传感器驱动网络中数据采集与定位的采集接收器设计

B. Ananthasubramaniam, Upamanyu Madhow
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引用次数: 3

摘要

我们考虑一个传感器网络,当传感器有东西要报告时,它们可以随意通信,而不需要事先与其他传感器或数据收集节点协调。解调传感器数据和定位正在通信的传感器的负担落在收集器节点的网络上,这些节点永久地监视来自传感器网络的传输。该模型允许以最小的功能随机部署大量传感器节点,同时将复杂性转移到收集器节点网络。虽然其原理与之前在“成像”传感器网络上的工作类似,但关键的区别在于,通信模型现在是传感器驱动的,而不是收集器驱动的。本文解决的两个主要技术挑战如下:(a)是否有简单的采集器接收器物理层实现来共同解决传感器传输的检测,估计它来自的方向和解调数据的任务?(b)鉴于收集器的时间同步不够好,无法使用到达时间差技术进行传感器定位,那么假设每个收集器节点的天线数量相对较少,仅凭空间信息定位传感器的效果如何?本文的研究结果表明,通过合理设计收集器物理层,并对每个收集器提取的空间信息进行贝叶斯组合,可以很好地解决上述问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collector Receiver Design for Data Collection and Localization in Sensor-driven Networks
We consider a sensor network in which the sensors communicate at will when they have something to report, without prior coordination with other sensors or with data collection nodes. The burden of demodulating the sensor data, and localizing the sensor which is communicating, falls on a network of collector nodes which are perpetually monitoring transmissions from the sensor network. This model allows the random deployment of very large numbers of sensor nodes with minimal capabilities, while shifting the complexity to a network of collector nodes. While the philosophy is similar to prior work on "imaging" sensor nets, the key difference is that the communication model is now sensor-driven, rather than collector-driven. The two major technical challenges addressed in this paper are as follows: (a) Are there simple physical layer implementations of the collector receiver for jointly solving the tasks of detection of a sensor transmission, estimation of the direction from which it comes, and demodulating the data? (b) Given that the collectors are not time synchronized well enough to permit the use of time-difference-of-arrival techniques for sensor localization, how well can the sensors be localized with spatial information alone, assuming that each collector node has a relatively small number of antennas? The results reported in this paper indicate that the preceding issues can be addressed satisfactorily with appropriate design of the collector physical layer, together with Bayesian combining of the spatial information extracted by each collector.
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