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Uncertainty-Aware Sensor Deployment Strategy in Mixed Wireless Sensor Networks

机译:混合无线传感器网络中具有不确定性的传感器部署策略

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Deployment is a fundamental issue in wireless sensor networks (WSNs), which affects the performance and lifetime of the networks. Usually the sensor locations are precomputed based on “perfect” sensor detection model, whereas sensors may not always provide reliable information, either due to operational tolerance levels or environmental factors. Therefore, it is very important to take into account this uncertainty in the deployment process. In this paper, we address the problem of sensor deployment in a mixed sensor network where the mobile and static nodes work collaboratively to perform deployment optimization task. We consider the Gaussian white noise in the environment and present a centralized algorithm (FABGM for short) which discoveres vacancies by using detection model based on false alarm and moves the mobile nodes according to the method based on bipartite graph matching in this study. In this algorithm, the management node of the WSNs collects the geographical information of all of the static and mobile sensors. Then, the management node executes the algorithm to get the best matches between mobile sensors and coverage holes. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
机译:部署是无线传感器网络(WSN)中的一个基本问题,它会影响网络的性能和寿命。通常,传感器位置是根据“完美的”传感器检测模型预先计算的,而传感器可能由于操作公差级别或环境因素而无法始终提供可靠的信息。因此,在部署过程中考虑这种不确定性非常重要。在本文中,我们解决了混合传感器网络中的传感器部署问题,其中移动节点和静态节点协同工作以执行部署优化任务。我们考虑了环境中的高斯白噪声,提出了一种集中算法(简称FABGM),该算法通过使用基于虚警的检测模型发现空位,并根据基于二部图匹配的方法移动移动节点。在该算法中,WSN的管理节点收集所有静态和移动传感器的地理信息。然后,管理节点执行该算法,以获取移动传感器和覆盖漏洞之间的最佳匹配。仿真结果表明了该方法的有效性。

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