A Review of Multidimensional Scaling Techniques for RSS-Based WSN Localization

Mobile nodes with sensing, computing and communicating capabilities are usually deployed in indoor environments as wireless sensor networks (WSN) for internet-of-things applications. Proper interpretation of the data acquired by these sensor networks requires an estimate of their location. Among the currently existing positioning methods for WSN, multidimensional scaling (MDS) is a computationally efficient technique based on empirical measurement or estimation of the ranges between the nodes. This communication describes and evaluates several versions of MDS localization methods, particularly applied to indoor networks based on transmission of radiofrequency signals and measurement of the received signal strengths (RSS). The positioning accuracy, resistance to measurement noise, and computation times are analyzed and commented within a common framework.

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