Skip Navigation Links
Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


Resource Allocation Strategy for Power Safety Tools with Fuzzy Systems and Edge Computing

Journal of Vibration Testing and System Dynamics 8(3) (2024) 329--340 | DOI:10.5890/JVTSD.2024.09.005

Bin Liu, Zhongqiang Luo, Xiang Dai, Hongbo Chen

School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, 644000, China

Sichuan Shuneng Electric Power Co. LTD, Chengdu, 610000, China

Download Full Text PDF



As a solution to solve data congestion and improve the quality of service, edge computing emphasizes that the data of local data sources should be processed according to their locations. In order to solve the problem that the efficiency of traditional power safety management tools is low in the management cycle process that affects business delivery, this paper proposes a resource offloading algorithm with multi-access edge computing (MEC) server as the core and cloud collaborative work. Firstly, the application of MEC in the scenario of new infrastructure power safety tools is studied.Then, a three-layer edge computing architecture of terminal, edge and cloud is constructed with resource scheduling, and the attributes of incoming tasks, network transmission and computing resource performance in this scenario are dynamically considered. Finally, the fuzzy logic coordinator is used to determine which services are cached at the edge and which tasks are executed in the cloud. The simulation experiment results show that the superiority of the proposed resource offloading algorithm in the service performance of power safety tools is verified from multiple performance indexes such as service time, task failure rate, and resource utilization rate.


  1. [1]  Kochovski, P. and Stankovski, V. (2018), Supporting smart construction with dependable edge computing infrastructures and applications, Automation in Construction, 85, 182-192.
  2. [2]  Kim, H.S. (2016), Fog computing and the internet of things: extend the cloud to where the things are, Int. Journal Cisco.
  3. [3]  Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., and Sabella, D. (2017), On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration, IEEE Communications Surveys and Tutorials, 19(3), 1657-1681.
  4. [4]  Wu, H.J., Chen, L., Wu, H.X., and Zhang, L.Y. (2014), Research on life cycle management of safety tools and tools with the introduction of RFID, Computer Simulation, 31(3), 159-161.
  5. [5]  Sonmez, C., Ozgovde, A., and Ersoy, C. (2017), Edgecloudsim: an environment for performance evaluation of edge computing systems, Transactions on Emerging Telecommunications Technologies, 29(11), 39-44.
  6. [6]  Yang, W., Gu, Z.W., Jiang, X.D., and Lan, Y.K. (2021), Design and development of power safety tools management system based on RFID, Modern Information Technology, 5(8), 115-118.
  7. [7]  Xu, Y.F., Feng, J., Ji, Y.H., You, Q., and Xue, Z. (2019), Design of electric field operation safety management system based on Internet of things technology, Manufacturing Automation, 41(8), 110-114.
  8. [8]  Li, Y., Lv, X.B., Li, Y., and Sun, S.J. (2021), The safety management and control method of construction tools in substation based on ultra-wideband technology, Journal of Shandong University Science (engineering), 51(3), 84-90.
  9. [9]  Li, F. (2020), Design of equipment asset management system based on RFID technology, Modern Electronics, 43(23), 130-133.
  10. [10]  Chen, L., Yang, K., and Zhang, J.J. (2022), Power safety tools management based on distributed cloud storage,Electrical Applications, 41(2), 32-35.
  11. [11]  Yin, Z., Zhou, X.J., and Su, Y.R. (2020), Theoretical research based on ``Internet+" in the Management of power safety tools, International Signal Processing, Communications and Engineering Management Conference (ISPCEM), 196-198.
  12. [12]  Qi, Q.L. and Tao, F. (2019), A smart Manufacturing service system based on edge computing, fog computing, and cloud computing, IEEE Access, 7, 86769-86777.
  13. [13]  Zhang, X., Duan, M.Y., Xu, R.F., Rao, H., and Deng, J.Z. (2022), EdgeCloudSim based computing resource configuration strategy analysis of cloud-edge system in power distribution internet of things, 7th Asia Conference on Power and Electrical Engineering (ACPEE), Hangzhou, China, June, 2013-2018.
  14. [14]  Aleyadeh, S., Moubayed, A., and Shami, A. (2021), mobility aware edge computing segmentation towards localized orchestration, 2021 International Symposium on Networks, Computers and Communications (ISNCC), Dubai, United Arab Emirates, Dubai, United Arab Emirates, Nov, 1-6.
  15. [15]  Sonmez, C., Ozgovde, A., and Ersoy, C. (2019), Fuzzy workload orchestration for edge computing, IEEE Transactions on Network and Service Management, 16(2), 769-782.
  16. [16]  Flores, H. and Srirama, S. (2013), Adaptive code offloading for mobile cloud applications: Exploiting fuzzy sets and evidence-based learning, Proceeding of the fourth ACM Workshop on Mobile Cloud Computing and Services, 9-16.