Fuzzy Distance Jaya Algorithm Based Node Localization in Anisotropic Wireless Sensor Networks

Many applications of Wireless Sensor Networks (WSNs) depend on location information. Every WSN has anchor nodes or known location-based nodes and target or unknown nodes. Due to several anisotropic factors, solving the node localization problem in Anisotropic WSNs (AWSNs) is more challenging. This work solves the node localization issue in AWSNs using soft-computing approaches. Distance is estimated using a fuzzy logic model to avoid irregularities in anchor nodes' Received Signal Strength Indicator (RSSI) value. The Mamdani Fuzzy Inference System (FIS) employs a triangular membership function to optimize the distance between the anchor and target nodes. The simplicity of the Jaya algorithm inspires us to use it to find the target node location coordinates in AWSNs. The performance of the proposed algorithm is measured in terms of localization error and computation time through simulation analysis on MATLAB software with the fuzzy logic toolbox. The localization error is calculated for different node densities, anchor nodes, and Degree of Irregularity (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$doi$</tex-math></inline-formula>) values. The proposed algorithm compares the performance metrics with existing localization algorithms for AWSNs and provides better location estimation.