Abstract:
The rapid advancement of technology and the proliferation of Internet of Things (IoT) devices have paved the way for the development of smart cities. Smart cities leverage IoT concepts to collect and analyze vast amounts of data from various sources in real-time to improve the quality of life for their residents. However, processing and analyzing this massive data stream in real-time pose significant challenges.
This paper proposes a novel real-time data stream processing model for a smart city application that harnesses the power of intelligent IoT. The model integrates advanced data processing techniques, such as stream processing and machine learning, to enable efficient and effective data analysis in real-time.
The proposed model consists of three main components: data ingestion, data processing, and data analytics. In the data ingestion phase, data is collected from a wide range of IoT devices deployed across the city, including sensors, cameras, and smart infrastructure. The data is then transmitted to a centralized processing unit for further analysis.
The data processing component employs stream processing techniques to handle the continuous and high-velocity data stream. It applies filtering, aggregation, and transformation operations to preprocess the data and extract relevant information. Additionally, machine learning algorithms are utilized to perform real-time anomaly detection, predictive analytics, and decision-making based on the processed data.
The data analytics component leverages the insights gained from real-time data processing to enable various smart city applications. These applications include traffic management, environmental monitoring, energy optimization, public safety, and urban planning. The real-time nature of the model ensures that actionable insights are derived promptly, enabling timely response and intervention.
The proposed model is evaluated through a proof-of-concept implementation in a simulated smart city environment. The results demonstrate the model’s effectiveness in processing and analyzing real-time data streams, as well as its ability to provide valuable insights for decision-making in a smart city context.
In conclusion, this paper presents a real-time data stream processing model that leverages intelligent IoT concepts for smart city applications. The model addresses the challenges associated with processing and analyzing vast amounts of real-time data, enabling the development of efficient and effective smart city solutions. The proposed model has the potential to revolutionize urban management by facilitating data-driven decision-making, improving resource allocation, and enhancing the overall quality of life in smart cities.
A REAL TIME DATA STREAM PROCESSING MODEL FOR A SMART CITY APPLICATION LEVERAGING INTELLIGENT INTERNET OF THINGS (IOT) CONCEPT. GET MOREÂ Â COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
