Topology-Based Evaluation for Symbolic Indoor Positioning Algorithms

Smart factories require location aware services such as asset tracking. These location aware services should be based on indoor positioning systems. Symbolic indoor positioning is considered as a classification task, where each category denotes a well-defined part of the building, such as a room or...

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Bibliographic Details
Published in:IEEE transactions on industry applications 2019-11, Vol.55 (6), p.6324-6331
Main Authors: Tamas, Judit, Toth, Zsolt
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Buildings
Classification
Classifiers
Evaluation
Gravity
Indoor Geographic Markup Language (IndoorGML)
indoor positioning
Industrial plants
IP networks
location awareness
Location based services
Position (location)
Shape
Smart manufacturing
Solid modeling
Topology
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Summary:Smart factories require location aware services such as asset tracking. These location aware services should be based on indoor positioning systems. Symbolic indoor positioning is considered as a classification task, where each category denotes a well-defined part of the building, such as a room or corridor. Hence, standard classifiers can be applied to symbolic indoor positioning. A topology-based classification evaluation method is presented that calculates the classification error based on the gravitational force between the symbolic positions denoted by categories. Three variants of the proposed topology-based method is evaluated and compared to the CRISP approach. The comparison was performed over a dataset recorded in a three-storey building whose topology is given in Indoor Geographic Markup Language format. Experimental results showed that the topology-based method gives a more detailed comparison of classifiers for indoor positioning than CRISP.
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2019.2928489