2020
DOI: 10.1109/lwc.2019.2953848
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On-Line Estimation of Base Station Location

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Cited by 2 publications
(2 citation statements)
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“…To extrapolate the components that follow (1) from the measured RSS, we propose the use of a Kalman filter. Kalman filters have been widely used in literature to improve the reliability of RSS-based localization [65]. The filter's parameters are usually in the form of matrices resulting in a computational complexity higher than O(N 2 ) [66].…”
Section: A Mobility-enhanced Proximity Estimationmentioning
confidence: 99%
“…To extrapolate the components that follow (1) from the measured RSS, we propose the use of a Kalman filter. Kalman filters have been widely used in literature to improve the reliability of RSS-based localization [65]. The filter's parameters are usually in the form of matrices resulting in a computational complexity higher than O(N 2 ) [66].…”
Section: A Mobility-enhanced Proximity Estimationmentioning
confidence: 99%
“…Instead of relying on a single RSSI measurement, we propose taking multiple RSSI measurements that, thanks to they mobility of the authenticating entity, are unpredictable by an attacker. To the best of our knowledge, this approach has only been reported once in the literature so far in the recent paper [23], where the authors leverage the mobility of a mobile handset to localize a base station using unscented Kalman filters with the time of arrival (ToA) and angle of arrival (AoA) information as inputs. Our proposed proximity estimator is much simpler and faster as it relies solely on RSSI measurements and uses a simple Kalman filter with scalar inputs, so it is an attractive approach for inverse proximity estimation in IoT networks.…”
Section: Related Work and Contributionsmentioning
confidence: 99%