Abstract
In this paper, we focus on detecting a special type of anomaly in wireless sensor network (WSN), which appears simultaneously in a collection of neighboring nodes and lasts for a significant period of time. Existing point-based techniques, in this context, are not very effective and efficient. With the proposed distributed segment-based recursive kernel density estimation, a global probability density function can be tracked and its difference between every two periods of time is continuously measured for decision making. Kullback-Leibler (KL) divergence is employed as the measure and, in order to implement distributed in-network estimation at a lower communication cost, several types of approximated KL divergence are proposed. In the meantime, an entropic graph-based algorithm that operates in the manner of centralized computing is realized, in comparison with the proposed KL divergence-based algorithms. Finally, the algorithms are evaluated using a real-world data set, which demonstrates that they are able to achieve a comparable performance at a much lower communication cost.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Information Forensics and Security
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
