双层压缩聚合：从不完整的感觉数据中恢复物理场
Dual-Level Compressed Aggregation: Recovering Fields of Physical Quantities from Incomplete Sensory Data
摘要：
虽然无线传感器网络（WSNs）是强大的监测物理事件，从无线传感器网络收集的数据几乎总是不完整的，如果所调查的物理事件分布在一个广阔的区域。这种不完整性的原因有两个：i）网络覆盖不足和ii）用于节能的数据聚合。而现有的恢复方案只解决第二个方面，我们开发了双水平压缩聚合（DECA）作为一个新的框架，以解决这两个方面。具体而言，DECA允许高保真恢复的广泛的事件，的情况下，无线传感器网络只稀疏覆盖的事件区域，并在网络中的数据聚合应用于流量减少。DECA利用现实世界事件的低秩性质和传感数据的冗余性，将矩阵补全与微调压缩传感技术相结合，进行双层重建过程。我们证明，DECA可以恢复一个广泛的事件与不到5%的数据，相对于事件的尺寸，被收集。基于合成和真实数据集的性能评估证实了我们的DECA框架的恢复保真度和能源效率。
英文摘要：
Although wireless sensor networks (WSNs) are powerful in monitoring physical events, the data collected from a WSN are almost always incomplete if the surveyed physical event spreads over a wide area. The reason for this incompleteness is twofold: i) insufficient network coverage and ii) data aggregation for energy saving. Whereas the existing recovery schemes only tackle the second aspect, we develop Dual-lEvel Compressed Aggregation (DECA) as a novel framework to address both aspects. Specifically, DECA allows a high fidelity recovery of a widespread event, under the situations that the WSN only sparsely covers the event area and that an in-network data aggregation is applied for traffic reduction. Exploiting both the low-rank nature of real-world events and the redundancy in sensory data, DECA combines matrix completion with a fine-tuned compressed sensing technique to conduct a dual-level reconstruction process. We demonstrate that DECA can recover a widespread event with less than 5% of the data, with respect to the dimension of the event, being collected. Performance evaluation based on both synthetic and real data sets confirms the recovery fidelity and energy efficiency of our DECA framework.
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