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Conference Papers Year : 2020

Indoor Localization with Multi-objective Selection of Radiomap Models

Rafael Alexandrou
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Harris Papadopoulos
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Andreas Konstantinidis
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Abstract

Over the last years, Indoor Localization Systems (ILS) evolved, due to the inability of Global Positioning Systems (GPS) to localize in indoor environments. A variety of studies tackle indoor localization with technologies such as Bluetooth Beacons and RFID that require costly installation, or techniques such as Google Wi-Fi/Cell DB and fingerprinting that leverage from the already existing Wi-FI and telecommunication infrastructure. Additionally, recent studies attempt to solve the same problem using Bio-Inspired techniques, such as Artificial Neural Networks (ANNs) and Deep Neural Networks (DNN). In this paper, we introduce a Multi-Objective Optimization Radiomap Modelling (MOO-RM) based ILS. The MOO-RM ILS divides the dataset into clusters using a K-Means algorithm and trains ANN models on the data of each cluster. The resulting models are fed into a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which minimizes the required storage space and the localization error, simultaneously. Our experimental studies demonstrate the superiority of the proposed approach on real datasets of Wi-Fi traces with respect to various existing techniques.
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hal-04050615 , version 1 (29-03-2023)

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Rafael Alexandrou, Harris Papadopoulos, Andreas Konstantinidis. Indoor Localization with Multi-objective Selection of Radiomap Models. 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2020, Neos Marmaras, Greece. pp.267-278, ⟨10.1007/978-3-030-49161-1_23⟩. ⟨hal-04050615⟩
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