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Quality-of-sensing aware budget constrained contaminant detection sensor deployment in water distribution system

机译:感知质量感知预算限制了供水系统中污染物检测传感器的部署

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Water contamination or pollution has raised serious disasters and social impact. It is significant to alleviate its impact or reduce the risks. Deploying water quality monitoring sensors in the water distribution systems naturally becomes a promising solution. In the consideration of sensor deployment, the deployment cost and the achieved quality-of-sensing, usually in terms of coverage, are always two contradictive issues. Although massively deploying sensors implies higher quality-of-sensing, it may also incur extremely high deployment cost. Actually, it is usually infeasible with the consideration of limited sensor deployment budget. In this paper, we are motivated to investigate a budget constrained sensor deployment in water distribution system, with the goal of maximizing the quality-of-sensing. Two kinds of sensors with different prices and hence different communication capabilities are considered. The cheaper one equips with only sensor-to-sensor communication capability. While, the expensive one is further capable of cellular communication. We first formally describe our problem using a mixed integer non-linear programming (MINLP) problem. To address the complexity on solving MINLP, we further propose a heuristic algorithm based on genetic algorithm, whose high efficiency is extensively validated by simulation based studies.
机译:水污染或污染已引起严重的灾难和社会影响。减轻其影响或降低风险具有重要意义。在水分配系统中部署水质监测传感器自然成为一种有前途的解决方案。在考虑传感器部署时,部署成本和所达到的感知质量(通常在覆盖范围方面)始终是两个矛盾的问题。尽管大规模部署传感器意味着更高的传感质量,但它也可能招致极高的部署成本。实际上,考虑到有限的传感器部署预算,这通常是不可行的。在本文中,我们旨在研究预算受限的水分配系统中传感器的部署,目的是最大程度地提高传感质量。考虑两种具有不同价格并因此具有不同通信能力的传感器。较便宜的设备仅具有传感器到传感器的通信功能。同时,昂贵的手机还能够进行蜂窝通信。我们首先使用混合整数非线性规划(MINLP)问题来正式描述我们的问题。为了解决求解MINLP的复杂性,我们进一步提出了一种基于遗传算法的启发式算法,其高效性已通过基于仿真的研究得到了广泛验证。

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