Automatic Device-Location Association based on Received Signal Strength Measurements

Sensory data is only meaningful if correctly associated with originating locations. Received signal strength (RSS) is a cost-effective option to locate low-cost sensors due to its universal availability, but yet to be practical because of its coarse ranging accuracy with multiplicative errors. This paper presents a new RSS-based approach to the localization and association of a large number of low-cost sensors given their possible 3D installation locations. The approach addresses the mathematically challenging combinatorial optimization of association by first co-operatively locating the sensors in the continuous 3D spaces, then associating the continuous location estimates to the installation points, and finally refining the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous 3D spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local 3D regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated through both computer simulation and lab test. It achieves close-to-100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly.