Imtiaz Syed Anas, Casson Alexander J, Rodriguez-Villegas Esther
IEEE Trans Biomed Eng. 2014 Apr;61(4):1080-90. doi: 10.1109/TBME.2013.2293916.
Wearable sensor nodes monitoring the human body must operate autonomously for very long periods of time. Online and low-power data compression embedded within the sensor node is therefore essential to minimize data storage/transmission overheads. This paper presents a low-power MSP430 compressive sensing implementation for providing such compression, focusing particularly on the impact of the sensor node architecture on the compression performance. Compression power performance is compared for four different sensor nodes incorporating different strategies for wireless transmission/on-sensor-node local storage of data. The results demonstrate that the compressive sensing used must be designed differently depending on the underlying node topology, and that the compression strategy should not be guided only by signal processing considerations. We also provide a practical overview of state-of-the-art sensor node topologies. Wireless transmission of data is often preferred as it offers increased flexibility during use, but in general at the cost of increased power consumption. We demonstrate that wireless sensor nodes can highly benefit from the use of compressive sensing and now can achieve power consumptions comparable to, or better than, the use of local memory.
监测人体的可穿戴传感器节点必须长时间自主运行。因此，传感器节点内嵌入的在线低功耗数据压缩对于最小化数据存储/传输开销至关重要。本文提出了一种用于提供此类压缩的低功耗MSP430压缩感知实现方案，特别关注传感器节点架构对压缩性能的影响。针对四种不同的传感器节点，比较了其压缩功率性能，这些节点采用了不同的数据无线传输/传感器节点本地存储策略。结果表明，所使用的压缩感知必须根据底层节点拓扑结构进行不同设计，并且压缩策略不应仅由信号处理考虑因素来指导。我们还提供了当前最先进的传感器节点拓扑结构的实用概述。数据的无线传输通常更受青睐，因为它在使用过程中提供了更高的灵活性，但一般是以增加功耗为代价。我们证明，无线传感器节点可以从压缩感知的使用中受益匪浅，现在可以实现与使用本地内存相当或更好的功耗。
