Abstract
As a special type of base station, Road Side Units (RSU) can be deployed at low cost and effectively alleviate the communication burden of regional Vehicular Ad-hoc Networks (VANETs). However, because of peak hour communication demands in VANETs and limited energy storage, it is necessary for RSU to adjust their participation in communication according to the requirements and allocate energy reasonably to balance the workload. Firstly, tidal traffic flow is generated according to the information of morning and evening peak in the city, so as to simulate the vehicle distribution around RSU on urban roads. Secondly, by inputting the historical information around RSU and the topological relationship between RSU, a network load prediction model is established by using the Attention based Spatial-Temporal Graph Convolutional Networks (ASTGCN) to predict the future communication load around RSU. Finally, according to the forecast of the future communication load, a RSU working mode alteration scheme is proposed with respect to the safety range amongst vehicles in order to control the corresponding area communication load. Compared with other models, our model has better accuracy and performance.
Supported by the Fundamental Research Funds for the Central Universities (GRANT NO. PA2021GDSK0095).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hartenstein, H., Laberteaux, K.P.: A tutorial survey on vehicular ad hoc networks. IEEE Commun. Mag. 46(6), 164–171 (2008)
Lee, E., Lee, E.K., Gerla, M., Oh, S.Y.: Vehicular cloud networking: architecture and design principles. IEEE Commun. Mag. 52(2), 148–155 (2014)
Dragicevic, T., Lu, X., Vasquez, J.C., Guerrero, J.M.: Dc microgrids-part ii: a review of power architectures, applications and standardization issues. IEEE Trans. Power Electron. 31(5), 1–1 (2015)
Barrachina, J., Garrido, P., Fogue, M., Martinez, F.: Road side unit deployment: a density-based approach. IEEE Intell. Transp. Syst. Mag. 5(3), 30–39 (2013)
Gao, Z., Chen, D., Cai, S., Wu, H.C.: Optdynlim: an optimal algorithm for the one-dimensional RSU deployment problem with nonuniform profit density. IEEE Trans. Industr. Inf. 15(2), 1052–1061 (2019)
Zhang, J., Wang, F.Y., Wang, K., Lin, W.H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)
Wen, C., Zheng, J.: An RSU on/off scheduling mechanism for energy efficiency in sparse vehicular networks. In: 2015 International Conference on Wireless Communications & Signal Processing (WCSP), pp. 1–5. IEEE (2015)
Patra, M., Murthy, C.S.R.: Performance evaluation of joint placement and sleep scheduling of grid-connected solar powered road side units in vehicular networks. IEEE Trans. Green Commun. Networking 2(4), 1197–1209 (2018)
Reyhanian, N., Maham, B., Shah-Mansouri, V., Tushar, W., Yuen, C.: Game-theoretic approaches for energy cooperation in energy harvesting small cell networks. IEEE Trans. Veh. Technol. 66(8), 7178–7194 (2017)
Zhang, D., et al.: Energy-harvesting-aided spectrum sensing and data transmission in heterogeneous cognitive radio sensor network. IEEE Trans. Veh. Technol. 66(1), 831–843 (2017)
Gupta, L., Jain, R., Vaszkun, G.: Survey of important issues in UAV communication networks. IEEE Commun. Surv. Tutorials 18(2), 1123–1152 (2016)
Liang, Y., Ke, S., Zhang, J., Yi, X., Zheng, Y.: Geoman: multi-level attention networks for geo-sensory time series prediction. In: IJCAI, vol. 2018, pp. 3428–3434 (2018)
Yao, H., Tang, X., Wei, H., Zheng, G., Yu, Y., Li, Z.: Modeling spatial-temporal dynamics for traffic prediction. arXiv preprint arXiv:1803.01254, pp. 922–929 (2018)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc. AAAI Conf. Artif. Intell. 33, 922–929 (2019)
Feng, X., Guo, J., Qin, B., Liu, T., Liu, Y.: Effective deep memory networks for distant supervised relation extraction. In: IJCAI, vol. 17, pp. 1–8 (2017)
Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–10 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning, December 2014, pp. 1–9 (2014)
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-Second AAAI Conference on Artificial Intelligence, pp. 1–8 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zheng, H., Ding, X., Wang, Y., Zhao, C. (2021). Attention Based Spatial-Temporal Graph Convolutional Networks for RSU Communication Load Forecasting. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-92635-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-92635-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92634-2
Online ISBN: 978-3-030-92635-9
eBook Packages: Computer ScienceComputer Science (R0)