Cost-aware compressive sensing for networked sensing systems
Cost-aware compressive sensing for networked sensing systems
复制标题
DOI：
10.1145/2737095.2737105
复制
发表时间：
2015-04
期刊：
影响因子：
--
通讯作者：
Liwen Xu;Xiaohong Hao;N. Lane;Xin Liu;T. Moscibroda
中科院分区：
文献类型：
--
作者：
Liwen Xu;Xiaohong Hao;N. Lane;Xin Liu;T. Moscibroda
压缩感测是一种可以帮助降低感测任务的采样率的技术。在移动的人群感知应用或无线传感器网络中，收集样本的资源负担通常是主要关注的问题。因此，压缩感测在这种情况下是一种很有前途的方法。压缩感知的一个隐含假设-无论是在理论上还是在其应用中-都是每个样本都具有相同的成本：其目标是简单地减少样本数量，同时实现良好的恢复精度。然而，在许多联网的感测系统中，获得特定样本的成本可能在很大程度上取决于位置、时间、设备的状况以及样本的许多其他因素。在本文中，我们研究压缩感知的情况下，不同的样本有不同的成本，我们试图找到一个很好的权衡之间最小化总样本成本和由此产生的恢复精度。我们设计了成本感知压缩感知（CACS），它将样本的成本多样性纳入压缩感知框架，我们将CACS应用于网络传感系统。从技术上讲，我们使用正则化列和（RCS）作为恢复精度的预测指标，并使用此指标来设计一个优化算法，找到一个最小成本的随机抽样方案与可证明的恢复界限。我们还展示了如何CACS可以应用在分布式环境中。以交通监测和空气污染为具体应用实例，基于大规模真实痕迹对CACS进行了评价。我们的研究结果表明，CACS实现了显着的成本节约，超过自然基线（贪婪和随机采样）高达4倍。
Compressive Sensing is a technique that can help reduce the sampling rate of sensing tasks. In mobile crowdsensing applications or wireless sensor networks, the resource burden of collecting samples is often a major concern. Therefore, compressive sensing is a promising approach in such scenarios. An implicit assumption underlying compressive sensing -- both in theory and its applications -- is that every sample has the same cost: its goal is to simply reduce the number of samples while achieving a good recovery accuracy. In many networked sensing systems, however, the cost of obtaining a specific sample may depend highly on the location, time, condition of the device, and many other factors of the sample. In this paper, we study compressive sensing in situations where different samples have different costs, and we seek to find a good trade-off between minimizing the total sample cost and the resulting recovery accuracy. We design Cost-Aware Compressive Sensing (CACS), which incorporates the cost-diversity of samples into the compressive sensing framework, and we apply CACS in networked sensing systems. Technically, we use regularized column sum (RCS) as a predictive metric for recovery accuracy, and use this metric to design an optimization algorithm for finding a least cost randomized sampling scheme with provable recovery bounds. We also show how CACS can be applied in a distributed context. Using traffic monitoring and air pollution as concrete application examples, we evaluate CACS based on large-scale real-life traces. Our results show that CACS achieves significant cost savings, outperforming natural baselines (greedy and random sampling) by up to 4x.