Spatial-Temporal Distribution of Mobile Traffic and Base Station Clustering Based on Urban Function in Cellular Networks
With the rapid development of mobile internet, it’s essential to understand the spatial-temporal distribution of mobile traffic. Based on the mobile traffic data collected from a large 4G cellular network in northwestern China, this paper presents detailed analyses of the traffic data on base stations in two aspects: (1) spatial-temporal distribution, (2) clustering based on physical context, i.e., urban function. We introduce the concept of traffic density to measure the traffic level, according to the Voronoi diagram to partition the covering area of BSs. Both spatial and temporal dimensions show distinct inhomogeneity property of mobile traffic. Furthermore, we cluster BSs utilizing urban function information, which enables us to identify and label base stations. The diverse application usage patterns of each cluster of BSs are obtained, which could be applied in resource cache policy and BS loading allocation.
KeywordsSpatial-temporal distribution Mobile traffic BS clustering Urban function Application usage pattern
This work is supported by the National Science Foundation of China (NSFC) under grant 61571054, 61771065 and 61631005, by the New Star in Science and Technology of Beijing Municipal Science and Technology Commission (Beijing Nova Program: Z151100000315077).
- 1.Cisco visual networking index: Global Mobile Data Traffic Forecast Update, 2016–2021. https://www.cisco.com
- 2.Gotzner, U., Rathgeber, R.: Spatial trac distribution in cellular networks. In: Vehicular Technology Conference, VTC 1998, Ottawa, vol. 3, pp. 1994–1998 (1998)Google Scholar
- 3.Paul, U., Subramanian, A.P., Buddhikot, M.M., Das, S.R.: Understanding traffic dynamics in cellular data networks. In: 2011 Proceedings IEEE INFOCOM, Shanghai, pp. 882–890 (2011)Google Scholar
- 4.Cranshaw, J., Schwartz, R., Hong, J., Sadeh, N.: The livehoods project: utilizing social media to understand the dynamics of a city. Social Science Electronic Publishing (2012)Google Scholar
- 5.Xu, F., Zhang, P., Li, Y.: Context-aware real-time population estimation for metropolis. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing ACM, pp. 1064–1075 (2016)Google Scholar
- 6.Guruprasad, K.R.: Generalized Voronoi partition: a new tool for optimal placement of base stations. In: 2011 Fifth IEEE International Conference on Advanced Telecommunication Systems and Networks (ANTS), Bangalore, pp. 1–3 (2011)Google Scholar
- 9.Leng, B., Liu, J., Pan, H., Zhou, S., Niu, Z.: Topic model based behaviour modeling and clustering analysis for wireless network users. In: 2015 21st Asia-Pacific Conference on Communications (APCC), Kyoto, pp. 410–415 (2015)Google Scholar
- 10.Agarwal, S., Yadav, S., Singh, K.: Notice of violation of IEEE publication principles K-means versus k-means ++ clustering technique. In: 2012 Students Conference on Engineering and Systems, Allahabad, Uttar Pradesh, pp. 1–6 (2012)Google Scholar