An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things

Xiangshuai Duan; Naiping Song; Fu Mo; Xiangshuai Duan; Naiping Song; Fu Mo

doi:10.3934/mbe.2023326

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 7548-7564. doi: 10.3934/mbe.2023326

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Research article

An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things

1.
President's Office, Jiangsu University of Technology, Changzhou 213001, China
2.
School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China
3.
School of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan 523083, China

Academic Editor: Vladimir Mityushev

Received: 28 November 2022 Revised: 15 January 2023 Accepted: 17 January 2023 Published: 16 February 2023

Edge intelligence refers to a novel operation mode in which intelligent algorithms are implemented in edge devices to break the limitation of computing power. In the context of big data, mobile computing has been an effective assistive tool in many cross-field areas, in which quantitative assessment of implicit working gain is typical. Relying on the strong ability of data integration provided by the Internet of Things (IoT), intelligent algorithms can be equipped into terminals to realize intelligent data analysis. This work takes the assessment of working gain in universities as the main problem scenario, an edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile IoT. Based on fundamental data acquisition from deployed mobile IoT environment, all the distributed edge terminals are employed to implement machine learning algorithms to formulate a quantitative assessment model. The dataset collected from a real-world application is utilized to evaluate the performance of the proposed mobile edge computing framework, and proper performance can be obtained and observed.
- edge intelligence,
- mobile IoT,
- quantitative assessment model,
- mobile computing,
- machine learning
Citation: Xiangshuai Duan, Naiping Song, Fu Mo. An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 7548-7564. doi: 10.3934/mbe.2023326

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Abstract

Edge intelligence refers to a novel operation mode in which intelligent algorithms are implemented in edge devices to break the limitation of computing power. In the context of big data, mobile computing has been an effective assistive tool in many cross-field areas, in which quantitative assessment of implicit working gain is typical. Relying on the strong ability of data integration provided by the Internet of Things (IoT), intelligent algorithms can be equipped into terminals to realize intelligent data analysis. This work takes the assessment of working gain in universities as the main problem scenario, an edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile IoT. Based on fundamental data acquisition from deployed mobile IoT environment, all the distributed edge terminals are employed to implement machine learning algorithms to formulate a quantitative assessment model. The dataset collected from a real-world application is utilized to evaluate the performance of the proposed mobile edge computing framework, and proper performance can be obtained and observed.

