A trust model based energy detection for cognitive radio networks

Fan Jin, Vijay Varadharajan, Udaya Tupakula

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference, ACSW 2017
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)9781450347686
Publication statusPublished - 30 Jan 2017
EventAustralasian Computer Science Week 2017 - Geelong, Australia
Duration: 31 Jan 20173 Feb 2017


OtherAustralasian Computer Science Week 2017


  • cognitive radio network
  • trust model
  • subjective logic
  • energy detection


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