Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors
This addresses the challenge of enabling sensing and automation applications by accurately localizing commercial-off-the-shelf sensors in 3D, representing a strong specific gain in the domain of IoT and localization.
The paper tackles the problem of associating many low-cost IoT sensors to their installation locations using RSS measurements, proposing an efficient combinatorial optimization approach that integrates cooperative localization and likelihood search, achieving close-to-100% accuracy with affordable execution time.
Many location-based services use Received Signal Strength (RSS) measurements due to their universal availability. In this paper, we study the association of a large number of low-cost Internet-of-Things (IoT) sensors and their possible installation locations, which can enable various sensing and automation-related applications. We propose an efficient approach to solve the corresponding permutation combinatorial optimization problem, which integrates continuous space cooperative localization and permutation space likelihood ascent search. A convex relaxation-based optimization is designed to estimate the coarse locations of blindfolded devices in continuous 3D spaces, which are then projected to the feasible permutation space. An efficient Cramér-Rao Lower Bound based likelihood ascent search algorithm is proposed to refine the solution. Extensive experiments were conducted to evaluate the performance of the proposed approach, which show that the proposed approach significantly outperforms state-of-the-art combinatorial optimization algorithms and achieves close-to-100% accuracy with affordable execution time.