Fingerprint-based technique for indoor localization in wireless sensor networks using Fuzzy C-Means clustering algorithm

ZigBee as IEEE 802.15.4 standard has been using for main research topic in the wireless sensor network (WSN) applications. The new method of radio frequency (RF) fingerprint-based technique for indoor localization is proposed. The received signal strength indicator (RSSI) is used as database values which correspond to the location of the sensor nodes. Fuzzy C-Means (FCM) clustering algorithm is applied as the experiment data cluster method. FCM algorithm is deployed to cluster the obtained feature vectors into several classes corresponding to the different amount of RSSI values. The results show that FCM can cluster the target node in a group of the fingerprint database. The location of target node is arranged in various forms to validate the accuracy of the clustering technique. Euclidean distance is used as the parameter to compare the similarity between fingerprint database and the target location. The results show that the new method is simple and effective method to reduce the complexity and to support the low power and to reduce the time using in the fingerprint-based localization technique.