References

[1]	D. Ushakov, The Role of Equity and Justice in Mediating the relationship between implicit working gain and employee performance: evidence from lebanon, J. Asian Finance Econ. Bus., 8 (2021), 625–635. https://doi.org/10.13106/jafeb.2021.vol8.no8.0625 doi: 10.13106/jafeb.2021.vol8.no8.0625
[2]	G. Fragkos, S. Lebien, E. E. Tsiropoulou, Artificial intelligent multi-access edge computing servers management, IEEE Access, 8 (2021), 171292–171304. https://doi.org/10.1109/ACCESS.2020.3025047 doi: 10.1109/ACCESS.2020.3025047
[3]	H. Ke, H. Wang, W. Sun, H. Sun, Adaptive computation offloading policy for multi-access edge computing in heterogeneous wireless networks, IEEE Trans. Network Serv. Manage., 19 (2022), 289–305. https://doi.org/10.1109/TNSM.2021.3118696 doi: 10.1109/TNSM.2021.3118696
[4]	H. Lin, X. Xu, J. Zhao, X. Wang, Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios, J. Wireless Com. Network., 191 (2020), 121–132. https://doi.org/10.1186/s13638-020-01805-2 doi: 10.1186/s13638-020-01805-2
[5]	H. Peng, Q. Ye, X. Shen, Spectrum management for multi-access edge computing in autonomous vehicular networks, IEEE Trans. Intell. Transp. Syst., 21 (2020), 3001–3012. https://doi.org/10.1109/TITS.2019.2922656 doi: 10.1109/TITS.2019.2922656
[6]	Q. Zhang, C. Li, Y. Huang, Y. Luo, Effective multi-controller management and adaptive service deployment strategy in multi-access edge computing environment, Ad Hoc Networks, 138 (2023), 41–46. https://doi.org/10.1016/j.adhoc.2022.103020 doi: 10.1016/j.adhoc.2022.103020
[7]	S. Messaoud, S. Bouaafia, A. Maraoui, A. C. Ammari, L. Khriji, M. Machhout, Deep convolutional neural networks-based Hardware-Software on-chip system for computer vision application, Comput. Electr. Eng. 98 (2022), 1–15. https://doi.org/10.1016/j.compeleceng.2021.107671 doi: 10.1016/j.compeleceng.2021.107671
[8]	P. Lin, Research on enterprise employee implicit working gain management system based on CS architecture, Secur. Commun. Networks, 2021 (2021), 32. https://doi.org/10.1155/2021/9087094 doi: 10.1155/2021/9087094
[9]	J. L. Donaldson, Tools for a statewide implicit working gain system for extension professionals, J. Ext., 57 (2020), 102.
[10]	A. Alsharef, Sonia, K. Kumar, C. Iwendi, Time series data modeling using advanced machine learning and autoML, Sustainability, 14 (2022), 15292. https://doi.org/10.3390/su142215292 doi: 10.3390/su142215292
[11]	J. H. Anajemba, C. Iwendi, I. Razzak, J. A. Ansere, I. M. Okpalaoguchi, A counter-eavesdropping technique for optimized privacy of wireless industrial IoT communications, IEEE Trans. Ind. Inf., 18 (2022), 6445–6454. https://doi.org/10.1109/TⅡ.2021.3140109 doi: 10.1109/TⅡ.2021.3140109
[12]	M. Shabbir, A. Shabbir, C. Iwendi, A. R. Javed, M. Rizwan, N. Herencsar, et al., Enhancing security of health information using modular encryption standard in mobile cloud computing, IEEE Access, 9 (2021), 8820–8834. https://doi.org/10.1109/ACCESS.2021.3049564 doi: 10.1109/ACCESS.2021.3049564
[13]	Z. Guo, D. Meng, C. Chakraborty, X. Fan, A. Bhardwaj, K. Yu, Autonomous behavioral decision for Vehicular Agents Based on Cyber-Physical Social Intelligence, IEEE Trans. Comput. Soc. Syst., (2022), 1–12. https://doi.org/10.1109/TCSS.2022.3212864 doi: 10.1109/TCSS.2022.3212864
[14]	S. Zhang, H. Gu, K. Chi, L. Huang, K. Yu, S. Mumtaz, DRL-based partial offloading for maximizing sum computation rate of wireless powered mobile edge computing network, IEEE Trans. Wireless Commun., 21 (2022), 10934–10948. https://doi.org/10.1109/TWC.2022.3188302 doi: 10.1109/TWC.2022.3188302
[15]	S. Xia, Z. Yao, Y. Li, S. Mao, Online distributed offloading and computing resource management with energy harvesting for heterogeneous MEC-enabled IoT, IEEE Trans. Wireless Commun., 20 (2021), 6743–6757. https://doi.org/10.1109/TWC.2021.3076201 doi: 10.1109/TWC.2021.3076201
