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Air Quality Prediction and Multi-Task Offloading based on Deep Learning Methods in Edge Computing

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Abstract

The problem at hand is to effectively monitor air quality, which requires the computation of sensor data from multiple dimensions and locations. Previous approaches to this problem have utilized centralized machine learning techniques to address it. For edge devices with limited resources, these are frequently inappropriate. This paper deals with this issue by developing a brand-new hybrid deep-learning method for hourly PM2.5 pollutant prediction. The proposed method optimizes the resulting model for edge devices and assesses model performance regarding accuracy and latency on edge devices. Next, we suggest a time-optimized, multi-task offloading model based on the ideas of Optimal Stopping Theory (OST), to increase the likelihood of offloading to the best servers. We offer another OST-based strategy to reduce the overall offloading latency when the server use is nearly distributed uniformly. To forecast hourly PM2.5 concentration, this work uses a deep learning model, Multi-factor Long Short-Term Memory (LSTM), and Deep Reinforcement Learning (DRL). The analysis of the findings using RMSE and MAE errors demonstrates that our suggested model beats other deep learning models. The edge device received 8272 hourly samples, and the RPi4B executed the model twice as quickly as the RPi3B + in all quantization modes. The least time was required for full-integer quantization, which resulted in latencies of 2.19 and 4.73 s for the RPi4B and RPi3B + , correspondingly.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Wardana, I NyomanKusama., Gardner, Julian W., Fahmy, Suhaib A.: Optimising deep learning at the edge for accurate hourly air quality prediction. Sensors 21(4), 1064 (2021)

    Article  Google Scholar 

  2. Mu, Liting, et al.: Multi-Task Offloading Based on Optimal Stopping Theory in Edge Computing Empowered Internet of Vehicles. Entropy 24(6), 814 (2022)

    Article  MathSciNet  Google Scholar 

  3. Alkabbani, Hanin, et al.: An improved air quality index machine learning-based forecasting with multivariate data imputation approach. Atmosphere 13(7), 1144 (2022)

    Article  Google Scholar 

  4. Chen, H., Guan, M., Li, H.: Air quality prediction based on integrated dual LSTM model. IEEE Access 9, 93285–93297 (2021)

    Article  Google Scholar 

  5. Islam, N., Saikia, B.K.: Atmospheric particulate matter and potentially hazardous compounds around residential/road side soil in an urban area. Chemosphere 259, 127453 (2020)

    Article  Google Scholar 

  6. Daiber, A., Kuntic, M., Hahad, O., Delogu, L.G., Rohrbach, S., Lisa, F.D., Schulz, R., Münzel, T.: Effects of air pollution particles (ultrafine and fine particulate matter) on mitochondrial function and oxidative stress – Implications for cardiovascular and neurodegenerative diseases. Arch. Biochem. Biophys 696, 108662 (2020)

    Article  Google Scholar 

  7. Castelli, M., Clemente, F.M., Popoviˇc, A., Silva, S., Vanneschi, L.: A Machine Learning Approach to Predict Air Quality in California. Complexity 2020, 8049504 (2020)

    Article  Google Scholar 

  8. Navares, R., Aznarte, J.L.: Predicting air quality with deep learning LSTM: Towards comprehensive models. Ecol. Inform 55, 101019 (2020)

    Article  Google Scholar 

  9. Xayasouk, T., Lee, H., Lee, G.: Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models. Sustainability 12, 2570 (2020)

    Article  Google Scholar 

  10. Seng, D., Zhang, Q., Zhang, X., Chen, G., Chen, X.: Spatiotemporal prediction of air quality based on LSTM neural network. Alex. Eng. J 60, 2021–2032 (2021)

    Article  Google Scholar 

  11. Xu, J.; Chen, L.; Lv, M.; Zhan, C.; Chen, S.; Chang, J. HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method, arXiv:2101.04264 (2021)

  12. Ma, J., Ding, Y., Cheng, J.C., Jiang, F., Wan, Z.: A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2.5. J. Clean. Prod 237, 117729 (2019)

    Article  Google Scholar 

  13. Bai, Y., Zeng, B., Li, C., Zhang, J.: An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting. Chemosphere 222, 286–294 (2019)

