1st International IEEE/ACM Workshop on Software for Sensor Networks

Research Article

Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks

  • @INPROCEEDINGS{10.1109/COMSWA.2006.1665221,
        author={D.  Janakiram and  Adi Mallikarjuna  Reddy  and Phani  Kumar},
        title={Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks},
        proceedings={1st International IEEE/ACM Workshop on Software for Sensor Networks},
        publisher={IEEE},
        proceedings_a={SENSORWARE},
        year={2006},
        month={8},
        keywords={},
        doi={10.1109/COMSWA.2006.1665221}
    }
    
  • D. Janakiram
    Adi Mallikarjuna Reddy
    Phani Kumar
    Year: 2006
    Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks
    SENSORWARE
    IEEE
    DOI: 10.1109/COMSWA.2006.1665221
D. Janakiram1,2,*, Adi Mallikarjuna Reddy ,*, Phani Kumar,*
  • 1: Distributed and Object Systems Lab, Department of CS & E,
  • 2: Indian Institute of Technology Madras, Chennai-600036, India.
*Contact email: fd.janakiram@cs.iitm.ernet.in, adi@cs.iitm.ernet.in, phanig@cs.iitm.ernet.in

Abstract

Data reliability is an important issue from the user's perspective, in the context of streamed data in wireless sensor networks (WSN). Reliability is affected by the harsh environmental conditions, interferences in wireless medium and usage of low quality sensors. Due to these conditions, the data generated by the sensors may get corrupted resulting in outliers and missing values. Deciding whether an observation is an outlier or not depends on the behavior of the neighbors' readings as well as the readings of the sensor itself. This can be done by capturing the spatio-temporal correlations that exists among the observations of the sensor nodes. By using naive Bayesian networks for classification, we can estimate whether an observation belongs to a class or not. If it falls beyond the range of the class, then it can be detected as an outlier. However naive Bayesian networks do not consider the conditional dependencies among the observations of sensor attributes. So, we propose an outlier detection scheme based on Bayesian belief networks, which captures the conditional dependencies among the observations of the attributes to detect the outliers in the sensor streamed data. Applicability of this scheme as a plug-in to the component oriented middleware for sensor networks (COMiS) of our early research work is also presented