Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/57144
Title: A trust model based energy detection for cognitive radio networks
Contributor(s): Jin, Fan (author); Varadharajan, Vijay (author); Tupakula, Udaya  (author)orcid 
Publication Date: 2017-01-31
DOI: 10.1145/3014812.3014882
Handle Link: https://hdl.handle.net/1959.11/57144
Abstract: 

In a cognitive radio network (CRN), energy detection is one of the most efficient spectrum sensing techniques for the protection of legacy spectrum users, with which the presence of primary users (PUs) can be detected promptly, allowing secondary users (SUs) to vacate the channels immediately. In this paper, we design a novel trust based energy detection model for CRNs. This model extends the widely used energy detection and employs the idea of a trust model to perform spectrum sensing in the CRN. In this model, trust among SUs is represented by opinion, which is an item derived from subjective logic. The opinions are dynamic and updated frequently: If one SU makes a correct decision, its opinion from other SUs' point of view can be increased. Otherwise, if an SU exhibits malicious behavior, it will be ultimately denied by the whole network. A trust recommendation is also designed to exchange trust information among SUs. The salient feature of our trust based energy detection model is that using trust relationships among SUs, this guarantees only reliable SUs will participate in generating a final result. This greatly reduces the computation overheads. Meanwhile, with neighbors' trust recommendations, a SU can make objective judgment about another SU's trustworthiness to maintain the whole system at a certain reliable level.

Publication Type: Conference Publication
Conference Details: ACSW 2017: Australasian Computer Science Week 2017, Geelong, Australia, 30th January - 3rd February, 2017
Source of Publication: Proceedings of the Australasian Computer Science Week Multiconference, No. 68, p. 1-8, p. 1-8
Publisher: Association for Computing Machinery
Place of Publication: New York, United States of America
Fields of Research (FoR) 2020: 460407 System and network security
Socio-Economic Objective (SEO) 2020: 220405 Cybersecurity
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Publisher/associated links: https://dl-acm-org.ezproxy.une.edu.au/doi/abs/10.1145/3014812.3014882
WorldCat record: https://www.worldcat.org/title/979993993
Appears in Collections:Conference Publication
School of Science and Technology

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