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首页> 外文会议>Information Sciences and Systems, 2009. CISS 2009 >Sparse event detection in wireless sensor networks using compressive sensing
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Sparse event detection in wireless sensor networks using compressive sensing

机译：使用压缩感测的无线传感器网络中的稀疏事件检测

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摘要

Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for sparse signals. For large wireless sensor networks, the events are relatively sparse compared with the number of sources. Because of deployment cost, the number of sensors is limited, and due to energy constraint, not all the sensors are turned on all the time. In this paper, the first contribution is to formulate the problem for sparse event detection in wireless sensor networks as a compressive sensing problem. The number of (wake-up) sensors can be greatly reduced to the similar level of the number of sparse events, which is much smaller than the total number of sources. Second, we suppose the event has the binary nature, and employ the Bayesian detection using this prior information. Finally, we analyze the performance of the compressive sensing algorithms under the Gaussian noise. From the simulation results, we show that the sampling rate can reduce to 25% without sacrificing performance. With further decreasing the sampling rate, the performance is gradually reduced until 10% of sampling rate. Our proposed detection algorithm has much better performance than the l1-magic algorithm proposed in the literature.
机译：压缩感测是最近提出的革命性想法，旨在为稀疏信号实现更低的采样率。对于大型无线传感器网络，事件与源数量相比相对较少。由于部署成本的原因，传感器的数量受到限制，并且由于能量的限制，并非所有传感器都始终处于打开状态。在本文中，第一个贡献是将无线传感器网络中的稀疏事件检测问题表述为压缩感测问题。 （唤醒）传感器的数量可以大大减少到稀疏事件数量的相似水平，这比源总数少得多。其次，我们假设事件具有二进制性质，并使用此先验信息进行贝叶斯检测。最后，我们分析了高斯噪声下压缩感知算法的性能。从仿真结果可以看出，在不牺牲性能的情况下，采样率可以降低到25％。随着采样率的进一步降低，性能逐渐降低，直到采样率的10％。我们提出的检测算法比文献中提出的l 1 -magic算法具有更好的性能。

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