A Real-Time Data Stream Processing Model for a Smart City Application Leveraging Intelligent Internet of Things (IOT) Concepts
Due to the vast amount of data that is being generated by the sensors through the smart devices in smart cities, streams of data must be processed in real time to gain insight quickly and to make decisions that are in most cases critical and time sensitive. The difficulty is diminished by using big data methods such as Cassandra, Hadoop, Kafka and Spark to perform real-time stream processing in an Internet of Things (IoT) environment, such as traffic monitoring in a smart city environment. Among the different dimensions that improve the quality of life of people in a smart city, one of the very important one is transportation. Intelligent Traffic Monitoring System (ITMS) in a smart city, monitors traffic by detecting and displaying what is occurring on a particular road. In this thesis, a real-time data stream processing model was developed and used data streaming trends to monitor traffic in an ITMS.