Given n distinct points p_1, p_2, …, p_n in the plane, the map labeling problem with four squares is to place n axis-parallel equi-sized squares Q_1, … ,Q_n of maximum possible size such that pi is a corner of Qi and no two squares overlap. This problem is NP-hard and no algorithm with approximation ratio better than 1/2 exists unless P = NP [10]. In this paper, we consider a scenario where we want to visualize the information gathered by smart dust, i.e. by a large set of simple devices, each consisting of a sensor and a sender that can gather sensor data and send it to a central station. Our task is to label (the positions of) these sensors in a way described by the labeling problem above. Since these devices are not positioned accurately (for example, they might be dropped from an airplane), this gives rise to consider the map labeling problem under the assumption, that the positions of the points are not fixed precisely, but perturbed by random noise. In other words, we consider the smoothed complexity of the map labeling problem. We present an algorithm that, under such an assumption and Gaussian random noise with sufficiently large variance, has linear smoothed complexity.
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