The growth of the Internet of Things (IoT) has brought the opportunity to create applications in several areas, with the use of sensors and actuators. One of the problems encountered in IoT systems is the difficulty of adding semantic relations to the raw data produced by the sensors and being able to infer new facts from these relations. Moreover, due to the fact that many IoT applications are online and need to react instantly on sensor data collected by them, they need to be analyzed in real-time. Streams are a sequence of time-varying data elements that should not be stored forever and queried on demand. Streaming data needs to be consumed quickly through ongoing queries that continue to analyze and produce new relevant data, i.e. stream of output/result events. The ability to infer new semantic relationships over streaming data is called Stream Reasoning. We propose a semantic model and a mechanism for real-time data stream processing and reasoning based on Complex Event Processing (CEP), RDF (resource description structure) and OWL (Web Ontology Language). This work presents a middleware service that supports continuous reasoning on data produced by sensors. The main advantages of our approach are: (a) to consider time as a key relationship between information; (b) flow processing can be implemented using CEP; (c) is general enough to be applied to any data flow management system (DSMS). It was developed in the Advanced Collaboration Laboratory (LAC) and a case study in the field of fire detection is conducted and implemented, elucidating the use of real-time inference on streams.