Design of a control mechanism for the educational management automation system under the Internet of Things environment

Yuanfu Liu; Yi Liu; Yuanfu Liu; Yi Liu

doi:10.3934/mbe.2023330

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 7661-7678. doi: 10.3934/mbe.2023330

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Research article

Design of a control mechanism for the educational management automation system under the Internet of Things environment

Yuanfu Liu ^,,
Yi Liu

Physical Education of Sichuan Normal University, Sichuan Normal University, Chengdu 610068, China

Received: 04 December 2022 Revised: 15 January 2023 Accepted: 03 February 2023 Published: 20 February 2023

Since the entrance of the Internet era, management automation has been an inevitable tendency in many areas. Especially, the great progress of Internet of Things (IoT) in recent years has provided more convenience for basic data integration. This also boosts the development of various management automation systems. In this context, this paper takes physical education as the object, and proposes the design of a control mechanism for educational management automation systems under the IoT environment. First, a description with respect to the overall design, detailed design, and database design is given. In addition, a low-consumption flow table batch update mechanism is studied, which packages and distributes the update rules of all nodes to be updated, in order to reduce the communication consumption between the controller and nodes. The results show that the education management automation of the college gymnasium can be well realized by using the optimization control mechanism. It cannot only make reasonable adjustments to college sports resource data, basic equipment, etc., but also improves the quality of resource management of college physical education courses to ensure that college sports resources can be used in all aspects, and further improves the operating efficiency of the sports management system. The automation technology design of the college sports management system can improve the efficiency of college sports management by more than 20%, so as to ensure the comprehensive development of students in physical education courses and promote the rapid improvement of college management level.
- optimal control,
- automation management system,
- Internet of Things,
- database design,
- smart education
Citation: Yuanfu Liu, Yi Liu. Design of a control mechanism for the educational management automation system under the Internet of Things environment[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 7661-7678. doi: 10.3934/mbe.2023330

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Abstract

Since the entrance of the Internet era, management automation has been an inevitable tendency in many areas. Especially, the great progress of Internet of Things (IoT) in recent years has provided more convenience for basic data integration. This also boosts the development of various management automation systems. In this context, this paper takes physical education as the object, and proposes the design of a control mechanism for educational management automation systems under the IoT environment. First, a description with respect to the overall design, detailed design, and database design is given. In addition, a low-consumption flow table batch update mechanism is studied, which packages and distributes the update rules of all nodes to be updated, in order to reduce the communication consumption between the controller and nodes. The results show that the education management automation of the college gymnasium can be well realized by using the optimization control mechanism. It cannot only make reasonable adjustments to college sports resource data, basic equipment, etc., but also improves the quality of resource management of college physical education courses to ensure that college sports resources can be used in all aspects, and further improves the operating efficiency of the sports management system. The automation technology design of the college sports management system can improve the efficiency of college sports management by more than 20%, so as to ensure the comprehensive development of students in physical education courses and promote the rapid improvement of college management level.

