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.
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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
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
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
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
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
Herrera JG, Botero JF (2016) Resource allocation in nfv: A comprehensive survey. IEEE Trans Netw Serv Manage 13(3):518–532
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
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
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
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
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
Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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
- Convolutional neural network
- Cellular traffic prediction
- Internet of things
- Lightweight neural network model