Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Journal of the ACM (JACM)
Discovering significant places from mobile phones: a mass market solution
MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
Discovering human places of interest from multimodal mobile phone data
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Energy-efficient positioning for smartphones using Cell-ID sequence matching
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
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In this work we investigate the limits of crowdsensing in discovering the mapping of mobile network Cell-IDs to geographic locations. We employ original large-scale mobility simulations, derived using the NRC-Lausanne dataset, to determine the fraction of cells visited by a fixed number of users over a time interval. This is vital to judge the ability of crowdsensing to rapidly update an inadequate, malfunctioning or obsolete Cell-ID database, thus preventing mechanisms such as Dynamic Cell-ID from obfuscating the network. We show that crowdsensing is quite a powerful tool, with for example only 25% more users than cells sufficing to scan 99% of the network over a day.