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.
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