Spatial Interpolation based Cellular Coverage Prediction with Crowdsourced Measurements

Massimiliano Molinari; Mah-Rukh  Fida; Mahesh Marina; Antonio Pescape

doi:10.1145/2787394.2787395

Spatial Interpolation based Cellular Coverage Prediction with Crowdsourced Measurements

Massimiliano Molinari, Mah-Rukh Fida, Mahesh Marina, Antonio Pescape

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Coverage prediction has always been of great concern for mobile network operators. Yet the prevalent approach using analytical models assisted by drive testing based measurements is inherently inaccurate and expensive. We consider a promising alternative for coverage mapping involving crowdsourced measurements and spatial interpolation. In particular, we empirically study the accuracy of wide range of spatial interpolation techniques in different scenarios that capture the unique characteristics of crowdsourced measurements (inaccurate locations, sparse and non-uniform measurements, etc.), and find ordinary kriging to be a fairly robust technique.

Original language	English
Title of host publication	ACM SIGCOMM Workshop on Crowdsourcing and crowdsharing of Big (Internet) Data (C2B(I)D)
Subtitle of host publication	Co-located with ACM SIGCOMM’ 15
Publisher	ACM
Number of pages	6
DOIs	https://doi.org/10.1145/2787394.2787395
Publication status	Published - 17 Aug 2015

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Massimilliano et al Spatial InterpolationAccepted author manuscript, 277 KB

http://conferences.sigcomm.org/sigcomm/2015/pdf/papers/c2bid/p33.pdf

Cite this

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title = "Spatial Interpolation based Cellular Coverage Prediction with Crowdsourced Measurements",

abstract = "Coverage prediction has always been of great concern for mobile network operators. Yet the prevalent approach using analytical models assisted by drive testing based measurements is inherently inaccurate and expensive. We consider a promising alternative for coverage mapping involving crowdsourced measurements and spatial interpolation. In particular, we empirically study the accuracy of wide range of spatial interpolation techniques in different scenarios that capture the unique characteristics of crowdsourced measurements (inaccurate locations, sparse and non-uniform measurements, etc.), and find ordinary kriging to be a fairly robust technique.",

author = "Massimiliano Molinari and Mah-Rukh Fida and Mahesh Marina and Antonio Pescape",

note = "Date of Acceptance:10/05/2015",

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Spatial Interpolation based Cellular Coverage Prediction with Crowdsourced Measurements. / Molinari, Massimiliano; Fida, Mah-Rukh; Marina, Mahesh et al.

ACM SIGCOMM Workshop on Crowdsourcing and crowdsharing of Big (Internet) Data (C2B(I)D): Co-located with ACM SIGCOMM’ 15. ACM, 2015.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Spatial Interpolation based Cellular Coverage Prediction with Crowdsourced Measurements

AU - Molinari, Massimiliano

AU - Fida, Mah-Rukh

AU - Marina, Mahesh

AU - Pescape, Antonio

N1 - Date of Acceptance:10/05/2015

PY - 2015/8/17

Y1 - 2015/8/17

N2 - Coverage prediction has always been of great concern for mobile network operators. Yet the prevalent approach using analytical models assisted by drive testing based measurements is inherently inaccurate and expensive. We consider a promising alternative for coverage mapping involving crowdsourced measurements and spatial interpolation. In particular, we empirically study the accuracy of wide range of spatial interpolation techniques in different scenarios that capture the unique characteristics of crowdsourced measurements (inaccurate locations, sparse and non-uniform measurements, etc.), and find ordinary kriging to be a fairly robust technique.

AB - Coverage prediction has always been of great concern for mobile network operators. Yet the prevalent approach using analytical models assisted by drive testing based measurements is inherently inaccurate and expensive. We consider a promising alternative for coverage mapping involving crowdsourced measurements and spatial interpolation. In particular, we empirically study the accuracy of wide range of spatial interpolation techniques in different scenarios that capture the unique characteristics of crowdsourced measurements (inaccurate locations, sparse and non-uniform measurements, etc.), and find ordinary kriging to be a fairly robust technique.

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DO - 10.1145/2787394.2787395

M3 - Conference contribution

BT - ACM SIGCOMM Workshop on Crowdsourcing and crowdsharing of Big (Internet) Data (C2B(I)D)

PB - ACM

ER -

Spatial Interpolation based Cellular Coverage Prediction with Crowdsourced Measurements

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