    Article  Google Scholar 

  14. Zhao, J., Deng, F., Cai, Y., Chen, J.: Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. Chemosphere 220, 486–492 (2019)

    Article  Google Scholar 

  15. Qin, D., Yu, J., Zou, G., Yong, R., Zhao, Q., Zhang, B.: A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration. IEEE Access 7, 20050–20059 (2019)

    Article  Google Scholar 

  16. Qi, Y., Li, Q., Karimian, H., Liu, D.: A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Sci. Total Environ. 664, 1–10 (2019)

    Article  Google Scholar 

  17. Zhang, Q.; Lam, J.C.; Li, V.O.; Han, Y. Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast, arXiv:2001.11957 (2020)

  18. Li, T., Hua, M., Wu, X.: A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5). IEEE Access 8, 26933–26940 (2020)

    Article  Google Scholar 

  19. Tao, Q., Liu, F., Li, Y., Sidorov, D.: Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU. IEEE Access 7, 76690–76698 (2019)

    Article  Google Scholar 

  20. Chen, H.C.; Putra, K.T.; Chun-WeiLin, J. A Novel Prediction Approach for Exploring PM2.5 Spatiotemporal Propagation Based on Convolutional Recursive Neural Networks, arXiv:2101.06213 (2021)

  21. Banner, R.; Nahshan, Y.; Soudry, D: Post training 4-bit quantization of convolutional networks for rapid-deployment. In Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 8–14 December; Volume 32, pp. 7950–7958 (2019)

  22. Zhang, X., Wen, S., Yan, L., Feng, J., & Xia, Y: A Hybrid-Convolution Spatial–Temporal Recurrent Network For Traffic Flow Prediction. Comput. J. c171 (2022)

  23. Wang, H., Cui, Z., Liu, R., Fang, L., & Sha, Y: A Multi-type Transferable Method for Missing Link Prediction in Heterogeneous Social Networks. IEEE Transactions on Knowledge and Data Engineering (2023)

  24. Yin, L., Wang, L., Tian, J., Yin, Z., Liu, M., … Zheng, W: Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer. Appl. Sci. 13(6) (2023)

  25. Liu, Y., Tian, J., Zheng, W., Yin, L.: Spatial and temporal distribution characteristics of haze and pollution particles in China based on spatial statistics. Urban climate 41, 101031 (2022)

    Article  Google Scholar 

  26. Tian, J., Liu, Y., Zheng, W., & Yin, L: Smog prediction based on the deep belief - BP neural network model (DBN-BP). Urban Clim. (2021)

  27. Wang, S., Sheng, H., Zhang, Y., Yang, D., Shen, J., … Chen, R. Blockchain-Empowered Distributed Multi-Camera Multi-Target Tracking in Edge Computing. IEEE Transact. Industr. Inform. (2023)

  28. Wu, H.; Judd, P.; Zhang, X.; Isaev, M.; Micikevicius, P: Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation, arXiv:2004.09602 (2020)

  29. Peng, P., You, M., Xu, W., Li, J.: Fully integer-based quantization for mobile convolutional neural network inference. Neurocomputing 432, 194–205 (2021)

    Article  Google Scholar 

  30. Li, J., Alvarez, R: On the quantization of recurrent neural networks, arXiv:2101.05453 (2021)

  31. Benhaddi, M., Ouarzazi, J.: ‘Multivariate time series forecasting with dilated residual convolutional neural networks for urban air quality prediction.’ Arabian J. Sci. Eng. 46(4), 3423–3442 (2021)

    Article  Google Scholar 

  32. X. Song, J. Huang, and D. Song: ‘‘Air quality prediction based on LSTM-Kalman model,’’ in Proc. IEEE 8th Joint Int. Inf. Technol. Artif. Intell. Conf. (ITAIC), Chongqing, China, May, pp. 695–699 (2019)

  33. Wang, J., Li, J., Wang, X., Wang, J., Huang, M.: ‘Air quality prediction using CT-LSTM.’ Neural Comput. Appl. 33(3), 1–14 (2020)

