Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling

TLDR

Sensor positioning is a fundamental and crucial issue for sensor network operation and management. The study investigates failure scenarios of existing positioning methods and proposes a distributed, on‑demand sensor positioning approach using multidimensional scaling to handle anisotropic topologies. The method applies multidimensional scaling and coordinate alignment to estimate adjacent sensor positions, compares anchor estimates to true locations for correction, and propagates corrections to other sensors. Iterative adjustment enables the method to overcome adverse network and terrain conditions and produce accurate sensor positions.

Abstract

Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. In the paper, we first study some situations where most existing sensor positioning methods tend to fail to perform well, an example being when the topology of a sensor network is anisotropic. Then, we explore the idea of using dimensionality reduction techniques to estimate sensors coordinates in two (or three) dimensional space, and we propose a distributed sensor positioning method based on multidimensional scaling technique to deal with these challenging conditions. Multidimensional scaling and coordinate alignment techniques are applied to recover positions of adjacent sensors. The estimated positions of the anchors are compared with their true physical positions and corrected. The positions of other sensors are corrected accordingly. With iterative adjustment, our method can overcome adverse network and terrain conditions, and generate accurate sensor position. We also propose an on demand sensor positioning method based on the above method.