  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 Cram\'er-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.
