A REAL-TIME DATA STREAM PROCESSING MODEL FOR A SMART TRAFFIC APPLICATION, LEVERAGING INTELLIGENT INTERNET OF THINGS (IOT) CONCEPTS
Abstract
Smart City of Smart systems is becoming ubiquitous. Improvements in miniaturization and networking capabilities of sensors have contributed to the proliferation of the Internet of Things (IoT) and continuous sensing environments. Data streams generated in such settings must keep pace with generation rates and be processed in real-time to gain insight quickly and make decisions that are in most cases critical and time-sensitive. The challenge lies in not only being able to process vast amounts of data in a given time but also being able to make data-driven decisions quickly or in many cases proactively. Handling the amounts of data generated could be very difficult especially when making data-driven decisions. The difficulty is being diminished using some big data methods to perform real-time stream processing. Among the different dimensions that improve the quality of life of people in a smart city environment, one of the important ones is transportation. In this work, a real-time data stream processing model for a smart traffic application was proposed and data streaming trends were used to monitor traffic which enables people to know if the roads in an IoT environment are congested
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