無(wú)線傳感器網(wǎng)絡(luò)中基于壓縮感知和GM(1,1)的異常檢測(cè)方案
doi: 10.11999/JEIT141219
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1.
(中南大學(xué)信息科學(xué)與工程學(xué)院 長(zhǎng)沙 410083) ②(香港理工大學(xué)電子計(jì)算學(xué)系 香港)
基金項(xiàng)目:
國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目(61232001/F02)和國(guó)家自然科學(xué)基金面上項(xiàng)目(61173169/F020802)
Abnormal Event Detection Scheme Based on Compressive Sensing and GM (1,1) in Wireless Sensor Networks
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1.
(School of Information Science and Engineering, Central South University, Changsha 410083, China)
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摘要: 針對(duì)現(xiàn)有的異常事件檢測(cè)算法準(zhǔn)確率低和能量開(kāi)銷(xiāo)較大等問(wèn)題，該文提出一種基于壓縮感知(CS)和GM(1,1) 的異常事件檢測(cè)方案。首先，基于分簇的思想將傳感器節(jié)點(diǎn)的數(shù)據(jù)進(jìn)行壓縮采樣后傳輸至Sink，針對(duì)傳感器網(wǎng)絡(luò)中數(shù)據(jù)稀疏度未知的特點(diǎn)，提出一種基于步長(zhǎng)自適應(yīng)的塊稀疏信號(hào)重構(gòu)算法。然后，Sink基于CM(1,1)對(duì)節(jié)點(diǎn)發(fā)生的異常進(jìn)行預(yù)測(cè)，并對(duì)節(jié)點(diǎn)的工作狀態(tài)進(jìn)行自適應(yīng)調(diào)整。仿真實(shí)驗(yàn)結(jié)果表明，相比于其它異常檢測(cè)算法，該算法的誤警率和漏檢率較低，在保證異常事件檢測(cè)可靠性的同時(shí)，有效地節(jié)省了節(jié)點(diǎn)能量。
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關(guān)鍵詞:
- 無(wú)線傳感器網(wǎng)絡(luò) /
- 異常事件檢測(cè) /
- 壓縮感知 /
- GM(1,1)(Grey Model(1,1)) /
- 信號(hào)重構(gòu) /
- 能耗
Abstract: In order to solve the problems of the low accuracy and the high energy cost by the existing abnormal event detection algorithm in Wireless Sensor Networks (WSN), this paper proposes an abnormal event detection algorithm based on Compressive Sensing (CS) and Grey Model(1,1) (GM(1,1)). Firstly, the network is divided into the clusters, and the data are sampled based on compressive sensing and are forwarded to the Sink. According to the characteristics of the unknown data sparsity in WSN, this paper proposes a block-sparse signal reconstruction algorithm based on the adaptive step. Then the abnormal event is predicted based on the GM(1,1) at the Sink node, and the work status of the node is adaptively adjusted. The simulation results show that, compared with the other anomaly detection algorithms, the proposed algorithm has lower probability of false detection and missed detection, and effectively saves the energy of nodes, with assurance the reliability of abnormal event detection at the same time. -
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