A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things


Accurate cellular traffic prediction becomes more and more critical for efficient network resource management in the Internet of Things (IoT). However, high-accuracy prediction results are usually accompanied by high computational capacity requirements. Although many lightweight neural network models have been proposed, some lightweight mechanisms will easily destroy the features of the raw data. Not all lightweight mechanisms are suitable for network traffic prediction. Therefore, this study proposes and optimizes an input data conversion method to extract the features of spatio-temporal dependencies based on convolutional neural network (CNN) architecture. In addition, we also propose a lightweight neural network model to reduce the computational cost for cellular traffic prediction problem and use mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) to evaluate the prediction accuracy. The experimental results show that the proposed model is better than CNN, ConvLstm, and Densenet as well as can greatly reduce the parameters of the neural network while maintaining prediction accuracy.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    Lai CF, Chien WC, Yang LT, Qiang W (2019) Lstm and edge computing for big data feature recognition of industrial electrical equipment. IEEE Trans Industr Inf 15(4):2469–2477

    Article  Google Scholar 

  2. 2.

    Chien WC, Lai CF, Chao HC (2019) Dynamic resource prediction and allocation in c-ran with edge artificial intelligence. IEEE Trans Industr Inf 15(7):4306–4314

    Article  Google Scholar 

  3. 3.

    Xu F, Lin Y, Huang J, Wu D, Shi H, Song J, Li Y (2016) Big data driven mobile traffic understanding and forecasting: A time series approach. IEEE Trans Serv Comput 9(5):796–805

    Article  Google Scholar 

  4. 4.

    Chien WC, Weng HY, Lai CF, Fan Z, Chao HC, Hu Y (2019) A sfc-based access point switching mechanism for software-defined wireless network in iov. Future Gener Comput Syst 98:577–585

    Article  Google Scholar 

  5. 5.

    Chien WC, Lai CF, Cho HH, Chao HC (2018) A sdn-sfc-based service-oriented load balancing for the iot applications. J Netw Comput Appl 114:88–97

    Article  Google Scholar 

  6. 6.

    Herrera JG, Botero JF (2016) Resource allocation in nfv: A comprehensive survey. IEEE Trans Netw Serv Manage 13(3):518–532

    Article  Google Scholar 

  7. 7.

    Wang L, Lu Z, Wen X, Knopp R, Gupta R (2016) Joint optimization of service function chaining and resource allocation in network function virtualization. IEEE Access 4:8084–8094

    Article  Google Scholar 

  8. 8.

    Wang C, Liang C, Yu FR, Chen Q, Tang L (2017) Computation offloading and resource allocation in wireless cellular networks with mobile edgecomputing. IEEE Trans Wireless Commun 16(8):4924–4938

    Article  Google Scholar 

  9. 9.

    Wang X, Zhou Z, Xiao F, Xing K, Yang Z, Liu Y, Peng C (2018) Spatio-temporal analysis and prediction of cellular traffic in metropolis. IEEE Trans Mob Comput 18(9):2190–2202

    Article  Google Scholar 

  10. 10.

    Tang F, Fadlullah ZM, Mao B, Kato N (2018) An intelligent traffic load prediction-based adaptive channel assignment algorithm in sdn-iot: A deep learning approach. IEEE Internet Things J 5(6):5141–5154

    Article  Google Scholar 

  11. 11.

    Azari A, Papapetrou P, Denic S, Peters G (2019) Cellular traffic prediction and classification: a comparative evaluation of lstm and arima. In: International conference on discovery science, Springer, pp 129–144

  12. 12.

    Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin

    Google Scholar 

  13. 13.

    Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  14. 14.

    Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl. https://doi.org/10.1007/s11042-020-10255-3

    Article  Google Scholar 

  15. 15.

    Barlacchi G, De Nadai M, Larcher R, Casella A, Chitic C, Torrisi G, Antonelli F, Vespignani A, Pentland A, Lepri B (2015) A multi-source dataset of urban life in the city of milan and the province of trentino. Scientific data 2(1):1–15

    Article  Google Scholar 

  16. 16.

    Trinh HD, Giupponi L, Dini P (2018) Mobile traffic prediction from raw data using lstm networks. 2018 IEEE 29th Annual International Symposium on Personal. Indoor and Mobile Radio Communications (PIMRC), IEEE, pp 1827–1832

    Google Scholar 

  17. 17.

    Dalgkitsis A, Louta M, Karetsos GT (2018) Traffic forecasting in cellular networks using the lstm rnn. In: Proceedings of the 22nd Pan-Hellenic Conference on Informatics, pp 28–33

  18. 18.

    Shiang EPL, Chien WC, Lai CF, Chao HC (2020) Gated recurrent unit network-based cellular trafile prediction. In: 2020 International Conference on Information Networking (ICOIN), IEEE, pp 471–476

  19. 19.

    Abdellah AR, Mahmood OAK, Paramonov A, Koucheryavy A (2019) Iot traffic prediction using multi-step ahead prediction with neural network. In: 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), IEEE, pp 1–4

  20. 20.

    Zhang C, Zhang H, Yuan D, Zhang M (2018) Citywide cellular traffic prediction based on densely connected convolutional neural networks. IEEE Commun Lett 22(8):1656–1659

    MathSciNet  Article  Google Scholar 

  21. 21.

    Qiu C, Zhang Y, Feng Z, Zhang P, Cui S (2018) Spatio-temporal wireless traffic prediction with recurrent neural network. IEEE Wireless Commun Lett 7(4):554–557

    Article  Google Scholar 

  22. 22.

    Wang J, Tang J, Xu Z, Wang Y, Xue G, Zhang X, Yang D (2017) Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, pp 1–9

  23. 23.

    Zhang C, Patras P (2018) Long-term mobile traffic forecasting using deep spatio-temporal neural networks. In: Proceedings of the Eighteenth acm International Symposium on Mobile Ad Hoc Networking and Computing, pp 231–240

  24. 24.

    Lopez-Martin M, Carro B, Sanchez-Esguevillas A (2019) Neural network architecture based on gradient boosting for iot traffic prediction. Future Gener Comput Syst 100:656–673

    Article  Google Scholar 

  25. 25.

    Zhang C, Zhang H, Qiao J, Yuan D, Zhang M (2019) Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data. IEEE J Sel Areas Commun 37(6):1389–1401

    Article  Google Scholar 

  26. 26.

    Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1314–1324

Download references

Author information



Corresponding author

Correspondence to Yueh-Min Huang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chien, WC., Huang, YM. A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things. J Supercomput (2021). https://doi.org/10.1007/s11227-021-03662-2

Download citation


  • Convolutional neural network
  • Cellular traffic prediction
  • Internet of things
  • Lightweight neural network model