    Google Scholar 

  34. Jianhui, Z., Ting, D., Bo, C.: ‘AQI prediction based on long short- term memory model with spatio-temporal optimizations and fireworks algorithm.’ J. Wuhan Univ. 65(3), 250–262 (2019)

    MATH  Google Scholar 

  35. Qin, D., Yu, J., Zou, G., Yong, R., Zhao, Q., Zhang, B.: ‘A novel combined prediction scheme based on CNN and LSTM for urban PM2.5 concentration.’ IEEE Access 7, 20050–20059 (2019)

    Article  Google Scholar 

  36. Li, S., Xie, G., Ren, J., Guo, L., Yang, Y., Xu, X.: ‘Urban PM2.5 concentration prediction via attention-based CNN–LSTM.’ Appl. Sci. 10(6), 1953 (2020)

    Article  Google Scholar 

  37. Zhang, L., Liu, P., Zhao, L., Wang, G., Zhang, W., Liu, J.: ‘Air quality predictions with a semi-supervised bidirectional LSTM neural network.’ Atmos. Pollut. Res. 12(1), 328–339 (2021)

    Article  Google Scholar 

  38. Belavadi, S.V., Rajagopal, S., Ranjani, R., Mohan, R.: Air quality forecasting using LSTM RNN and wireless sensor networks. Procedia Comput. Sci. 170, 241–248 (2020)

    Article  Google Scholar 

  39. X. Sun, W. Xu, H. Jiang: Spatio-temporal prediction of air quality based on recurrent neural networks. in Proc. 52nd Hawaii Int. Conf. Syst. Sci. (HICSS) (2019)

  40. Zou, X., Zhao, J., Zhao, D., Sun, B., He, Y., Fuentes, S.: ‘Air quality prediction based on a spatiotemporal attention mechanism.’ Mobile Inf. Syst. 2021, 1–12 (2021)

    Google Scholar 

  41. Qin, Z., Cen, C., Guo, X.: ‘Prediction of air quality based on KNN- LSTM.’ J. Phys. Conf. 1237(Jun), 042030 (2019)

    Article  Google Scholar 

  42. Zhao, J., Deng, F., Cai, Y., Chen, J.: ‘Long short-term memory—Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction.’ Chemosphere 220, 486–492 (2019)

    Article  Google Scholar 

  43. Meisami, S., Beheshti-Atashgah, M., Aref, M.R.: “Using blockchain to achieve decentralized privacy in IoT healthcare” arXiv preprint arXiv:2109.14812 (2021)

  44. Bozorgkhou, H., Rokni, M.A.: Studying and investigating the impact of marketing mix factors on e-purchase via smart phones (case study: Digikala corporation). Nexo Revista Científica 35(04), 992–1003 (2022)

    Article  Google Scholar 

  45. Abedi, M., Tan, X., Klausner, J.F., Murillo, M.S. and Benard, A.: A Comparison of the Performance of a Data-Driven Surrogate Model of a Dehumidifier with Mathematical Model of Humidification-Dehumidification System. In AIAA SCITECH 2023 Forum (p. 2329) (2023)

  46. Wang, X., Wang, Y., Javaheri, Z., Almutairi, L., Moghadamnejad, N., Younes, O.S.: Federated deep learning for anomaly detection in the internet of things. Comput. Electr. Eng. 108, 108651 (2023)

    Article  Google Scholar 

  47. Chiniforooshan Esfahani, I.: A data-driven physics-informed neural network for predicting the viscosity of nanofluids. AIP Adv. 13(2), 025206 (2023)

    Article  Google Scholar 

  48. Dai, X., Xiao, Z., Jiang, H., Alazab, M., Lui, J. C. S., Dustdar, S.,... Liu, J, Task Co-Offloading for D2D-Assisted Mobile Edge Computing in Industrial Internet of Things. IEEE Transact. Industr. Inform. 19(1), 480–490 (2023)

  49. Jiang, H., Dai, X., Xiao, Z., Iyengar, A. K.: Joint Task Offloading and Resource Allocation for Energy-Constrained Mobile Edge Computing. IEEE Transactions on Mobile Computing (2022)

