Design and Explanation of a Hybrid IoT and UAV Model for Intelligent Monitoring of Industrial Equipment Performance with Edge Computing Approach (Case Study: Wind Turbines)

Document Type : Original Article

Authors

1 Ph.D Student, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Tehran, Iran.

2 Professor, Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

3 Assistant Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Tehran, Iran.

Abstract

Investment in industrial equipment is a necessity for every industrial unit. In this study, a mathematical model for intelligent monitoring of the performance of industrial equipment of wind turbines using the Internet of Things and unmanned aerial vehicles (UAVs) with an edge computing approach is investigated. In this model, the performance of the UAV for intelligent monitoring of wind turbines is investigated in three stages: detection, computational offloading, and local computations. Considering the bi-objective nature of the final model, the model was solved by a genetic algorithm with non-dominated sorting and the epsilon constraint method using random numbers. According to the findings of the study, the epsilon constraint method loses its efficiency with increasing model dimensions and is not able to find the Pareto frontier in problems with large dimensions. For this purpose, the model was solved using the second version of the genetic metaheuristic algorithm with non-dominated sorting and a new structure for representing chromosomes. This algorithm was able to solve problems in large dimensions that the epsilon constraint method was unable to solve. According to the results, the epsilon constraint method and the genetic algorithm with non-dominated sorting differ only in the time to solve, and have similar performance in other criteria; as a result, the genetic algorithm with non-dominated sorting is proposed as the superior method.

Keywords

Main Subjects

References

  1. Alipour, M.M., Rajoli Dezfouli, A., & Danesh Kohan, H. (2010). Using drones to inspect oil and gas pipelines. The second pipe and related industries conference. (In Persian).
  2. Alturjman, F., & Alturjman, S. (2020). 5G/IoT-enabled UAVs for multimedia delivery in industry-oriented applications. Multimedia Tools and Applications, 79(25), 74-89.
  3. Athreyasa, G. (2021). Roadway Traffic Analysis Scheme using Unmanned Aerial Vehicle Based on Image Processing and Edge Computing. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12, 122-131.
  4. Bahhar, C., Chokri, B., Sofiene, B., & Hedi, S. (2021). Real-time intelligent monitoring system based on IoT. 18th International Multi-Conference on Systems, Signals & Devices (SSD).
  5. Baradaran, V., & Hosseinian, A.H. (2021). A Multi-Objective Mathematical Formulation for the Airline Crew Scheduling Problem: MODE and NSGA-II Solution Approaches. The Journal of Industrial Management Perspective, 11(41), 247-269. (In Persian)
  6. Cao, P., YI, L., Chao, Y., Shengli, X., & Kan, X. (2019). MEC-Driven UAV-Enabled Routine Inspection Scheme in Wind Farm under Wind Influence. Digital Object Identifier, 51(33), 342-361.
  7. Chagh, Y., Guennoun Z., & Jouihri, Y. (2016). Voice service in 5G network: Towards an edge computing enhancement of voice over Wi-Fi, in Proc. Conf. Telecommun. Signal Process. (TSP), 65(5), 116–120.
  8. Fazllolahtabar, H. (2022). An Intelligent Sales Management System Based on Internet of Things and Bayesian Network. The Journal of Industrial Management Perspective, 11(44), 60-84. (In Persian).
  9. Ghazavi, A., & Tabataba, F.A. (2020). UAVs and their application in public security and smart police. Scientific Journal of Police Information and Communication Technology, 1(1), 67-90. (In Persian)
  10. Ghiasvand Ghiasi, F., Yazdani, M., Vahdani, B., & Kazemi, A. (2022). Meta-Heuristic Algorithms for Multi-Objective Home Health Care Routing and Scheduling Problem Considering Time Windows and Workload Balance of Nurses. The Journal of Industrial Management Perspective, 12(45), 226-260. (In Persian)
  11. Haghighi, H., Sadati, S.H., Karimi, J., & Dehghani, S.M.M. (2018). Continuous monitoring of multi-vehicles by basic survey patterns with the aim of minimizing review time. Aeronautical engineering, 20(1), 1-12. (In Persian)
  12. Kazami, H., & Elahian, S. (2020). The development of civilian drones in Iran and the challenges facing it. Technology in aerospace engineering, 2(23), 45-64. (In Persian)
  13. Lagkas, T., Bibi, S., Argyriou, V., & Panagiotis, G. (2018). UAV IoT Framework Views and Challenges: Towards Protecting Drones as “Things”. Sensors, 18(1), 18-25.
  14. Na, Z., Mengshu, Z., & Jun, W. (2020). UAV-assisted wireless powered Internet of Things: Joint trajectory optimization and resource allocation. Ad Hoc Networks, 98(23), 254-276.
  15. Mavrotas, G. (2009). Effective implementation of the e-constraint method in Multi-Objective Mathematical Programming problems. Applied mathematics and computation, 213(3), 455–465.
  16. Pasandideh, S.H.R., & Niaki, S.T.A. (2012). Genetic application in a facility location problem with random demand within queuing framework. Journal of Intelligent Manufacturing, 23(3), 651-659.
  17. Salhaoui, M., Guerrero, Antonio, Arioua, M., Francisco, J., Ortiz, A., Oualkadi, E., & Luis Torregrosa, C. (2019). Smart Industrial IoT Monitoring and Control System Based on UAV and Cloud Computing Applied to a Concrete Plant. Sensors, 19(3), 16-30.
  18. Wulfovich, S., Rivas, H., Matabuena, P. (2020). Drones in Healthcare. Digital Health, 4(22), 159–168.
  19. Zhang, K., Mao, Y., Leng, S., Vinel, A., & Zhang, Y. (2016). Delay constrained offloading for mobile edge computing in cloud-enabled vehicular networks. Workshop Resilient Netw. Design Modeling (RNDM), 33(2), 288–294.
  20. Zhao, T., Zhou, S., Guo, X., Zhao, Y., &Niu, Z. (2016). Pricing policy and computational resource provisioning for delay-aware mobile edge computing. IEEE/CIC Int. Commun. China (ICCC). 1–6.

Files

Share

How to cite

Statistics