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3D genetic algorithms for underwater sensor networks

机译:水下传感器网络的3D遗传算法

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We introduce a genetic algorithm-based topology control mechanism, named 3D-GA, for Autonomous Underwater Vehicles (AUVs) operating in Underwater Sensor Networks (UWSNs). Using limited information collected from a node's local neighbours, 3D-GA runs autonomously at each AUV and provides guidance for its speed and direction towards a uniform spatial distribution while maintaining network connectivity. Imprecise and limited neighbourhood knowledge could potentially disrupt convergence towards a uniform and stable spatial coverage. We demonstrate that AUVs running our 3D-GA create a highly resilient network that can adapt to changing conditions such as the addition, loss or malfunction of number of AUVs. We also show that the ambiguity in detecting neighbours' exact locations does not prevent 3D-GA from achieving a uniform coverage but requiring AUVs travel longer distances to stabilise. Our simulation software results verify that 3D-GA is an effective tool for providing a robust solution for volumetric spatial control of AUVs in UWSNs.
机译:我们为在水下传感器网络(UWSN)中运行的自主水下航行器(AUV)引入了一种基于遗传算法的拓扑控制机制,称为3D-GA。 3D-GA使用从节点本地邻居收集的有限信息,在每个AUV处自动运行,并在保持网络连接性的同时为其速度和方向提供指导,以实现均匀的空间分布。不精确和有限的邻居知识可能会破坏向统一和稳定的空间覆盖的融合。我们证明了运行我们的3D-GA的AUV创建了一个高度弹性的网络,该网络可以适应不断变化的条件,例如AUV数量的增加,丢失或故障。我们还表明,在检测邻居的确切位置时的不确定性不会阻止3D-GA实现均匀的覆盖范围,而是需要AUV行驶更长的距离才能稳定下来。我们的仿真软件结果证明3D-GA是为UWSN中的AUV的体积空间控制提供鲁棒解决方案的有效工具。

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