Neural Network-based Indoor Localization in WSN Environments

Abstract

With the advancement of wireless technology even more wireless sensor network (WSN) applications are gaining ground. Their field of application is increasingly widening. This paper examines the WSN application which allows indoor localization based on the Fingerprint (FP) method. The communication between the modules was monitored during the experiment whereby the received radio signal strength indicator (RSSI) values from 5 modules were recorded by a mobile sensor. The received data was used for training of the feed-forward type of neural network. Through use of the trained neural network and the measured RSSI values an indoor localization was realized in a real environment. The neural network-based localization method is analyzed applying the cumulative distribution function (CDF). For the reference model the well-known weighted k-nearest neighbour (WkNN) method was used.