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An anti-interference detection algorithm for parking monitoring systems

An anti-interference detection algorithm for parking monitoring systems Magnetic sensors have recently been proposed for parking occupancy detection. However, there has adjacent interference problem, i.e. the magnetic signal is easy to be interfered by the vehicles which are parking on adjacent spaces. The purpose of this paper is to propose a sensing algorithm to eliminate the adjacent interference.Design/methodology/approachThe magnetic signals are converted to the pattern representation sequences, and the similarity is calculated using the pattern distance. The detection algorithm includes two levels: local decision and data fusion. In the local decision level, the sampled signals can be divided into three classes: vacant, occupied and uncertain. Then a collaborative decision is used to fusion the signals which belong to the uncertain class for the second level.FindingsAn experiment system included 60 sensor nodes that were deployed on bay parking spaces. Experiment results show that the proposed algorithm has better detection accuracy than existing algorithms.Originality/valueThis paper proposes a data fusion algorithm to eliminate adjacent interference. To balance the energy consumption and detection accuracy, the algorithm includes two levels: local decision and data fusion. In most of cases, the local decision can obtain the accurate detection result. Only the signals that cannot be correctly detected at the local level need data fusion operation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

An anti-interference detection algorithm for parking monitoring systems

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1756-378X
DOI
10.1108/ijicc-04-2019-0034
Publisher site
See Article on Publisher Site

Abstract

Magnetic sensors have recently been proposed for parking occupancy detection. However, there has adjacent interference problem, i.e. the magnetic signal is easy to be interfered by the vehicles which are parking on adjacent spaces. The purpose of this paper is to propose a sensing algorithm to eliminate the adjacent interference.Design/methodology/approachThe magnetic signals are converted to the pattern representation sequences, and the similarity is calculated using the pattern distance. The detection algorithm includes two levels: local decision and data fusion. In the local decision level, the sampled signals can be divided into three classes: vacant, occupied and uncertain. Then a collaborative decision is used to fusion the signals which belong to the uncertain class for the second level.FindingsAn experiment system included 60 sensor nodes that were deployed on bay parking spaces. Experiment results show that the proposed algorithm has better detection accuracy than existing algorithms.Originality/valueThis paper proposes a data fusion algorithm to eliminate adjacent interference. To balance the energy consumption and detection accuracy, the algorithm includes two levels: local decision and data fusion. In most of cases, the local decision can obtain the accurate detection result. Only the signals that cannot be correctly detected at the local level need data fusion operation.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Oct 29, 2019

Keywords: Interference; Sensor networks; Data fusion; Parking detection

References