A Novel Algorithm to Improve Indoor Localization Accuracy and Path-Loss Reduction Using Real Time RSSI

Abstract

Distance estimation is vital for localization and many other applications in wireless sensor networks. Research has revealed that the correlation between distance and RSSI (Received Signal Strength Indication) values is the key of ranging and localization technologies in wireless sensor networks (WSNs). Distance measurement based on RSSI (Received Signal Strength Indication) is a low cost and low complexity of the distance measurement technique, and it is widely applied in the range-based localization of the WSN. In this paper, an Log Normal Shadowing Model (LNSM) that estimates the distance between sensor nodes in WSNs is presented. The performance of this model and proposed method are evaluated and analyzed in an indoor environment with and without considering obstacles by performing an empirical measurement using Crossbow TelosB wireless sensor motes. Our result shows that the proposed method reduced distance error and path-loss effectively than LNSM. The results of these evaluations would contribute towards obtaining accurate locations of wireless sensor nodes.