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Cellular automaton-based algorithms for the dispersion of mobile wireless sensor networks

机译:基于元胞自动机的移动无线传感器网络分散算法

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Due to the advent of sensor technology and its applications, mobile wireless sensor networks (MWSNs) have gained a significant amount of research interest. In a typical MWSN, sensors can move within the network. We develop a set of probabilistic and deterministic cellular automaton (CA)-based algorithms for motion planning problems in MWSNs. First, we consider a scenario where a group of sensors are deployed and they need to disperse in order to maximise the area covered by the network. In this variant of the problem we do not explicitly consider that the sensors should maintain the connectivity of the network while they move. Second, we consider a scenario where the sensors are initially randomly distributed and they need to disperse autonomously to both maximise the coverage of the network and maintain its connectivity. We carry out extensive simulations of both deterministic and randomised variants of the algorithms. For the first variant of the problem we compare our algorithms with one previous algorithm and find that our algorithm yields better network coverage than the earlier algorithm. We also find that probabilistic algorithms have better overall performance for the second variant. CA algorithms rely only on local information about the network and, hence, they can be used in practice for MWSN problems. On the other hand, locality of the algorithm implies that maintaining connectivity becomes a non-trivial problem.
机译:由于传感器技术及其应用的出现,移动无线传感器网络(MWSN)获得了大量的研究兴趣。在典型的MWSN中,传感器可以在网络内移动。我们针对MWSN中的运动规划问题开发了一套基于概率和确定性元胞自动机(CA)的算法。首先,我们考虑一个场景,其中部署了一组传感器,它们需要分散以最大化网络覆盖的面积。在此问题的变体中,我们没有明确考虑传感器在移动时应保持网络的连通性。其次,我们考虑一种情况,其中传感器最初是随机分布的,它们需要自动分散以最大化网络覆盖范围并保持其连接性。我们对算法的确定性变量和随机变量进行了广泛的仿真。对于问题的第一个变体,我们将我们的算法与以前的算法进行了比较,发现我们的算法比早期的算法具有更好的网络覆盖范围。我们还发现,概率算法在第二个变量中具有更好的整体性能。 CA算法仅依赖于有关网络的本地信息,因此,它们可在实践中用于MWSN问题。另一方面,算法的局部性意味着维持连通性成为一个不小的问题。

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