Analysis of Localization Methods for Wireless Sensor Networks in Indoor IoT Environment
DOI:
https://doi.org/10.31649/1997-9266-2025-178-1-150-155Keywords:
localization methods, wireless sensor networks, Internet of Things (IoT), localization accuracy, energy efficiency, scalability, interference resistance, sensitivity, trilateration, ESPRIT, minimum Capon variance, RSSI, proximity-based positioning, weighted subspace fittingAbstract
Experimental studies and analysis of various localization methods for wireless sensor networks in the indoor environment of the Internet of Things (IoT) have been conducted. The aim of the work was to determine the effectiveness and limitations of the main localization approaches for their further application in different environments. The investigated methods included minimum Capon variance, ESPRIT algorithm, weighted subspace fitting, proximity-based positioning, and RSSI-based trilateration. The main focus was on analyzing the key characteristics of the methods, such as accuracy, energy efficiency, scalability, interference resistance, sensitivity to environmental parameters, and signal transmission range. The results showed that each of the considered methods has its own unique advantages and limitations depending on the specifics of the application. The ESPRIT algorithm, for example, provided the highest positioning accuracy and scalability, making it suitable for large and complex networks with high node density. Proximity-based positioning methods were found to be the most energy-efficient, which is a crucial factor for IoT applications with tight power constraints. The RSSI-based trilateration method demonstrated high noise immunity and stability over a wide range of conditions, including signal density variability and the presence of physical barriers in the environment. The study enabled to formulate recommendations for choosing the optimal localization method depending on the specific requirements for accuracy, power consumption, network stability, and operating environment characteristics. The results obtained are of practical value for IoT system developers, as they contribute to optimizing network performance, increasing data transmission reliability, and reducing power consumption. The findings of this study can serve as a basis for future developments and improvements of localization algorithms, ensuring their effective use in a wide range of IoT applications, such as smart buildings, industrial monitoring, logistics, and healthcare. Keywords: localization methods, wireless sensor networks, Internet of Things, localization accuracy, energy efficiency, scalability, interference resistance, sensitivity, trilateration, ESPRIT, minimum Capon variance, RSSI, proximity-based positioning, weighted subspace fitting.
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