METHOD FOR LOCALIZING NODES IN WIRELESS SENSOR NETWORKS BASED ON THE RECONSTRUCTION OF SPARSE DISTANCE MATRICES

DOI: 10.31673/2786-8362.2025.018942

Abstract

The article considers modern methods for localizing nodes
in wireless sensor networks with an emphasis on increasing the positioning accuracy by combining classical
multidimensional scaling (MDS) with greedy heuristic algorithms and methods for restoring the sparse
matrix of RSSI signals. A comprehensive approach to detecting anomalies and errors in measurements
using spectral analysis and machine learning algorithms is proposed. Experimental modeling in MATLAB
was carried out, which confirmed the effectiveness of the developed methods in various network operation
scenarios. The results obtained have practical significance for increasing the reliability, security, and
performance of wireless sensor networks, which is important for the development of modern Internet of
Things (IoT) systems and industrial applications.
Keywords: wireless sensor networks, localization, multidimensional scaling (MDS), greedy
heuristics, RSSI, matrix reconstruction, anomaly detection, spectral analysis, machine learning, MATLAB,
Internet of Things (IoT), network security, positioning, measurement errors

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