"The Internet of Things & Big Data"
In recent years, the development of IoT (Internet of Things) has been marked by significant advancements in various areas. When it comes to healthcare, edge computing provides flexible and collaborative communication, computation, and storage services over heterogeneous devices. A number of IoT services, such as computation resources and storage capabilities, have revolutionized various domains of human life through cloud computing. Similarly, emergency and health monitoring services can be negatively impacted by issues such as low latency and delays in transferring data to and from the cloud. Healthcare applications generate large volumes of data that often need to be stored in the cloud due to limited computing resources and storage devices. The storage and retrieval of this data is crucial for effective healthcare diagnosis.
In term of ITS (intelligent transportation system), real-time information regarding traffic scenarios, vehicle movements and environmental variables must be gathered from sensors installed in ITS. In order to collect data from multiple sources inside the transport system, sensors are essential. The data collected from these sensors are processed and served as inputs to the AI algorithms which in turn results in smart applications and services in ITS for a smart city. The data collection and analysis in ITS is enabled via either a IoT based, or Cloud based big data architecture.
Closed-Circuit Television (CCTV) Cameras: Roadways, crossroads, and other important locations are all captured on real-time video by these cameras. They offer visual information for tracking traffic, spotting accidents, and assessing road conditions. Automatic Number Plate Recognition (ANPR) Cameras: ANPR cameras record photos of licence plates, enabling automatic vehicle recognition and tracking. They give information for toll collection, vehicle recognition, and enforcement of traffic laws.
The image and video data collected from the cameras fixed on roads and parking lots greatly help in vehicle detection, number plate recognition, driver’s driving pattern identification, incident detection which in turns result in smart traffic management with accurate analysis to transportation demand.
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S. Abirami, M. Pethuraj, M. Uthayakumar and P. Chitra (2024). A systematic survey on big data and artificial intelligence algorithms for intelligent transportation system. Case Studies on Transport Policy, 17, pp.101247–101247. doi:https://doi.org/10.1016/j.cstp.2024.101247