Dealing with network changes in cellular fingerprint positioning systems

Viel, Andrea; Gallo, Paolo; Montanari, Angelo; Gubiani, Donatella; Torre, Andrea Dalla; Pittino, Federico; Marshall, C.

doi:10.1109/icl-gnss.2017.8376255

Cited by 3 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example, in WiFi fingerprinting, access points may be added, removed, or replaced. As for cellular data, mobile cells can be merged or relocated [30]. In addition, new wireless technologies are continuously being developed.…”

Section: Motivations and Challengesmentioning

confidence: 99%

A Framework for Indoor Positioning Including Building Topology

2022

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Motivations and Challengesmentioning

confidence: 99%

A Framework for Indoor Positioning Including Building Topology

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Indeed, a number of reconfigurations can happen over time. For instance, in [ 15 ], the authors considered a cellular fingerprint dataset composed of 785,000 GSM observations collected in 2 years over a wide area of 1,800,000 km

, finding 198 instances of cells that had changed one or more of their identifiers. The phenomenon is named cell renaming—that is, a change, even a partial one, of the cell identifier (e.g., the LAC parameter for a group of cells).…”

Section: An Overview Of the Cellular Networkmentioning

confidence: 99%

“…The phenomenon would be accentuated in areas with a low cell density, such as rural regions. In [ 15 ], it is shown how accounting for such network changes allows one to increase the average number of candidates available for each position estimation by

. In the case of relocation, the problem would be an inconsistency of the data, which would again result in a positioning error, potentially of high magnitude, being closely related to the spatial displacement affecting the cell.…”

Section: An Overview Of the Cellular Networkmentioning

confidence: 99%

“…An example of a periodic check is as follows: if a cell has enough associated observations, it can be determined whether the (possibly estimated) locations of its latest observations (that is, those entered after the last launch of this periodic check) are consistent with the coverage of the cell—i.e., if the geometric distances of the locations with respect to the previously known cell extension are plausible (e.g., not too large). Finally, the detection of cell renaming phenomena has also been implemented as a periodic routine: briefly, on the basis of [ 15 ], we verified whether several (spatio-temporal) conditions characterizing the renaming phenomenon are satisfied. Ideally, periodic checks should complement and be run in parallel with continuous ones.…”

Section: Cellular Network Reconstruction and Validationmentioning

confidence: 99%

“…The task is exacerbated due to several characteristics of such a network being time-dependent, i.e., they undergo continuous changes. For instance, a cell may be created o removed, merged with another, or even spatially relocated [ 15 ]. Additionally, this continuous network evolution is taken into account and effectively managed by our proposed system, which overall demonstrates how a deep knowledge of the cellular network arrangement can be achieved and maintained based only on crowdsourced information.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Brunello

Torre

Gallo

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only.

show abstract