CAJ | 학술논문

数据质量是物联网发展所面临的重大挑战，数据异常检测能实现数据质量提升与潜在信息挖掘。在智能家居等小型物联网场景中数据空间相关性严重不足，因此只能利用时间相关性实现对单传感器数据的异常检测。本文给出基于距离的滑动窗口异常检测算法，通过只处理新加入和刚离开窗口的数据降低时间复杂度，只存储数据对象的 k 个邻居以降低空间复杂度。此外，本文根据滑动窗口内局部异常和全局异常的定义，设计异常检测的处理流程，并借助真实数据实现算法仿真，以检测率 DR 和误检率 FR 为检测指标分析参数对检测结果的影响。从仿真结果可知，该算法能实现较好的检测效果，局部异常检测能保证高 DR，全局异常检测能保证低 FR。
수거질량시물련망발전소면림적중대도전，수거이상검측능실현수거질량제승여잠재신식알굴。재지능가거등소형물련망장경중수거공간상관성엄중불족，인차지능이용시간상관성실현대단전감기수거적이상검측。본문급출기우거리적활동창구이상검측산법，통과지처리신가입화강리개창구적수거강저시간복잡도，지존저수거대상적 k 개린거이강저공간복잡도。차외，본문근거활동창구내국부이상화전국이상적정의，설계이상검측적처리류정，병차조진실수거실현산법방진，이검측솔 DR 화오검솔 FR 위검측지표분석삼수대검측결과적영향。종방진결과가지，해산법능실현교호적검측효과，국부이상검측능보증고 DR，전국이상검측능보증저 FR。
Data quality is a major challenge of the web of things(WoT), data quality can be improved and the underlying information can be mined by detecting outlier in WoT data. The data spatial correlation is serious shortage in some small scale scenarios, such as smart home, it can only use the time correlation for single sensor data outlier detection. In this paper, a detection algorithm based on the distance outlier for sliding windows was given, the time complexity of the algorithm was reduced by only handling the new input instance and leaving instance, moreover, by just storing the k neighbors of the in-stance, the space complexity was reduced. Besides, based on the definition of local outlier and global outlier in sliding win-dows, this paper designed the process of outlier detection. The algorithm was simulated by using the real data from the smart home demo scenario, the detection rate (DR) and false alarm rate (FR) were the detection index of the algorithm to analyze the parameters affect of the detection results. The simulation results show that the algorithm can reach better detec-tion results, local outlier detection can achieve higher DR, for global outlier to ensure low FR.