References

[1]	Y. Zhu, W. Zheng, Observer-based control for cyber-physical systems with dos attacks via a cyclic switching strategy, IEEE Transact. Autom. Control, 65 (2020), 3714–3721. https://doi.org/ 10.1109/TAC.2019.2953210 doi: 10.1109/TAC.2019.2953210
[2]	Z. Cai, Z. He, X. Guan, Y. Li, Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks, IEEE T. Depend. Secure., 15 (2018), 577–590. https://doi.org/10.1109/TDSC.2016.2613521 doi: 10.1109/TDSC.2016.2613521
[3]	Z. Guo, K. Yu, A. K. Bashir, D. Zhang, Y. D. Al-Otaibi, M. Guizani, Deep information fusion-driven POI scheduling for mobile social networks, IEEE Network, 36 (2022), 210–216. https://doi.org/10.1109/MNET.102.2100394 doi: 10.1109/MNET.102.2100394
[4]	Y. Li, H. Ma, L.Wang, S. Mao, G. Wang, Optimized content caching and user association for edge computing in densely deployed heterogeneous networks, IEEE T. Mobile Comput., 21 (2022), 2130–2142. https://doi.org/10.1109/TMC.2020.3033563 doi: 10.1109/TMC.2020.3033563
[5]	L. Chen, Y. Zhu, C. K. Ahn, Adaptive neural network-based observer design for switched systems with quantized measurements, IEEE T. Neur. Net. Lear., (2021). https://doi.org/10.1109/TNNLS.2021.3131412 doi: 10.1109/TNNLS.2021.3131412
[6]	Z. Guo, K. Yu, Z. Lv, K.-K. R. Choo, P. Shi, J. J. P. C. Rodrigues, Deep federated learning enhanced secure POI microservices for cyber-physical systems, IEEE Wirel. Commun., 29 (2022), 22–29. https://doi.org/10.1109/MWC.002.2100272 doi: 10.1109/MWC.002.2100272
[7]	L. Zhao, H. Chai, Y. Han, K. Yu, S. Mumtaz, A collaborative V2X data correction method for road safety, IEEE T. Reliab., 71 (2022), 951–962. https://doi.org/10.1109/TR.2022.3159664 doi: 10.1109/TR.2022.3159664
[8]	L. Huang, R. Nan, K. Chi, Q. Hua, K. Yu, N. Kumar, et al., Throughput guarantees for multi-cell wireless powered communication networks with non-orthogonal multiple access, IEEE T. Veh. Technol., 71 (2022), 12104–12116. https://doi.org/10.1109/TVT.2022.3189699
[9]	H. Moore, How to mathematically optimize drug regimens using optimal control, J. Pharmacokinet. Phar., 45 (2018), 127–137. https://doi.org/10.1007/s10928-018-9568-y doi: 10.1007/s10928-018-9568-y
[10]	Z. Guo, Y. Shen, S. Wan, W. Shang, K. Yu, Hybrid intelligence-driven medical image recognition for remote patient diagnosis in Internet of medical things, IEEE J. Biomed. Health., 26 (2022), 5817–5828, https://doi.org/10.1109/JBHI.2021.3139541 doi: 10.1109/JBHI.2021.3139541
[11]	S. Xia, Z. Yao, Y. Li, W. Shang, K. Yu, Online distributed offloading and computing resource management with energy harvesting for heterogeneous MEC-enabled IoT, IEEE T. Wirel. Commun., 20 (2021), 6743–6757. https://doi.org/10.1109/TWC.2021.3076201 doi: 10.1109/TWC.2021.3076201
[12]	Z. Guo, K. Yu, A. Jolfaei, F. Ding, N. Zhang, Fuz-Spam: Label smoothing-based fuzzy detection of spammers in Internet of Things, IEEE T. Fuzzy Syst., 30 (2022), 4543–4554. https://doi.org/10.1109/TFUZZ.2021.3130311 doi: 10.1109/TFUZZ.2021.3130311
[13]	X. Zheng, Z. Cai, Privacy-preserved data sharing towards multiple parties in industrial IoTs, IEEE J. Sel. Area. Comm., 38 (2020) 968–979. https://doi.org/10.1109/JSAC.2020.2980802 doi: 10.1109/JSAC.2020.2980802
[14]	T. Yang, Z. Bai, Z. Li, N. Feng, L. Chen, Intelligent vehicle lateral control method based on feedforward+ predictive LQR algorithm, Actuators, 10 (2021), 228. https://doi.org/10.3390/act10090228 doi: 10.3390/act10090228
[15]	C. Chen, Z. Liao, Y. Ju, C. He, K. Yu, S. Wan, Hierarchical domain-based multi-controller deployment strategy in SDN-enabled space-air-ground integrated network, IEEE T. Aero. Elec. Sys., 58 (2022), 4864–4879. https://doi.org/10.1109/TAES.2022.3199191 doi: 10.1109/TAES.2022.3199191
[16]	A. Hudimova, I. Popových, O. Savchuk, V. Lіashko, A. Pyslar, A. Hrys, et al., Research on the relationship between excessive use of social media and young athletes' physical activity, J. Physical Educ. Sport, 21 (2021), 3364–3373. http://ekhsuir.kspu.edu/123456789/16375
[17]	Y. Lin, X. Wang, F. Hao, Y. Jiang, Y.Wu, G. Min, et al., Dynamic control of fraud information spreading in mobile social networks, IEEE T. Syst. Man. Cy. A, 51 (2019), 3725–3738. https://doi.org/10.1109/TSMC.2019.2930908 doi: 10.1109/TSMC.2019.2930908
