Enhancing Cluster-based RFID Tag Localization using artificial neural networks and virtual reference tags

Abstract

Abstract Construction sites are changing every day, which brings some difficulties for different contractors to do their tasks properly. One of the key points for all entities who work on the same site is the location of resources including materials, tools, and equipment. Therefore, the lack of an integrated localization system leads to an increase in the time wasted on searching for resources. In this research, a localization method based on Radio Frequency Identification (RFID) systems which does not need infrastructure is proposed to overcome this problem. This paper investigates the usage of active RFID technology for the localization of movable objects (e.g. components, equipment, and tools) equipped with RFID tags using handheld readers by extending a Cluster-based Movable Tag Localization (CMTL) technique which uses a k-Nearest Neighbor (k-NN) algorithm. CMTL uses a multidimensional clustering technique that considers signal pattern similarity between target and reference tags together with spatial distribution of reference tags for detecting the region where the target tag is located. The paper proposes applying an irregular bilinear interpolation method to form a grid of virtual reference tags within the selected cluster of real reference tags. Moreover, the proposed method uses artificial neural networks (ANNs) for positioning the target tag, as opposed to empirical weighted averaging formulas used in similar k-NN based methods. Comparative analysis is performed to quantify the improvement of the proposed method over similar k-NN-based methods using a simulation environment. A case study is performed to analyze the performance of the proposed method.