A taxonomy of localization techniques based on multidimensional scaling

TL;DR

This paper provides a comprehensive survey of localization techniques in Wireless Sensor Networks that utilize multidimensional scaling, classifying various algorithms and evaluation metrics to clarify their differences and advancements.

Contribution

It offers a detailed taxonomy and classification of MDS-based localization algorithms in WSNs, summarizing their variations and evaluation criteria.

Findings

Extensive classification of MDS-based localization algorithms

Comparison of different evaluation metrics used in the field

Identification of key variations and improvements in MDS-MAP algorithms

Abstract

Localization in Wireless Sensor Networks (WSNs) has been a challenging problem in the last decade. The most explored approaches for this purpose are based on multidimensional scaling (MDS) technique. The first algorithm that introduced MDS for nodes localization in sensor networks is well known as MDS-MAP. Since its appearance in 2003, many variations of MDS-MAP have been proposed in the literature. This paper aims to provide a comprehensive survey of the localization techniques that are based on MDS. We classify MDS-based algorithms according to different taxonomy features and different evaluation metrics.