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首页> 外文会议>9th ACM/IEEE international conference on information processing in sensor networks 2010 >Lakon: A Middle-ground Approach to High-frequency Data Acquisition and In-network Processing in Sensor Networks
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Lakon: A Middle-ground Approach to High-frequency Data Acquisition and In-network Processing in Sensor Networks

机译：Lakon：传感器网络中高频数据采集和网络内处理的中间方法

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摘要

The need for high-frequency signal acquisition and processing is becoming increasingly prevalent in sensor networks. Applications that require high-frequency data sampling are presently at a disadvantage; applications that only sample at high data rates (and not process any of it locally) end up transmitting large quantities of data, greatly reducing network lifetime. Other applications that do use in-network signal processing rely on power-hungry motes. We present Lakon, a mote architecture capable of onboard signal processing of high-frequency data that provides a middle ground for more general classes of applications that require signal processing. Our design takes advantage of an energy-efficient on-board digital signal processor (DSP) that can be intelligently enabled on demand. Our contribution here is threefold. First, we present a general mote architecture that is more appropriate for applications such as body sensor networks and habitat monitoring. Second, we present a switching scheme for processor scheduling that determines the co-processor's usage. Finally, we demonstrate the use and potential of Lakon in the context of a text-independent speaker recognition system that takes an audio signal as input and performs classification on that signal to identify the owner of the voice.
机译：在传感器网络中，对高频信号采集和处理的需求变得越来越普遍。当前，需要高频数据采样的应用处于劣势。仅以高数据速率采样（而不在本地处理任何数据）的应用程序最终会传输大量数据，从而大大缩短了网络寿命。其他使用网络内信号处理的应用程序也需要耗电的微粒。我们介绍了Lakon，这是一种能够对高频数据进行信号处理的微尘架构，为需要信号处理的更一般应用类别提供了中间基础。我们的设计利用了可按需智能启用的高能效板载数字信号处理器（DSP）。我们在这里的贡献是三方面的。首先，我们提出一种通用的微尘架构，该架构更适合诸如人体传感器网络和栖息地监控之类的应用。其次，我们提出了用于处理器调度的切换方案，该方案确定了协处理器的使用情况。最后，我们演示了Lakon在独立于文本的说话者识别系统中的用途和潜力，该系统以音频信号作为输入并对信号进行分类以识别语音的所有者。

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