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Optimizing Resurfacing Schedules to Maximize Value of Information in UWSNs

机译:优化重排计划以最大化UWSN中的信息价值

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In Underwater Sensor Networks (UWSNs) with high volume of data recording activity, a mobile sink such as a Autonomous Underwater Vehicle (AUV) can be used to offload data from the sensor nodes. When the AUV approaches the underwater node, it can use high data rate optical communication. However, the data is not considered delivered when it was transferred from the sensor node to the AUV, but when the AUV had resurfaced and transferred the data to the sink. If the data is not time sensitive, it is sufficient for the AUV to resurface only once at the end of its data collection path. However, for time-sensitive data, it is more advantageous for the AUV to resurface multiple times during its path, and upload the data collected since the previous resurfacing. Thus, a resurfacing schedule needs to complement the path planning process. In this paper we are using the metric of Value of Information (VoI) as the optimization criteria to capture the time- sensitive nature of collected information. We propose a genetic algorithm based approach to determine the resurfacing schedule for an AUV which is already provided with the sequence of nodes to be visited.
机译:在具有大量数据记录活动的水下传感器网络(UWSN)中,可以使用诸如自动水下航行器(AUV)之类的移动接收器从传感器节点卸载数据。当AUV接近水下节点时,它可以使用高数据速率光通信。但是,当数据从传感器节点传输到AUV时,不认为数据已传递,而是当AUV重新浮出水面并将数据传输到接收器时,才认为数据已传递。如果数据不是时间敏感的,则AUV在其数据收集路径的末尾仅重新出现一次就足够了。但是,对于时间敏感的数据,AUV在其路径期间多次重新浮出水面并上传自上次重新铺面以来收集的数据更为有利。因此,需要重铺计划以补充路径计划过程。在本文中,我们使用信息价值(VoI)度量作为优化标准来捕获所收集信息的时间敏感性。我们提出了一种基于遗传算法的方法来确定AUV的重修计划,该计划已经提供了要访问的节点序列。

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