A New Cloud Resource Manufacturing System Based on the Internet of Things Using Optimized Deep Neural Network

Cloud Computing (CC) and the Internet of Things (IoT) are important in the modern era for providing new techniques of connection through intelligence perception, M2M, sharing efficient resources, and on-demand use. In a cloud environment, the manufacturing frame has numerous action tasks to perform or manufacture. Various Cloud Manufacturing system (CMfgs) resources influence some difficulties and attacks in manufacturing resources (work units, resource pools, workstations, and so on) and system modeling. To address these issues and meet the deadline, CMfgs proposed a novel Hybrid Grey Wolf-Ant Lion Regression Frame (HGW-ALRF) deep learning technique. In this case, the fitness function of the Ant Lion and Grey Wolf in the recurrent frame is used to effectively schedule cloud resources. During testing, the presented attacks (malware injection, DDoS, bottleneck attacks) can be monitored by the designed IoT application. Finally, the proposed model's obtained results are described, including computation time, accuracy, energy consumption, delay, and resource utilization.

• Publication year

  2023

• Venue

  2023 1st International Conference on Cognitive Computing and Engineering Education (ICCCEE)

• Publication date

  2023-04-27

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICCCEE55951.2023.10424547
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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