Augmenting in-situ with mobile sensing for adaptive monitoring of water distribution networks
Augmenting in-situ with mobile sensing for adaptive monitoring of water distribution networks
复制标题
通过移动传感增强原位，对配水网络进行自适应监测
DOI：
10.1145/3302509.3311048
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
N. Venkatasubramanian
中科院分区：
文献类型：
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作者：
Praveen Venkateswaran;M. Suresh;N. Venkatasubramanian
为配水网络配备监测传感器对于维持适当的水质和水量至关重要。检测和定位网络中的不良事件的现有努力已经探索了在连接处安装原位传感器或通过管道部署多个移动的传感器。这些方法具有高成本、低感测精度、缺乏足够的覆盖范围或提供间歇性监测。在本文中，我们结合联合收割机的好处，在现场和移动的传感与各种geosocial因素开发一个具有成本效益的混合监测架构，最大限度地减少不利的水事件对社会的影响。该架构可以在网络内按需自适应地调整感测分辨率，基于事件确定所需的感测能力，并响应变化的事件严重性。我们提出了一个两阶段的规划和部署方法，首先集成网络结构，事件和社区信息与基于模拟的分析，以确定安装现场传感器和移动的传感器插入基础设施的位置。然后，我们将网络流信息，以确定移动的传感器部署的位置和体积，以快速本地化检测到的事件，以尽量减少其影响。我们使用多个现实世界的水网络来评估我们的方法，以应对不利的水质和损失事件，并将其与现有方法进行比较。我们的研究结果表明，我们提出的方法可以实现高达79%的影响减少高达68%以上的成本效率相比，使用传统的覆盖范围的方法，并高达30%的影响减少，而高达52%以上的成本效率相比，试图尽量减少影响的方法。
Instrumenting water distribution networks with sensors for monitoring is critical to maintain adequate levels of water quality and quantity. Existing efforts to detect and localize adverse events in the network have explored either installing in-situ sensors on junctions or deploying a number of mobile sensors through pipes. These approaches have high costs, low sensing accuracy, lack sufficient coverage or provide intermittent monitoring. In this paper, we combine the benefits of in-situ and mobile sensing with various geosocial factors to develop a cost-effective hybrid monitoring architecture that minimizes the impact of adverse water events on the community. The architecture can adaptively adjust sensing resolutions on-demand within the network, determine required sensing capabilities based on the event, and respond to varying event severities. We propose a two-phase planning and deployment approach that first integrates network structure, event, and community information with simulation based analytics to determine locations to install in-situ sensors and mobile sensor insertion infrastructure. We then incorporate network flow information to determine mobile sensor deployment locations and volume to quickly localize detected events to minimize their impact. We evaluate our approach using multiple real-world water networks for adverse water quality and loss events and compare it to existing approaches. Our results show that our proposed approach can achieve upto 79% reduction in impact with upto 68% greater cost efficiency compared to approaches using traditional coverage heuristics, and upto 30% reduction in impact while being upto 52% more cost efficient compared to approaches that attempt to minimize impact.