[16]	Q. Zhang, K. Yu, Z. Guo, S. Garg, J. J. P. C. Rodrigues, M. M. Hassan, et al., Graph neural networks-driven traffic forecasting for connected internet of vehicles, IEEE Trans. Network Sci. Eng., 9 (2022), 3015–3027. https://doi.org/10.1109/TNSE.2021.3126830 doi: 10.1109/TNSE.2021.3126830
[17]	L. Zhao, Z. Yin, K. Yu, X. Tang, L. Xu, Z. Guo, A fuzzy logic based intelligent multi-attribute routing scheme for two-layered SDVNs, IEEE Trans. Network Serv. Manage., 2022 (2022), 1. https://doi.org/10.1109/TNSM.2022.3202741 doi: 10.1109/TNSM.2022.3202741
[18]	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
[19]	D. Peng, D. He, Y. Li, Z. Wang, Integrating terrestrial and satellite multibeam systems toward 6G: techniques and challenges for interference mitigation, IEEE Wireless Commun., 29 (2022), 24–31. https://doi.org/10.1109/MWC.002.00293 doi: 10.1109/MWC.002.00293
[20]	L. D. Corso, A. D. Carlo, F. Carluccio, D. Girardi, A. Falco, An opportunity to grow or a label? implicit working gain justice and implicit working gain satisfaction to increase teachers' well-being, Front. Psychol., 10 (2020), 22. https://doi.org/10.3389/fpsyg.2019.02361 doi: 10.3389/fpsyg.2019.02361
[21]	A. Bayo-Moriones, J. E. Galdon-Sanchez, S. Martinez-de-Morentin, Business strategy, implicit working gain and organizational results, Pers. Rev., 50 (2021), 515–534. https://doi.org/10.1108/PR-09-2019-0498 doi: 10.1108/PR-09-2019-0498
[22]	Z. Guo, K. Yu, A. Jolfaei, F. Ding, N. Zhang, Fuz-spam: label smoothing-based fuzzy detection of spammers in Internet of Things, IEEE Trans. Fuzzy Syst., 30 (2022), 4543–4554. https://doi.org/10.1109/TFUZZ.2021.3130311 doi: 10.1109/TFUZZ.2021.3130311
[23]	J. Yang, Y. Li, Q. Liu, L. Li, A. Feng, T. Wang, et al., Brief introduction of medical database and edge intelligence-enhanced quantitative assessment model in big data era, J. Evid. Based Med., 13 (2020), 57–69. https://doi.org/10.1111/jebm.12373 doi: 10.1111/jebm.12373
[24]	O. V. Nagovitsyn, S. V. Lukichev, Temporal approach to modeling objects within a mining technology, J. Min. Sci., 56 (2020), 1046–1052. https://doi.org/10.1134/S1062739120060174 doi: 10.1134/S1062739120060174
[25]	S. Gul, S. Bano, T. Shah, Exploring data mining: facets and emerging trends, Digital Libr. Perspect., 37 (2021), 429–448. https://doi.org/10.1108/DLP-08-2020-0078 doi: 10.1108/DLP-08-2020-0078
[26]	L. Barsanti, L. Birindelli, P. Gualtieri, Water monitoring by means of digital microscopy identification and classification of microalgae, Environ. Sci. Process. Impacts, 23 (2021), 1443–1457. https://doi.org/10.1039/d1em00258a doi: 10.1039/d1em00258a
[27]	L. Abualigah, M. A. Elaziz, P. Sumari, Z. W. Geem, A. H. Gandomi, Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer, Expert Syst. Appl., 8 (2021), 191. https://doi.org/10.1016/j.eswa.2021.116158 doi: 10.1016/j.eswa.2021.116158
[28]	Z. Wang, W. Lu, Z. Chang, H. Wang, Simultaneous identification of groundwater contaminant source and simulation model parameters based on an ensemble Kalman filter-Adaptive step length ant colony optimization algorithm, J. Hydrol., 605 (2022), 127352. https://doi.org/10.1016/j.jhydrol.2021.127352 doi: 10.1016/j.jhydrol.2021.127352
[29]	B. Sun, X. Liu, Z. Xu, A multiscale bridging material parameter and damage inversion algorithm from macroscale to mesoscale based on ant colony optimization, J. Eng. Mech., 148 (2022), 9. http://doi.org/10.1061/(ASCE)EM.1943-7889.0002067 doi: 10.1061/(ASCE)EM.1943-7889.0002067
[30]	S. Li, T. Gao, Z. Ye, Y. Wang, Comparative research on the formation of backbone media of wireless self-organizing network by DLA-GF algorithm and ant colony algorithm, Alexandria Eng. J., 61 (2022), 949–961. https://doi.org/10.1016/j.aej.2021.06.003 doi: 10.1016/j.aej.2021.06.003

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