Research PaperNo Access

Application Optimization of Cloud Management Mode in Information Technology in Sudden Social Security Incidents

Haining Huang

School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei 430070, P. R. China

Guangxi Vocational College of Quality Engineering, Nanning, Guangxi 530000, P. R. China

Search for more papers by this author

Lihua Xu

School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei 430070, P. R. China

Guangxi Vocational College of Quality Engineering, Nanning, Guangxi 530000, P. R. China

Search for more papers by this author

Haomin Lu

School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei 430070, P. R. China

Guangxi Vocational College of Quality Engineering, Nanning, Guangxi 530000, P. R. China

Search for more papers by this author

Zhencheng Lin

School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei 430070, P. R. China

Guangxi Vocational College of Quality Engineering, Nanning, Guangxi 530000, P. R. China

Search for more papers by this author

, and

Wei Yan

https://orcid.org/0000-0001-5090-6976

School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei 430070, P. R. China

Guangxi Vocational College of Quality Engineering, Nanning, Guangxi 530000, P. R. China

China Emergency Management Research Center, Wuhan University of Technology, Wuhan, Hubei 430070, P. R. China

E-mail Address: emfe@whut.edu.cn

Corresponding author.

Search for more papers by this author

https://doi.org/10.1142/S0218126622502127Cited by:2 (Source: Crossref)

Abstract

With the change of social structure and management mode, emergency social security incidents have become the focus of social security management. In order to solve the problems of insufficient overall performance and low security in the practical application of cloud management mode in traditional social security emergencies. With the use of improved pick-KX load balancing algorithm and RSA encryption algorithm, this paper optimizes the cloud computing model and applies it to the cloud management mode of emergent social security events. Then the optimized cloud management model is simulated and evaluated by the multi-level fuzzy comprehensive evaluation model. The simulation results reflect that the proposed cloud management model in this paper is able to effectively improve the management effect of social security events.

This paper was recommended by Regional Editor Takuro Sato.

Keywords:

References

1. J. Bryson et al., Critical incidents and emergent issues in the management of large scale change effects, Stand. Sci. 172 (2018) 297–298. Google Scholar
2. T. Lei et al., The influence study of the emergent incident to security environments and city image, J. Northwest Univ. 90 (2018) 441–443. Google Scholar
3. S. Q. Chai et al., Conception on constructing emergency platform system for emergent forest pest incidents in China, J. Zhejiang Forestry Coll. 79 (2018) 1–3. Google Scholar
4. J. Huang , On the government crisis management of emergent public affairs, J. Huaihai Inst. Technol. 6 (2018) 17–18. Google Scholar
5. L. Barton and D. Hardigree , Risk and crisis management in facilities: Emerging paradigms in assessing critical incidents, Facilities 6 (2018) 416–424. Google Scholar
6. B. Hammi et al., IoT technologies for smart cities, IET Netw. 7 (2018) 1–13. Crossref, Web of Science, Google Scholar
7. S. Luo and L. K. Zhang , The development of International Telecommunication Union(ITU-T) IoT and smart city standardization activities and China’s propulsion strategy, Telecommun. Netw. Technol. 37 (2018) 179–181. Google Scholar
8. F. Nemtanu and F. Pînzaru , Smart city management based on IoT, Smart Cities Reg. Dev. J. 28 (2018) 367–375. Google Scholar
9. X. Li , Research on key technologies of the IOT infrastructure for smart city, Comput. Meas. Control 2 (2018) 67–75. Google Scholar
10. D. H. Kim et al., A study on the development of smart food trash box using IoT technology, Environ. Politics 8 (2018) 1–34. Google Scholar
11. H. Huang , The smart libraries construction based on SIGN system of the IOT, Libr. Work Study 3 (2018) 310–311. Google Scholar
12. Q. Guo , Application of cloud computing and IOT to smart city construction, J. Chongqing Univ. Sci. Technol. 8 (2018) 427–436. Google Scholar
13. L. I. Xuefeng et al., The countermeasures and suggestions of smart city construction development in Shenyang, J. Shenyang Jianzhu Univ. 18 (2018) 488–492. Google Scholar
14. G. Fortino et al., Agent-oriented cooperative smart objects: From IoT system design to implementation, IEEE Trans. Syst. Man Cybernet. Syst. 11 (2018) 1–18. Google Scholar
15. Y. Li et al., Study on the development of smart jewelry in the IoT environment, IEEE Trans. Syst. Man Cybernet. Syst. 9 (2018) 19. Google Scholar
16. N. Sheth et al., Comprehensive study on various applications using internet of things (IOT) for smart city, Int. J. Adv. Res. 5(11), 1154–1162. Crossref, Google Scholar
17. C. D. G. Romero, J. K. D. Barriga and J. I. R. Molano , Big data meaning in the architecture of IoT for smart cities, Lect. Notes Comput. Sci. 1 (2018) 457–461. Google Scholar
18. J. Qi , Mechanism system of emergent incidents and emergency management, Bull. Chin. Acad. Sci. 7 (2018) 159–162. Google Scholar
19. Q. Zhang, K. Yu, Z. Guo, S. Garg, J. J. P. C. Rodrigues, M. M. Hassan and M. Guizani , Graph neural networks-driven traffic forecasting for connected internet of vehicles, IEEE Trans. Netw. Sci. Eng., https://doi.org/10.1109/TNSE.2021.3126830. Web of Science, Google Scholar
20. D. Meng et al., A data-driven intelligent planning model for UAVs routing networks in mobile internet of things, Comput. Commun. 179 (2021) 231–241. Crossref, Web of Science, Google Scholar
21. Z. Guo, K. Yu, A. Jolfaei, F. Ding and N. Zhang , Fuz-spam: Label smoothing-based fuzzy detection of spammers in internet of things, IEEE Trans. Fuzzy Syst, https://doi.org/10.1109/TFUZZ.2021.3130311. Web of Science, Google Scholar
22. J. Gao et al., Modeling of social-impact diffusion of major security emergencies in oil-gas production companies, Int. J. Adv. Res. 6 (2018) 73–97. Google Scholar
23. K. Yu, L. Tan, L. Lin, X. Cheng, Z. Yi and T. Sato , Deep-learning-empowered breast cancer auxiliary diagnosis for 5GB remote E-health, IEEE Wireless Commun. 28 (2021) 54–61. Crossref, Web of Science, Google Scholar
24. M. Vashishtha et al., Security and detection mechanism in IoT-based cloud computing using hybrid approach, Int. J. Internet Technol. Secur. Trans. 11 (2021) 436–451. Crossref, Google Scholar
25. L. Xu, X. Zhou, X. Li, R. H. Jhaveri, T. R. Gadekallu and Y. Ding , Mobile collaborative secrecy performance prediction for artificial IoT networks, IEEE Trans. Ind. Inf. (2021). https://doi.org/10.1109/TII.2021.3128506 Web of Science, Google Scholar
26. R. M. S. Priya et al., Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything, J. Parallel Distrib. Comput. 142 (2020) 16–26. Crossref, Web of Science, Google Scholar