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Presenting a hybrid model based on the Internet of Things with an edge computing approach and drones with civilian applications for intelligent monitoring of industrial equipment performance (case study: oil pipelines) | ||
| Journal of New Researches in Mathematics | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 03 بهمن 1401 | ||
| نوع مقاله: research paper | ||
| شناسه دیجیتال (DOI): 10.30495/jnrm.2023.68978.2309 | ||
| نویسندگان | ||
elham aghazadeh1؛ Akbar Alemtabriz  2؛ Keyvan Shah Gholian1
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| 1Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran | ||
| 2Department of Management and Accounting, Shahid Beheshti University Tehran, Iran | ||
| چکیده | ||
| Today, in various industries, especially large industries, the monitoring of equipment performance is done by experts, and simultaneous and coordinated communication between different parts of an industrial environment is difficult due to the complexity of the operating environments and the multiplicity of equipment, and it causes large losses. Therefore, according to the extensive and innovative possibilities that today's technologies have provided to the industries, the control and monitoring of industrial equipment is done much more accurately and quickly by using smart devices in the context of the Internet of Things. In the topic of Internet of Things, cloud computing is mainly used to perform calculations on the internet platform, but with the emergence of "edge computing" computing technology to optimize cloud computing systems, researchers have become more inclined towards edge computing in recent years. Therefore, in this research, a hybrid model based on the Internet of Things with an edge computing approach and drones with civilian application was presented for intelligent monitoring of industrial equipment performance, which was investigated as a case study of oil pipelines. In this model, the performance of UAV for intelligent monitoring of oil pipelines was investigated in three stages. 1) Measuring the health of oil pipelines 2) UAV computing evacuation process 3) UAV local computing process. Since the final research model had two objectives, it was solved using two genetic methods with sparse sorting and the enhanced epsilon limit method using random numbers, and the genetic method with sparse sorting performed better; Because in problems with large dimensions, due to the complexity of the problem, the enhanced epsilon constraint method was not able to respond in a timely manner. | ||
| کلیدواژهها | ||
| IoT؛ UAV؛ Edge Computing؛ Smart Monitoring | ||
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آمار تعداد مشاهده مقاله: 106 |
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