Outlier detection based on spatio-temporal nearest neighbors and a likelihood ratio test for sensor networks
LIU Yimin1, WEN Junjie1, WANG Lanjun2
1. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
2. David R. Cheriton School of Computer Science, University of Waterloo, Waterloo N2L 3G1, Canada
Abstract：A spatio-temporal nearest neighbors and likelihood ratio test method was developed to detect outliers caused by sensor failures in sensor networks. In the space dimension, a sensor's spatially nearest neighbors were selected using a maximum posterior probability criterion while in the time dimension, the temporal nearest neighbors were previous observations from the same sensor. Each sensor's reading was evaluated based on differences between its earlier measurements and those of its neighbors with a sensor failure model and likelihood ratio test used to detect whether the sensor had failed. Tests show that this approach gives a higher detection rate for the same false alarm rate than existing outlier detection approaches. For example, for a 10% false alarm rate, the detection rate was increased by 10%-30%.
刘一民, 文俊杰, 王岚君. 基于空-时近邻与似然比检验的传感器网络异常点检测[J]. 清华大学学报（自然科学版）, 2017, 57(11): 1196-1201.
LIU Yimin, WEN Junjie, WANG Lanjun. Outlier detection based on spatio-temporal nearest neighbors and a likelihood ratio test for sensor networks. Journal of Tsinghua University(Science and Technology), 2017, 57(11): 1196-1201.
蔡劼, 顾明. 基于IEEE 802.15.4的星形拓扑传感器网络性能分析[J]. 清华大学学报(自然科学版), 2015, 55(5):565-571. CAI Jie, GU Ming. Performance analysis of star topology wireless sensor networks based on IEEE 802.15.4[J]. Journal of Tsinghua University (Science and Technology), 2015, 55(5):565-571. (in Chinese)
[2]
Liu Y, He Y, Li M, et al. Does wireless sensor network scale? A measurement study on GreenOrbs[J]. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(10):1983-1993.
[3]
Mainwaring A, Culler D, Polastre J, et al. Wireless sensor networks for habitat monitoring[C]//Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications. New York, NY, USA:ACM Press, 2002:88-97.
[4]
Zhang Y, Meratnia N, Havinga P. Outlier detection techniques for wireless sensor networks:A survey[J]. IEEE Communications Surveys & Tutorials, 2010, 12(2):159-170.
[5]
蒋晓隆, 刘培, 李政. 数据协调方法在传感器故障监测中的应用[J]. 清华大学学报(自然科学版), 2014, 54(6):763-768.JIANG Xiaolong, LIU Pei, LI Zheng. Data reconciliation for sensor fault monitoring[J]. Journal of Tsinghua University (Science and Technology), 2014, 54(6):763-768. (in Chinese)
[6]
Han J, Kamber M, Pei J. Data Mining:Concepts and Techniques[M]. Burlington, VT, USA:Morgan Kaufmann Press, 2012.
[7]
Knox E M, Ng R T. Algorithms for mining distance-based outliers in large datasets[C]//Proceedings of the International Conference on Very Large Data Bases. Burlington, VT, USA:Morgan Kaufmann Press, 1998:392-403.
[8]
Rajasegarar S, Leckie C, Palaniswami M, et al. Distributed anomaly detection in wireless sensor networks[C]//IEEE Singapore International Conference on Communication Systems. Singapore, Singapore:IEEE Press, 2006:1-5.
[9]
Bettencourt L M A, Hagberg A A, Larkey L B. Separating the wheat from the chaff:Practical anomaly detection schemes in ecological applications of distributed sensor networks[C]//International Conference on Distributed Computing in Sensor Systems. Berlin, German:Springer Press, 2007:223-239.
[10]
Papadimitriou S, Kitagawa H, Gibbons P B, et al. Loci:Fast outlier detection using the local correlation integral[C]//19th International Conference on Data Engineering (ICDE). Bangalore, India:IEEE Press, 315-326.
[11]
Ramaswamy S, Rastogi R, Shim K. Efficient algorithms for mining outliers from large data sets[J]. ACM Sigmod Record, 2000, 29(2):427-438.
[12]
Sandryhaila A, Moura J M F. Discrete signal processing on graphs:Frequency analysis[J]. IEEE Transactions on Signal Processing, 2014, 62(12):3042-3054.
[13]
Chandola V, Banerjee A, Kumar V. Anomaly detection:A survey[J]. ACM Computing Surveys, 2009, 41(3):75-79.
[14]
Zhang K, Hutter M, Jin H. A new local distance-based outlier detection approach for scattered real-world data[C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin, German:Springer Press, 2009:813-822.
[15]
Bruwer F J, Viljoen J, Swanepoel N J. Sensor Structure:World Intellectual Property Organization, WO/2008/052229. 2008-05-02.
[16]
Paik B G, Cho S R, Park B J, et al. Characteristics of wireless sensor network for full-scale ship application[J]. Journal of Marine Science and Technology, 2009, 14(1):115-126.
[17]
NOAA. Global summary of the day[Z/OL].[2017-01-15]. ftp://ftp.ncdc.noaa.gov/pub/data/gsod
[18]
国家地磁台网中心. 数据服务:观测数据[Z/OL].[2017-01-15]. http://oa.global183.com. Geomagnetic Network of China. Data service:Observation data[Z/OL].[2017-01-15]. http://oa.global183.com. (in Chinese)
[19]
Lazarevic A, Ertöz L, Kumar V, et al. A comparative study of anomaly detection schemes in network intrusion detection[C]//Siam International Conference on Data Mining. San Francisco, CA, USA:SIAM Press, 2003:25-36.
2017, Vol. 57 
