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Managing the Mobility of a Mobile Sensor Network Using Network Dynamics

机译:使用网络动力学管理移动传感器网络的移动性

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It has been discussed in the literature that the mobility of a mobile sensor network (MSN) can be used to improve its sensing coverage. How to efficiently manage the mobility towards a better coverage, however, remains unanswered. In this paper, motivated by classical dynamics that studies the movement of objects, we propose the concept of network dynamics and define the associated potential functions that capture the operational goals as well as the environment of a MSN. We find that, in managing the mobility of a MSN, Newtonu00026;#8217;s laws of motion in classical dynamics are insufficient for they introduce oscillations into the movement of sensor nodes. Instead, in network dynamics, the laws of motion are formulated using the steepest descent method in optimization. Based on the network dynamics model, we first devise a parallel and distributed algorithm (PDND) that runs on each sensor node to guide its movement. PDND then turns sensor nodes into autonomous entities capable of adjusting their locations according to the operational goals and environmental changes. After that, we formally prove the convergence of PDND. Finally, we apply PDND in three applications to demonstrate its effectiveness.
机译:在文献中已经讨论过,可以使用移动传感器网络(MSN)的移动性来改善其检测范围。但是,如何有效地管理移动性以实现更好的覆盖范围仍然没有答案。在本文中,受研究对象运动的经典动力学的启发,我们提出了网络动力学的概念,并定义了关联的潜在功能,这些功能捕获了MSN的操作目标和环境。我们发现,在管理MSN的移动性时,经典动力学中的Newtonu00026;#8217; s运动定律是不足的,因为它们将振荡引入到传感器节点的运动中。取而代之的是,在网络动力学中,运动定律是使用最速下降法进行优化的。基于网络动力学模型,我们首先设计一种并行分布式算法(PDND),该算法在每个传感器节点上运行以指导其运动。然后,PDND将传感器节点转变为能够根据操作目标和环境变化调整其位置的自治实体。之后,我们正式证明了PDND的收敛性。最后,我们将PDND应用于三个应用程序以证明其有效性。

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