Edge Computing Application: Real-Time Anomaly Detection Algorithm for Sensing Data
-
摘要: 随着物联网技术的不断发展，已逐步进入“万物互联”的新时代.针对物联网中实时采集的传感数据总体质量低下的问题，提出基于边缘计算的传感数据异常实时检测算法.该算法首先对相应的传感数据以“时间序列”的形式进行表示,并建立基于边缘计算的分布式传感数据异常检测模型;其次利用单源时间序列自身的连续性以及多源时间序列之间的相关性，分别对实时传感数据中出现的数据异常进行有效检测,并分别形成相应的异常检测结果集;最后将上述2个异常检测结果集进行有效地融合处理，从而得到更加准确的异常数据检测结果.通过实验验证该算法的检测准确性和有效性，结果显示：该算法检测时间短并且异常检出率高.Abstract: With the rapid development of Internet of things (IoT), we have gradually entered into the IoE (Internet of everything) era. In face of the low quality of real-time gathering sensor data in IoT, this paper proposes a novel real-time anomaly detection algorithm based on edge computing for streaming sensor data. This algorithm firstly expresses the corresponding sensor data in the form of time series and establishes the distributed sensing data anomaly detection model based on edge computation. Secondly, this algorithm utilizes the continuity of single-source time series and the correlation between multi-source time series to detect anomaly data from streaming sensor data effectively and respectively. The corresponding anomaly detection result sets are also generated in the same process. Finally, the above two anomaly detection result sets would be effectively fused in a certain way so as to obtain more accurate detection result. In other words, this algorithm achieves a higher detection rate compared with other traditional methods. Extensive experiments on the real-world dataset of household heating data from the Jinan municipal steam heating system, which collects monitoring data from 3084 apartments of 394 buildings, have been conducted to demonstrate the advantages of our algorithm.
-
Keywords:
- Internet of things /
- edge computing /
- time series data /
- correlation /
- outlier distance
-
-
期刊类型引用(6)
1. 王璐璐. 基于混合注意力机制的时间旋转知识图谱补全. 网络安全与数据治理. 2024(10): 42-48 . 
百度学术
2. 何鹏，周刚，陈静，章梦礼，宁原隆. 类型增强的时态知识图谱表示学习模型. 计算机研究与发展. 2023(04): 916-929 . 本站查看
3. 陈小英，熊盛武，王盛，张士伟. 基于上下文时序关联的时序知识图谱嵌入方法. 武汉大学学报(理学版). 2023(02): 249-257 . 百度学术
4. 周丽华，王家龙，王丽珍，陈红梅，孔兵. 异质信息网络表征学习综述. 计算机学报. 2022(01): 160-189 . 百度学术
5. 杨大伟，周刚，卢记仓，宁原隆. 基于知识表示学习的知识图谱补全研究综述. 信息工程大学学报. 2021(05): 558-565 . 百度学术
6. 王红，卢林燕，王童. 航空安全事件知识图谱补全方法. 西南大学学报(自然科学版). 2020(11): 31-42 . 百度学术
其他类型引用(3)
计量
- 文章访问数: 2568
- HTML全文浏览量: 6
- PDF下载量: 1436
- 被引次数: 9
下载:
