CAJ | 학술논문

针对现有的异常事件检测算法准确率低和能量开销较大等问题，该文提出一种基于压缩感知(CS)和GM(1,1)的异常事件检测方案。首先，基于分簇的思想将传感器节点的数据进行压缩采样后传输至Sink，针对传感器网络中数据稀疏度未知的特点，提出一种基于步长自适应的块稀疏信号重构算法。然后，Sink基于GM(1,1)对节点发生的异常进行预测，并对节点的工作状态进行自适应调整。仿真实验结果表明，相比于其它异常检测算法，该算法的误警率和漏检率较低，在保证异常事件检测可靠性的同时，有效地节省了节点能量。
침대현유적이상사건검측산법준학솔저화능량개소교대등문제，해문제출일충기우압축감지(CS)화GM(1,1)적이상사건검측방안。수선，기우분족적사상장전감기절점적수거진행압축채양후전수지Sink，침대전감기망락중수거희소도미지적특점，제출일충기우보장자괄응적괴희소신호중구산법。연후，Sink기우GM(1,1)대절점발생적이상진행예측，병대절점적공작상태진행자괄응조정。방진실험결과표명，상비우기타이상검측산법，해산법적오경솔화루검솔교저，재보증이상사건검측가고성적동시，유효지절성료절점능량。
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.