Aldalahmeh, SA, Ghogho, M orcid.org/0000-0002-0055-7867, McLernon, D orcid.org/0000-0001-8278-6171 et al. (1 more author) (2016) Optimal fusion rule for distributed detection in clustered wireless sensor networks. EURASIP Journal on Advances in Signal Processing, 5 (Advances in Signal P). pp. 1-12. ISSN 1687-6180
Abstract
We consider distributed detection in a clustered wireless sensor network (WSN) deployed randomly in a large field for the purpose of intrusion detection. The WSN is modeled by a homogeneous Poisson point process. The sensor nodes (SNs) compute local decisions about the intruder’s presence and send them to the cluster heads (CHs). A stochastic geometry framework is employed to derive the optimal cluster-based fusion rule (OCR), which is a weighted average of the local decision sum of each cluster. Interestingly, this structure reduces the effect of false alarm on the detection performance. Moreover, a generalized likelihood ratio test (GLRT) for cluster-based fusion (GCR) is developed to handle the case of unknown intruder’s parameters. Simulation results show that the OCR performance is close to the
Chair-Varshney rule. In fact, the latter benchmark can be reached by forming more clusters in the network without increasing the SN deployment intensity. Simulation results also show that the GCR performs very closely to the OCR when the number of clusters is large enough. The performance is further improved when the SN deployment
intensity is increased.
Metadata
Item Type: | Article |
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Copyright, Publisher and Additional Information: | © 2016, Aldalahmeh et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Keywords: | Wireless sensor network,; Cluster; Distributed detection; Fusion rule; Stochastic geometry |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Apr 2016 10:03 |
Last Modified: | 18 Feb 2022 15:51 |
Published Version: | http://dx.doi.org/10.1186/s13634-016-0303-9 |
Status: | Published |
Publisher: | Springer Open |
Identification Number: | 10.1186/s13634-016-0303-9 |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:96511 |