neural- network-based approach that collectes captured images and video streams from cameras. The data were processed using a combination of ontolo- gies and semantic web technologies. The neural network approach fo- cused on detecting and recognizing Egyptian license plate recognition and was divided into three parts: license plate region location of image of
a car, license plate number, letters and numbers recognition, and stored the image of car and transaction date and recognition result in MYSQL database.
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