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ANFIS-based Indoor localization and Tracking in Wireless Sensor Networking
Abstract
Localizing wireless sensor networks poses a persistent challenge in accurately determining sensor node locations based on known anchor node positions, especially when nodes move between different locations. Conventional techniques like Trilateration, relying on Received Signal Strength Indicators (RSSIs), frequently employed in Wireless Sensor Networks (WSNs), serve the purpose of localizing and tracking moving targets. However, the inherent nonlinear relationship between RSSI and distance often leads to substantial errors in localization estimations. This paper introduces an innovative approach by proposing the utilization of an Adaptive Neural Fuzzy Inference System (ANFIS) as a departure from the conventional RSSI-based method. This ANFIS-based approach aims to initially estimate the locations of single moving targets in a 2-D WSN setup. Subsequently, these initial estimates undergo further refinement within an Unscented Kalman Filter (UKF). The results demonstrate the superior performance of the proposed algorithms in tracking targets, showcasing high accuracy levels within a few centimeters is evident from the mean localization errors for standard RSSI, ANFIS, and ANFIS+UKF, that the ANFIS+UKF framework can handle real-time target tracking issues in WSN utilizing RSSI (5.657, 0.805, and 0.068, respectively). By contrast, the proposed method offers an impressive improvement of 98.797% over the standard RSSI method.
