Wireless big data in cellular networks: the cornerstone of smart cities. Issue 13 (29th June 2018)

• Record Type: Journal Article
• Authors: Li, Rongpeng; Zhao, Zhifeng; Yang, Chenyang; Wu, Chunming; Zhang, Honggang
• Is Part Of: IET communications. Volume 12:Issue 13(2018)
• Page Start: 1517
• Page End: 1523
• Publication Date: 2018-06-29
• Subjects: town and country planning -- telecommunication traffic -- data mining -- cellular radio; communication messages -- particular network operators -- cellular network operators -- human initiated service requests -- necessary infrastructure -- calibrating transmission protocols -- cellular network records -- human‐initiated messages -- network‐triggered responses -- urban cellular networks -- human mobility -- heavy‐tailed feature -- smarter cellular networks -- wireless big data -- smart cities -- preferential human settlement -- human activities -- information -- communications technology industry; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2017.1278
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : The rapid urbanisation has transformed cities to the preferential human settlement and allowed cities to quietly witness all range of human activities. As the key enabler in the information and communications technology industry, cellular networks play a decisive role in delivering communication messages and entertainment content. In particular, cellular network operators respond to human initiated service requests by gradually deploying necessary infrastructure and calibrating transmission protocols. Hence, cellular network records encompass the interesting interaction between human‐initiated messages and network‐triggered responses. In this study, the authors collect the 'big data' in urban cellular networks and try to dig out the human and urban planning properties. Specifically, they focus on the statistical modelling of three representative scenarios like spatial deployment density of base stations, packet length or traffic volume of mobile services, as well as inter‐arrival time and dwell time of human mobility. Through extensive data mining, they validate the heavy‐tailed feature universally existing in these scenarios. Afterwards, they discuss the implications of this heavy‐tailed feature and talk about its fundamental contribution to intelligent resource adjustment, proactive content caching, and enhanced connection management in cellular networks. Finally, they highlight the applications of this feature towards smarter cellular networks and cities.