Energy-based adaptive compression in water network control systems
Energy-based adaptive compression in water network control systems
复制标题
水网控制系统中基于能量的自适应压缩
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
J. Mccann
中科院分区：
文献类型：
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作者：
Sokratis Kartakis;Marija Milojevic;G. Tzagkarakis;J. Mccann
现代给水管网利用物联网(IoT)技术来监控供水管网资产的行为。智能仪表/传感器和执行器节点被用于将信息从供水网络传输到数据中心以供进一步分析。由于水资产的地下位置，许多自来水公司倾向于部署电池供电的节点，这些节点的寿命超过10年。这禁止使用高采样率感测，因此限制了我们可以从记录器数据中获得的知识。为了缓解这一问题，高效的数据压缩支持高速率采样，同时显著减少所需的存储和带宽资源，而不会牺牲有意义的信息内容。本文介绍了一种新的算法，它结合了标准无损压缩的精度和压缩感知框架的效率。我们的方法平衡了每种技术的权衡，并在给定传感器节点电池状态的情况下，通过最小化重建误差来优化选择最佳压缩模式。为了验证我们的算法，我们使用了真实世界大型试验台上超过170天、25个传感器节点的真实高采样率水压数据。实验结果表明，与传统的周期性通信技术相比，该算法可以减少66%左右的通信量，延长46%的电池寿命。
Contemporary water distribution networks exploit Internet of Things (IoT) technologies to monitor and control the behavior of water network assets. Smart meters/sensor and actuator nodes have been used to transfer information from the water network to data centers for further analysis. Due to the underground position of water assets, many water companies tend to deploy battery driven nodes which last beyond the 10-year mark. This prohibits the use of high-sample rate sensing therefore limiting the knowledge we can obtain from the recorder data. To alleviate this problem, efficient data compression enables high-rate sampling, whilst reducing significantly the required storage and bandwidth resources without sacrificing the meaningful information content. This paper introduces a novel algorithm which combines the accuracy of standard lossless compression with the efficiency of a compressive sensing framework. Our method balances the tradeoffs of each technique and optimally selects the best compression mode by minimizing reconstruction errors, given the sensor node battery state. To evaluate our algorithm, real high-sample rate water pressure data of over 170 days and 25 sensor nodes of our real world large scale testbed was used. The experimental results reveal that our algorithm can reduce communication around 66% and extend battery life by 46% compared to traditional periodic communication techniques.