With the recent advances in robotic technologies, field coverage using mobile sensors is now possible, so that a small set of sensors can be mounted on mobile robots and move to desired areas. Compared to static settings, area coverage is more complicated in a mobile sensor network due to the dynamics arising from the continuous movement of the sensors. This complication is even higher in the more realistic case where little or no prior metric information is available about the sensor field. In this paper, we consider the problem of self-deployment of a set of mobile sensors which have no knowledge of the area, the number of nodes, their location, and even the distances to each other. In this restricted setting, we formulate the problem as a multi-player game in which each sensor tries to maximize its coverage while considering the overlapping sensing areas by its neighbors. We propose a distributed learning algorithm for coordinating the movement of the sensors in the field, and prove its convergence to the equilibria of the formulated game. Simulation results demonstrate that for moderate density deployments, the proposed algorithm competes with the existing location-dependent mobility strategies, while outperforming location-free algorithms.

中文翻译：

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没有位置信息的移动传感器网络中的分布式覆盖

随着机器人技术的最新发展，现在可以使用移动传感器进行现场覆盖，因此可以将一小组传感器安装在移动机器人上并移动到所需区域。与静态设置相比，由于传感器连续运动而产生的动态变化，在移动传感器网络中区域覆盖更为复杂。在更现实的情况下，甚至很少或没有关于传感器场的现有度量信息，这种复杂性甚至更高。在本文中，我们考虑了一组移动传感器的自部署问题，这些传感器不知道面积，节点数，它们的位置，甚至彼此之间的距离。在此受限设置中，我们将问题描述为一个多人游戏，其中每个传感器都在考虑相邻传感器重叠的感应区域的同时，试图最大化其覆盖范围。我们提出了一种分布式学习算法，用于协调传感器在野外的运动，并证明其收敛于公式化游戏的均衡性。仿真结果表明，对于中等密度的部署，所提出的算法可与现有的位置相关的移动性策略竞争，而优于无位置算法。