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Bohn, J. (2008) Prototypical Implementation of Location Aware Services Based on a Middleware Architecture for Super-Distributed RFID Tag Infrastructures. Personal and Ubiquitous Computing, 12, 155-166. https://doi.org/10.1007/s00779-006-0107-2

has been cited by the following article:

  • TITLE: An Application of Kalman Filtering and Artificial Neural Network with K-NN Position Detection Technique

    AUTHORS: Hakan Koyuncu, Baki Koyuncu

    KEYWORDS: RFID, RSSI, WSN, ANN, Kalman Filtering, MLP, SDE, AP

    JOURNAL NAME: Wireless Sensor Network, Vol.9 No.8, August 10, 2017

    ABSTRACT: RFID technology is one of the important technologies to determine the object locations. Distances are calculated with respect to calibration curves of RSSI amplitudes. The aim of this study is to determine the 2D position of mobile objects in the indoor environment. The importance of the work is to show that localization by using Artificial Neural Network plus Kalman Filtering is more accurate than using classical KNN method. An indoor wireless sensing network is established with strategically stationed RFID transmitter nodes and a mobile object with a RFID receiver node. A fingerprint map is generated and K-Nearest Neighbourhood algorithm (KNN) is deployed to calculate the object locations. Fingerprint coordinates and RSS values received at these coordinates are deployed to set up an Artificial Neural Network (ANN). This network is used to determine the unknown object locations by using RSS values received at these locations. The accuracy of object localization is found to be better with ANN technique than KNN technique. Object coordinates, determined with ANN technique, are subjected to Kalman filtering. The results show that localization accuracies are improved and localization error distances are reduced by 46% with the deployment of ANN + Kalman Filtering.