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Cited by 287

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

An Efficient Distributed Trust Model for Wireless Sensor Networks

Jiang, Jinfang, Han, Guangjie, Wang, Feng, Shu, Lei, Guizani, Mohsen·2015·IEEE Transactions on Parallel and Distributed Systems

Abstract

Trust models have been recently suggested as an effective security mechanism for Wireless Sensor Networks (WSNs). Considerable research has been done on modeling trust. However, most current research work only takes communication behavior into account to calculate sensor nodes' trust value, which is not enough for trust evaluation due to the widespread malicious attacks. In this paper, we propose an Efficient Distributed Trust Model (EDTM) for WSNs. First, according to the number of packets received by sensor nodes, direct trust and recommendation trust are selectively calculated. Then, communication trust, energy trust and data trust are considered during the calculation of direct trust. Furthermore, trust reliability and familiarity are defined to improve the accuracy of recommendation trust. The proposed EDTM can evaluate trustworthiness of sensor nodes more precisely and prevent the security breaches more effectively. Simulation results show that EDTM outperforms other similar models, e.g., NBBTE trust model.

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Metadata

Authors
Jiang, J., Han, G., Wang, F., Shu, L., & Guizani, M.
Venue
IEEE Transactions on Parallel and Distributed Systems
Published
2015-05-01
Volume
26
Issue
5
Pages
1228-1237
ISSN
1045-9219 (ISSN-L)
OpenAlex
W2171775586
Cited by
287
References
13
FWCI
23.11
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An Efficient Distributed Trust Model for Wireless Sensor Networks. https://app.cochat.ai/papers/10.1109/tpds.2014.2320505
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Jiang, J., Han, G., Wang, F., Shu, L., & Guizani, M. (2015). An Efficient Distributed Trust Model for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems. https://doi.org/10.1109/tpds.2014.2320505

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.