Wireless Sensor Networks Localization Methods: Multidimensional Scaling vs. Semidefinite Programming Approach

TL;DR

This paper compares two wireless sensor network localization methods, multidimensional scaling and semidefinite programming, through extensive simulations to evaluate their performance across various network configurations.

Contribution

It provides a detailed comparative analysis of MDS and SDP localization methods, highlighting their effectiveness and performance in different scenarios.

Findings

Both methods achieve minimal estimation errors.

Performance varies with network topology and parameters.

Both techniques are highly satisfactory.

Abstract

With the recent development of technology, wireless sensor networks are becoming an important part of many applications such as health and medical applications, military applications, agriculture monitoring, home and office applications, environmental monitoring, etc. Knowing the location of a sensor is important, but GPS receivers and ophisticated sensors are too expensive and require processing power. Therefore, the localization wireless sensor network problem is a growing field of interest. The aim of this paper is to give a comparison of wireless sensor network localization methods, and therefore, multidimensional scaling and semidefinite programming are chosen for this research. Multidimensional scaling is a simple mathematical technique widely-discussed that solves the wireless sensor networks localization problem. In contrast, semidefinite programming is a relatively new field of optimization with a growing use, although being more complex. In this paper, using extensive simulations, a detailed overview of these two approaches is given, regarding different network topologies, various network parameters and performance issues. The performances of both techniques are highly satisfactory and estimation errors are minimal