本文選題：無(wú)線傳感網(wǎng) + 時(shí)空相關(guān)性　； 參考：《傳感技術(shù)學(xué)報(bào)》2017年08期

【摘要】：針對(duì)降低無(wú)線傳感網(wǎng)能耗和保證數(shù)據(jù)精度之間的矛盾,提出了自適應(yīng)采樣數(shù)據(jù)并利用壓縮感知進(jìn)行壓縮的方法。傳統(tǒng)的基于壓縮感知的無(wú)線傳感器數(shù)據(jù)壓縮,只采樣部分節(jié)點(diǎn)的數(shù)據(jù),對(duì)于未被采樣節(jié)點(diǎn)感知到的突發(fā)事件很有可能發(fā)生漏檢情況。本文方法檢測(cè)所有節(jié)點(diǎn)上傳的數(shù)據(jù)再進(jìn)行壓縮,可以有效避免漏檢情況的發(fā)生。根據(jù)信號(hào)具有時(shí)間相關(guān)性的特點(diǎn),本文采用基于方差分析ANOVA(Analysis of Variance)原理改進(jìn)的傳感器自適應(yīng)采樣頻率方法,并考慮節(jié)點(diǎn)剩余能量,減少平穩(wěn)信號(hào)的采集次數(shù),均衡網(wǎng)絡(luò)節(jié)點(diǎn)能耗。在LEACH協(xié)議基礎(chǔ)上,對(duì)簇內(nèi)數(shù)據(jù)進(jìn)行壓縮感知的方法對(duì)數(shù)據(jù)進(jìn)行壓縮從而減少數(shù)據(jù)的空間相關(guān)性并傳輸?shù)絽R聚節(jié)點(diǎn),以減少網(wǎng)絡(luò)整體的能量消耗。針對(duì)可能的漏報(bào)情況,提出一種改進(jìn)的局部事件監(jiān)測(cè)算法—滑動(dòng)窗口局部事件監(jiān)測(cè)SW-LED(Sliding Window-Local Event Detection)算法,實(shí)現(xiàn)了實(shí)時(shí)準(zhǔn)確的異常檢測(cè)和預(yù)警。實(shí)驗(yàn)結(jié)果表明本文方法既可以有效的均衡網(wǎng)絡(luò)節(jié)點(diǎn)能耗以提高網(wǎng)絡(luò)生存周期,同時(shí)保證了數(shù)據(jù)的精度,對(duì)于異常情況的識(shí)別率也有很大的提高。
[Abstract]:Aiming at the contradiction between reducing the energy consumption of wireless sensor network and ensuring the precision of the data, an adaptive data sampling method is proposed and compression sensing is used to compress the data. Traditional wireless sensor data compression based on compressed sensing only samples the data of some nodes. It is very possible to miss detection for unexpected events which are not perceived by the sampled nodes. In this paper, the data uploaded by all nodes are detected and compressed, which can effectively avoid the occurrence of missed detection. According to the time dependence of the signal, an adaptive sampling frequency method based on the ANOVA(Analysis of Variance principle is proposed in this paper. Considering the residual energy of the sensor, the acquisition frequency of stationary signal is reduced, and the energy consumption of the network node is equalized. Based on the LEACH protocol, the data is compressed and sensed to reduce the spatial correlation of the data and transmit to the convergent node to reduce the energy consumption of the network as a whole. An improved local event monitoring algorithm, sliding window Local event Monitoring (SW-LED(Sliding Window-Local Event Detection) algorithm, is proposed to realize real-time and accurate anomaly detection and early warning. The experimental results show that the proposed method can effectively balance the energy consumption of network nodes to improve the network lifetime, ensure the accuracy of the data, and improve the recognition rate of abnormal cases.
【作者單位】： 江西師范大學(xué)計(jì)算機(jī)信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61462042,61365002) 江西師范大學(xué)研究生創(chuàng)新基金項(xiàng)目(YJS2016086)
【分類號(hào)】：TN929.5;TP212.9

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