Title: A dynamic data driven indoor localisation framework based on ultra high frequency passive RFID system
Authors: Bijoy Dripta Barua Chowdhury; Sara Masoud; Young-Jun Son; Chieri Kubota; Russell Tronstad
Addresses: Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA ' Department of Industrial and Systems Engineering, The Wayne State University, Detroit, MI 48202, USA ' Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA ' Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH 43210, USA ' Department of Agricultural and Resource Economics, The University of Arizona, Tucson, AZ 85721, USA
Abstract: Better monitoring of workers' and the materials' flow within a production system can potentially enhance any facility's productivity and efficiency. This paper proposes a data driven framework to affordably localise indoor workers and materials using a passive radio frequency identification (RFID) system in large scale. Here, indoor wireless sensor networks are developed via passive Ultra-High Frequency (UHF) tags, where received signal strength indicator (RSSI) is measured by different access points (APs) to generate a fingerprinting database. Then, this database not only translates the signal strength reported by APs to distance through regression models but also helps to localise each tag utilising our proposed k-nearest neighbours (KNN) algorithm. Our improved KNN algorithm dynamically defines different neighbourhoods, in terms of size and topology considering environment status. Results from multiple experiments under different scenarios reveal that our proposed methods can detect and localise objects with an error as low as 0.36 m.
Keywords: RFID; radio frequency identification; passive UHF RFID; WSNs; wireless sensor networks; RFID-sensor networks; distance estimation; localisation; KNN; k-nearest neighbours; dynamic data driven; machine learning; statistical modelling.
International Journal of Sensor Networks, 2020 Vol.34 No.3, pp.172 - 187
Received: 02 Mar 2020
Accepted: 05 Mar 2020
Published online: 26 Oct 2020 *