Bayesian estimate of position in mobile phone network

TL;DR

This paper introduces a Bayesian probabilistic method for mobile phone device positioning that accounts for antenna overlap, improving accuracy over traditional Voronoi-based approaches and highlighting privacy implications.

Contribution

It develops a Bayesian inference framework to better estimate device locations by considering antenna overlap and connection data, challenging traditional Voronoi-based methods.

Findings

Bayesian approach improves positioning accuracy.

Antenna overlap significantly affects device location estimates.

Privacy risks are heightened with probabilistic positioning methods.

Abstract

The traditional approach to mobile phone positioning is based on the assumption that the geographical location of a cell tower recorded in a call details record (CDR) is a proxy for a device's location. A Voronoi tessellation is then constructed based on the entire network of cell towers and this tessellation is considered as a coordinate system, with the device located in a Voronoi polygon of a cell tower that is recorded in the CDR. If Voronoi-based positioning is correct, the uniqueness of the device trajectory is very high, and the device can be identified based on 3-4 of its recorded locations. We propose and investigate a probabilistic approach to device positioning that is based on knowledge of each antennas' parameters and number of connections, as dependent on the distance to the antenna. The critical difference between the Voronoi-based and the real world layout is in the essential overlap of the antennas' service areas: the device that is located in a cell tower's polygon can be served by a more distant antenna that is chosen by the network system to balance the network load. This overlap is too significant to be ignored. Combining data on the distance distribution of the number of connections available for each antenna in the network, we succeed in resolving the overlap problem by applying Bayesian inference and construct a realistic distribution of the device location. Probabilistic device positioning demands a full revision of mobile phone data analysis, which we discuss with a focus on privacy risk estimates.