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Efficient Implementation of Classification of Air-Polluting Cars in Apache Storm

Ghasemi, Reza | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52316 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Goudarzi, Maziar
  7. Abstract:
  8. Today, due to the increasing use of fossil fuel-powered vehicles, attention has been paid to advanced societies by the amount of pollutants emitted by these vehicles and the need to control and reduce them. As the number of cars increases, the volume of data for processing increases, which increases the need for data centers and processing infrastructure with more computing power. Therefore, in this research, we intend to implement a program that continuously categorizes cars according to specific criteria in terms of emission levels. For this purpose we used Apache Storm, a data stream processing framework, to implement the program.In this research, we develop a simulation for cars with the aim of simulating their emission rates in which cars have different emission rates at different times. In this research is a suitable Storm program developed for the above application, which according to the data sent from the simulator, puts the cars in different categories according to their emission levels. This topology should be implemented using appropriate Storm scheduling to meet the instantaneous and variable throughput required.The main purpose of this study was to evaluate and determine the minimum processing power for vehicle classification according to the exhaust emission rate of vehicles. In this project, we able to increase throughput by up to 362% for topology, and based on the existing processing system, a topology was able to process, categorize, and record data 1450 vehicle every second
  9. Keywords:
  10. Apache Storm ; Stream Data Processing ; Internet of Things ; Pollution Monitoring ; Cloud Computing

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