Abstract
A network planning method based on data mining was proposed for Random Phase Multiple Access (RPMA) low-power wide-area network (LPWAN) with large density of base stations and uneven traffic distribution. First, a signal quality prediction model was established by using the boosting regression trees algorithm, which was used to extract the coverage distribution spacial pattern of the network. Then, the weighted K-centroids clustering algorithm was utilized to obtain the optimal base station deployment for the current spacial pattern. Finally, according to the total objective function, the best base station topology was determined. Experimental results with the real data sets show that compared with the traditional network planning method, the proposed method can improve the coverage of low-power wide-area networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Patel, D., Won, M. Experimental study on low power wide area networks (LPWAN) for mobile internet of things. In: IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, pp. 1–5 (2017)
Hernandez, D.M, Peralta, G., Manero, L., et al.: Energy and coverage study of LPWAN schemes for industry 4.0. In: 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), Donostia-San Sebastian, pp. 1–6 (2017)
Xiong, X., Zheng, K., Xu, R., Xiang, W., Chatzimisios, P.: Low power wide area machine-to-machine networks: key techniques and prototype. Commun. Mag. IEEE 53(9), 64–71 (2015)
Krupka, L., Vojtech, L., Neruda, M.: The issue of LPWAN technology coexistence in IoT environment. In: 2016 17th International Conference on Mechatronics - Mechatronika (ME), Prague, pp. 1–8 (2016)
Wang, S., Zhao, W., Wang, C.: Budgeted cell planning for cellular networks with small cells. IEEE Trans. Veh. Technol. 64(10), 4797–4806 (2015)
Ghazzai, H., Yaacoub, E., Alouini, M.S., et al.: Optimized LTE cell planning with varying spatial and temporal user densities. IEEE Trans. Veh. Technol. 65(3), 1575–1589 (2016)
Yang, Z.H., Chen, M., Wen, Y.P., et al.: Cell Planning based on minimized power consumption for lte networks. In: IEEE Wireless Communications and NETWORKING Conference. IEEE (2016)
Wang, S., Ran, C.: Rethinking cellular network planning and optimization. IEEE Wirel. Commun. 23(2), 118–125 (2016)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Wen, R., Yan, W., Zhang, A.N.: Weighted clustering of spatial pattern for optimal logistics hub deployment. In: 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, pp. 3792–3797 (2016)
Kanungo, T., Mount, D.M., Netanyahu, N.S., et al.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Acknowledgements
This work was supported by National Science & Technology Key Project of China (2017ZX03001008), Natural Science Foundation of China (61871237), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0766) and Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJA510005).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Shen, Y., Zhu, X., Wang, Y. (2019). RPMA Low-Power Wide-Area Network Planning Method Basing on Data Mining. In: Zheng, J., Xiang, W., Lorenz, P., Mao, S., Yan, F. (eds) Ad Hoc Networks. ADHOCNETS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-05888-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-05888-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05887-6
Online ISBN: 978-3-030-05888-3
eBook Packages: Computer ScienceComputer Science (R0)