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

Abstract

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 sophisticated 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.

Localization in Wireless Sensor Networks for Accurate Event Detection

“…On the basis of this survey, localization via single mobile anchor node is more economical. Additionally, by considering the limitations of computation and less memory power of sensor nodes (Stojkoska et al, 2009;Phoemphon et al, 2018), we adopted a method that uses mobile anchor nodes for locating sensors in a WSN. In this process, a localization algorithm for computing the location of sensor nodes is proposed.…”

Application of Wireless Sensor Networks for Indoor Temperature Regulation

“…Many algorithms have been proposed for ZigBee-based WSN localization. Most of them consider a WSN deployed in outdoor environment where GPS signals are available and a global map of the network can be easily achieved using well-known techniques for localization [15, 16]. On the other side, indoor localization methods should consider different characteristics of the indoor surroundings where WSN is installed.…”

A taxonomy of localization techniques based on multidimensional scaling

“…This taxonomy can be easily mapped to other localization approaches that use similar nonlinear dimensionality reduction techniques. For example, self organizing maps (SOMs) [5] [6], manifold learning algorithm [7] or semidefinite programming [8] [9] can follow same or similar taxonomy like the one presented in this paper. However, most of these approaches are represented with limited number of publications compared to MDS with respect to solving WSN localization problem.…”