[18]	P. Singh, M. A. Dulebenets, J. Pasha, E. D. R. S. Gonzalez, Y.-Y. Lau, R. Kampmann, Deployment of autonomous trains in rail transportation: Current trends and existing challenges, IEEE Access, 9 (2021), 91427–91461. https://doi.org/10.1109/ACCESS.2021.3091550 doi: 10.1109/ACCESS.2021.3091550
[19]	R. S. Rajan, Y. Yu, F. Richert, Impact of cost-optimized dedicated hybrid transmission (DHT) constraints on powertrain optimal control, P. I. Mech. Eng. D: J. Aut., 236 (2022), 987–1006. https://doi.org/10.1177/09544070211029445 doi: 10.1177/09544070211029445
[20]	D. Fan, G. P. Jiang, Y. R. Song, Y.-W. Li, G. R. Chen, Novel epidemic models on PSO-based networks, J. Theor. Boil., 477 (2019), 36–43. https://doi.org/10.1016/j.jtbi.2019.06.006 doi: 10.1016/j.jtbi.2019.06.006
[21]	W. Wang, X. Chen, H. Fu, M. Wu, Data-driven adaptive dynamic programming for partially observable nonzero-sum games via Q-learning method, Int. J. Syst. Sci., 50 (2019), 1338–1352. https://doi.org/10.1080/00207721.2019.1599463 doi: 10.1080/00207721.2019.1599463
[22]	H. Habibzadeh, K. Dinesh, O. R. Shishvan, A. Boggio-Dandry, G. Sharma, T. Soyata, A survey of healthcare Internet of Things (HIoT): A clinical perspective, IEEE Int. Things J., 7 (2019), 53–71. https://doi.org/10.1109/JIOT.2019.2946359 doi: 10.1109/JIOT.2019.2946359
[23]	K. Mahmoud, M. Abdel-Nasser, M. Lehtonen, M. M. Hussein, Optimal voltage regulation scheme for pv-rich distribution systems interconnected with D-STATCOM, Elect. Pow. Compo. Sys., 48 (2021), 2130–2143. https://doi.org/10.1080/15325008.2021.1915430 doi: 10.1080/15325008.2021.1915430
[24]	M. F. Tabassum, S. Akram, S. Mahmood-ul-Hassan, R. Karim, P. A. Naik, M. Farman, et al., Differential gradient evolution plus algorithm for constraint optimization problems: A hybrid approach, Int. J. Optim. Control Theor. Appl. (IJOCTA), 11 (2021) 158–177. https://doi.org/10.11121/ijocta.01.2021.001077 doi: 10.11121/ijocta.01.2021.001077
[25]	C. Song, K. Kim, D. Sung, K. Kim, H. Yang, H. Lee, et al., A review of optimal energy management strategies using machine learning techniques for hybrid electric vehicles, Int. J. Auto. Tech., 22 (2021), 1437–1452. https://doi.org/10.1007/s12239-021-0125-0 doi: 10.1007/s12239-021-0125-0
[26]	Z. D. Asher, A. A. Patil, V. T. Wifvat, A. A. Frank, S. Samuelsen, T, H. Bradley, Identification and review of the research gaps preventing a realization of optimal energy management strategies in vehicles, SAE Int. J. Altern. Pow., 8 (2019), 133–150. https://www.jstor.org/stable/26926444
[27]	Y. B. Zikria, R. Ali, M. K. Afzal, et al. Next-generation internet of things (iot): Opportunities, challenges, and solutions, Sensors, 21(2021) 1174. https://doi.org/10.3390/s21041174
[28]	M. Strazzullo, F. Ballarin, G. Rozza, POD-Galerkin model order reduction for parametrized nonlinear time-dependent optimal flow control: an application to shallow water equations, J. Numer. Math., 30 (2022) 63–84. https://doi.org/10.1515/jnma-2020-0098 doi: 10.1515/jnma-2020-0098
[29]	P. Singh, Z. Elmi, V. K. Meriga, J. Pasha, M. A. Dulebenets, Internet of Things for sustainable railway transportation: Past, present, and future, Clean. Logist. Supply Chain, 4 (2022), 100065. https://doi.org/10.1016/j.clscn.2022.100065 doi: 10.1016/j.clscn.2022.100065
[30]	Y. Sarac, S. S. Sener, Identification of the initial temperature from the given temperature data at the left end of a rod, Appl. Math. Nonlinear Sci., 4 (2019), 469–474. https://doi.org/10.2478/AMNS.2019.2.00044 doi: 10.2478/AMNS.2019.2.00044
[31]	X. Shen, G. Shi, H. Ren, W. Zhang, Biomimetic vision for zoom object detection based on improved vertical grid number YOLO algorithm, Front. Bioeng. Biotech., 847 (2022), 905583. https://doi.org/10.3389/fbioe.2022.905583 doi: 10.3389/fbioe.2022.905583
[32]	P. Singh, Elmi Z, Lau Y, M. Borowska-Stefańska, S. Wiśniewski, M. A. Dulebenets, Blockchain and AI technology convergence: Applications in transportation systems, Veh. Commun., 38 (2022), 100521. https://doi.org/10.1016/j.vehcom.2022.100521 doi: 10.1016/j.vehcom.2022.100521
[33]	Y. Ünlü, Z. Taş, A bibliography experiment on research within the scope of industry 4.0 application areas in sports, J. New Result Sci., 17 (2020), 1149–1176. https://doi.org/10.14687/jhs.v17i4.6088 doi: 10.14687/jhs.v17i4.6088

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