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Spectrum and Energy Efficient Multiple Access for Detection in Wireless Sensor Networks

机译:频谱和能源高效的多路访问,用于无线传感器网络中的检测

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We consider a binary hypothesis testing problem using wireless sensor networks (WSNs). The decision is made by a fusion center and is based on received data from the sensors. We focus on a spectrum and energy efficient transmission scheme used to reduce the spectrum usage and energy consumption during the detection task. We propose a spectrum and energy efficient multiple access (SEEMA) transmission protocol that performs a censoring-type transmission based on the density of observations using multiple access channels (MAC). Specifically, in SEEMA, only sensors with highly informative observations transmit their data in each data collection. The sensors transmit a common shaping waveform and the fusion center receives a superposition of the analog transmitted signals. SEEMA has important advantages for detection tasks in WSNs. First, it is highly energy and bandwidth efficient due to transmission savings and narrowband transmission over MAC. Second, it can be implemented by simple dumb sensors (oblivious to observation statistics, and local data processing is not required), which simplifies the implementation as compared to existing MAC transmission schemes for detection in WSNs. We establish a finite sample analysis and an asymptotic analysis of the error probability with respect to the network size and provide system design conditions to obtain the exponential decay of the error. Specific performance analysis is developed for common non-i.i.d. observation scenarios, including local i.i.d. observations, and Markovian correlated observations. Numerical examples demonstrate SEEMA performance.
机译:我们考虑使用无线传感器网络(WSN)的二元假设检验问题。该决定由融合中心做出,并基于从传感器接收到的数据。我们专注于一种频谱和节能传输方案,该方案可用于减少检测任务期间的频谱使用和能耗。我们提出了一种频谱和能源效率高的多路访问(SEEMA)传输协议,该协议基于使用多路访问信道(MAC)的观测密度执行审查类型的传输。具体来说,在SEEMA中,只有具有高度了解性的观察结果的传感器才能在每个数据集中传输其数据。传感器发送共同的整形波形,融合中心接收模拟发送信号的叠加。 SEEMA对于WSN中的检测任务具有重要的优势。首先,由于节省了传输和通过MAC进行窄带传输,因此具有很高的能源和带宽效率。其次,它可以通过简单的哑传感器(无需观察统计数据,并且不需要本地数据处理)来实现,与用于WSN的现有MAC传输方案相比,它简化了实现。我们建立了关于网络规模的误差概率的有限样本分析和渐近分析,并提供了系统设计条件以获得误差的指数衰减。针对常见的非i.d.开发了特定的性能分析。观察情景,包括本地i.d.观测值和马尔可夫相关观测值。数值示例证明了SEEMA性能。

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