Abstract:
The emergence of smart city initiatives has led to the integration of advanced technologies, such as the Internet of Things (IoT), to improve the quality of life for citizens. However, the massive amounts of data generated by IoT devices in a smart city pose significant challenges in terms of real-time processing and analysis. This abstract presents a novel data stream processing model that leverages intelligent IoT concepts to enable efficient and timely decision-making in a smart city environment.
The proposed model combines real-time data stream processing techniques with intelligent IoT concepts to address the unique requirements of smart city applications. The model incorporates intelligent sensors, actuators, and edge computing capabilities to capture and process data streams in real-time. It employs machine learning algorithms and artificial intelligence techniques to extract meaningful insights from the data, enabling proactive decision-making and providing valuable services to citizens.
The key components of the model include data acquisition, data preprocessing, real-time stream processing, and intelligent analytics. Data acquisition involves collecting data from various IoT devices deployed across the city, such as smart meters, environmental sensors, and surveillance cameras. The acquired data undergoes preprocessing to filter noise, handle missing values, and ensure data quality.
Real-time stream processing is performed using scalable and distributed processing frameworks, enabling the efficient analysis of data streams as they arrive. This allows for timely detection of critical events or anomalies, such as traffic congestions, air pollution spikes, or security breaches. The processed data is then fed into intelligent analytics modules, which employ machine learning algorithms and AI techniques to extract valuable insights, predict future trends, and optimize resource allocation in the smart city.
The proposed model offers several advantages over traditional batch processing approaches. By processing data streams in real-time, it enables immediate response to changing conditions, facilitating timely interventions and improved city operations. Moreover, the integration of intelligent IoT concepts enhances the model’s ability to adapt and learn from the data, enabling proactive decision-making and personalized services tailored to citizens’ needs.
To evaluate the effectiveness of the proposed model, a prototype implementation was developed and tested in a real-world smart city environment. The results demonstrate the model’s capability to handle large-scale data streams, achieve low-latency processing, and provide valuable insights for decision-makers.
In conclusion, the real-time data stream processing model presented in this abstract offers a promising approach to leverage intelligent IoT concepts for efficient and timely decision-making in smart cities. By combining real-time processing, intelligent analytics, and IoT technologies, the model enables proactive interventions, optimized resource allocation, and improved services for citizens, ultimately contributing to the development of smarter and more sustainable cities
AN IMPLEMENTATION OF A REAL-TIME DATA STREAM PROCESSING MODEL FOR A SMART CITY APPLICATION LEVERAGING INTELLIGENT INTERNET OF THINGS (IOT) CONCEPTS. GET MORE COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