  50. Dai, X., Xiao, Z., Jiang, H., Lui, J. C. S: UAV-Assisted Task Offloading in Vehicular Edge Computing Networks. IEEE Transactions on Mobile Computing (2023)

  51. Ren, Y., Jiang, H., Ji, N., Yu, H.: TBSM: A traffic burst-sensitive model for short-term prediction under special events. Knowl.-Based Syst. 240, 108120 (2022)

    Article  Google Scholar 

  52. Liu, H., Yuan, H., Hou, J., Hamzaoui, R., Gao, W.: PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling. IEEE Transact Image Process 31, 7389–740 (2022)

    Article  Google Scholar 

  53. Huang, C., Jiang, F., Huang, Q., Wang, X., Han, Z., … Huang, W, Dual-Graph Attention Convolution Network for 3-D Point Cloud Classification. IEEE Transactions on Neural Networks and Learning Systems, 1–13 (2022)

  54. Ni, Q., Guo, J., Wu, W., Wang, H., Wu, J.: Continuous Influence-Based Community Partition for Social Networks. IEEE Transact Network Sci Eng 9(3), 1187–1197 (2022)

    Article  MathSciNet  Google Scholar 

  55. Xiong, Z., Li, X., Zhang, X., Deng, M., Xu, F., Zhou, B., … Zeng, M, A Comprehensive Confirmation-based Selfish Node Detection Algorithm for Socially Aware Networks. J Signal Process Syst (2023)

  56. Lv, Z., Yu, Z., Xie, S., & Alamri, A.: Deep Learning-Based Smart Predictive Evaluation for Interactive Multimedia-Enabled Smart Healthcare. ACM Trans. Multimedia Comput. Commun. Appl. 18(1s) (2022)

  57. Liu, Z., Feng, J., Uden, L.: From technology opportunities to ideas generation via cross-cutting patent analysis: Application of generative topographic mapping and link prediction. Technol. Forecast. Soc. Chang. 192, 122565 (2023)

    Article  Google Scholar 

  58. Wang, H., Cui, Z., Liu, R., Fang, L., & Sha, Y, A Multi-type Transferable Method for Missing Link Prediction in Heterogeneous Social Networks. IEEE Transact. Knowledge Data Eng (2023)

  59. Singh, R., Gill, S.: S, Edge AI: A survey. Internet Things Cyber-Phys Syst 3, 71–92 (2023)

    Article  Google Scholar 

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Acknowledgements

For Samia Elattar, Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R163), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Besides, Badr S. Alotaibi is thankful to the Deanship of Scientific Research at Najran University for funding this work, under the Research Groups Funding program grant code (NU/RG/SERC/12/1).

Funding

This research received no specific grant from any funding agency.

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Authors and Affiliations

  1. School of Economics, Shanghai University, Shanghai, 201900, China

    Changyuan Sun

  2. Division for Integration of Industry and Education, Shanghai Publishing And Printing College, Shanghai, 200082, China

    Jingjing Li

  3. Institute of IR4.0, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia

    Riza Sulaiman

  4. Architectural Engineering Department, Najran University, Najran, Saudi Arabia

    Badr S. Alotaibi & Mohammed Abuhussain

  5. Department of Industrial & Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia

    Samia Elattar

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  1. Changyuan Sun
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  2. Jingjing Li
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  3. Riza Sulaiman
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  4. Badr S. Alotaibi
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  5. Samia Elattar
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Contributions

Changyuan Sun: Conceptualization, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing.

Jingjing Li: Writing—original draft, Writing—review & editing.

Riza Sulaiman: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Badr S Alotaibi: Project administration, Investigation, Writing—review & editing.

Samia Elattar: Software, Visualization, Writing—original draft.

Mohammed Abuhussain: Software, Visualization, Writing—original draft.

Corresponding author

Correspondence to Changyuan Sun.

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Sun, C., Li, J., Sulaiman, R. et al. Air Quality Prediction and Multi-Task Offloading based on Deep Learning Methods in Edge Computing. J Grid Computing 21, 32 (2023). https://doi.org/10.1007/s10723-023-09671-0

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