CAJ | 학술논문

　　针对基于无线传感器网络构建的环境监测系统，为减少多媒体信号采样、处理、传输过程所消耗的计算、存储、电能、带宽资源，该文分析了现有信号处理理论和随机采样方法存在的局限性，结合压缩感知理论，提出了一种改进的加性随机采样方法，设计了一种新型的资源节约型音频信号采集算法。该算法依靠传感器节点和汇聚节点的共同协作完成音频信号采集，其中，传感器节点对具有稀疏性的音频信号进行低频随机采样，汇聚节点可从随机采样数据中高概率重构原始信号。将所提出的信号采集方法用于采集粮仓内的赤拟谷盗成虫爬行声，并与已有数据压缩方法进行性能对比。初步试验结果表明：在随机采样频率为196Hz且最大数据重构误差小于0.5%情况下，可实现13个采集节点所获取的78路声信号的远程、无线、实时采集，特别适用于资源有限的无线传感器网络。
　　침대기우무선전감기망락구건적배경감측계통，위감소다매체신호채양、처리、전수과정소소모적계산、존저、전능、대관자원，해문분석료현유신호처리이론화수궤채양방법존재적국한성，결합압축감지이론，제출료일충개진적가성수궤채양방법，설계료일충신형적자원절약형음빈신호채집산법。해산법의고전감기절점화회취절점적공동협작완성음빈신호채집，기중，전감기절점대구유희소성적음빈신호진행저빈수궤채양，회취절점가종수궤채양수거중고개솔중구원시신호。장소제출적신호채집방법용우채집량창내적적의곡도성충파행성，병여이유수거압축방법진행성능대비。초보시험결과표명：재수궤채양빈솔위196Hz차최대수거중구오차소우0.5%정황하，가실현13개채집절점소획취적78로성신호적원정、무선、실시채집，특별괄용우자원유한적무선전감기망락。
In order to reduce the cost by multimedia signal sampling, processing, transmission of computing, storage, electricity, bandwidth resources in an environment-monitoring system built on a wireless sensor networks, this paper analyzed the limitations of the existing signal processing theory and random sampling methods. An improved additive random sampling method and the sampling time sequence obtained by this method had the same probability density function with the existing random sampling method. The proposed method effectively avoiding the phenomenon of adjacent sampling time interval was too large or too small, and each sequential sampling time had a clear causal relationship. On this basis, a novel resource-saving audio signal acquisition method was designed. In the proposed signal acquisition method, the audio signal acquisition was completed by cooperation between the sensor nodes and the sink nodes. The sensor nodes took the low frequency random sampling with the sparse audio signal, and the sink nodes reconstructed the original signal with high probability by using the received random sampling data. Then a test system was established with 13 acquisition nodes and a sink node based on the Zigbee network technology, and this system was used to implement the remote, wireless, and distributed acquisition of the crawl acoustic signal of Tribolium castaneum Herbst adults in grain barrel. In the test system, the proposed signal acquisition method was compared with the existing data compression method. Results showed that if the maximum data reconstruction error was less than 0.5%, the packet loss rate was less than 10%and each acquisition node only sampled one acoustic signal, then the packet loss rate was 9.1%and the maximum reconstruction error was 0.44%by using the proposed signal acquisition method. However, if using the existing data compression method, packet loss rate and the maximum reconstruction error were 9.3%and 0.46%respectively. Two acquisition methods had similar performance, and could achieve the remote, wireless, real-time acquisition for 13 acoustic signals. For the proposed audio signal acquisition method, when the sampling frequency was as low as 196 Hz, the packet loss rate and the maximum reconstruction error were 21.6% and 0.48%, respectively. But for the proposed signal acquisition method, the sampling frequency was only 586Hz, and it effectively reduced the resource consumption of acoustic signal acquisition. The method proposed in this paper can provide references especially for the wireless sensor networks with limited resources.