Affiliations: [a]
School of Information Engineering, Changsha Medical University, Changsha, China
| [b]
Research Institute for Primary Care and Health Sciences, Keele University, Keele, Staffs, England
Correspondence:
[*]
Corresponding author. Rong Liu, School of Information Engineering, Changsha Medical University, Changsha 410219, China. E-mail: Xli0630@163.com.
Abstract: The traditional abnormal location algorithm ignores the uncertainty of wireless sensor networks, which is not suitable for practical applications, and has low accuracy of location. To address this problem, a new fuzzy weighted location algorithm for abnormal target in wireless sensor networks is proposed in this paper. For the characteristics of spatiotemporal association and association of non-spatiotemporal attribute, the abnormal target is identified by multi-attribute association algorithm. Considering that Bayesian networks can effectively express dependencies between variables, Bayesian networks are used to establish the dependency model of non-spatiotemporal attribute. The dependence structure of non-spatiotemporal attributes is obtained by structure learning. The parameter learning of each node of the network structure is carried out to obtain the conditional probability table. The confidence degree of attribute association is used to judge whether the attribute association pattern of the point to be detected is an abnormal pattern. The abnormal target location problem is described. The coordinates of sensor node with abnormal target are identified by the weighted location algorithm. The circles with the centers of three points not on a straight line and the diameter of the signal intensity indicator distance are drawn to obtain the abnormal target position. The weights for weighted location are obtained by fuzzy algorithm. Experimental results show that the proposed algorithm has high accuracy of location.
