Cognitive radio networks (CRNs) have emerged as a solution for the looming spectrum crunch caused
by the rapid adoption of wireless devices over the previous decade. This technology enables efficient
spectrum utility by dynamically reusing existing spectral bands. A CRN achieves this by requiring its
users – called secondary users (SUs) – to measure and opportunistically utilise the band of a legacy
broadcaster – called a primary user (PU) – in a process called spectrum sensing. Sensing requires the
distribution and fusion of measurements from all SUs, which is facilitated by a variety of architectures
and topologies.
CRNs possessing a central computation node are called centralised networks, while CRNs composed of
multiple computation nodes are called decentralised networks. While simpler to implement, centralised
networks are reliant on the central node – the entire network fails if this node is compromised. In
contrast, decentralised networks require more sophisticated protocols to implement, while offering
greater robustness to node failure. Relay-based networks, a subset of decentralised networks, distribute
the computation over a number of specialised relay nodes – little research exists on spectrum sensing
using these networks. CRNs are vulnerable to unique physical layer attacks targeted at their spectrum sensing functionality.
One such attack is the Byzantine attack; these attacks occur when malicious SUs (MUs) alter their
sensing reports to achieve some goal (e.g. exploitation of the CRN’s resources, reduction of the CRN’s
sensing performance, etc.). Mitigation strategies for Byzantine attacks vary based on the CRN’s
network architecture, requiring defence algorithms to be explored for all architectures. Because of the
sparse literature regarding relay-based networks, a novel algorithm – suitable for relay-based networks
– is proposed in this work. The proposed algorithm performs joint MU detection and secure sensing by
large-scale probabilistic inference of a statistical model.
The proposed algorithm’s development is separated into the following two parts.
• The first part involves the construction of a probabilistic graphical model representing the
likelihood of all possible outcomes in the sensing process of a relay-based network. This is
done by discovering the conditional dependencies present between the variables of the model.
Various candidate graphical models are explored, and the mathematical description of the chosen
graphical model is determined.
• The second part involves the extraction of information from the graphical model to provide
utility for sensing. Marginal inference is used to enable this information extraction. Belief
propagation is used to infer the developed graphical model efficiently. Sensing is performed by
exchanging the intermediate belief propagation computations between the relays of the CRN.
Through a performance evaluation, the proposed algorithm was found to be resistant to probabilistic
MU attacks of all frequencies and proportions. The sensing performance was highly sensitive to
the placement of the relays and honest SUs, with the performance improving when the number of
relays was increased. The transient behaviour of the proposed algorithm was evaluated in terms of its
dynamics and computational complexity, with the algorithm’s results deemed satisfactory in this regard.
Finally, an analysis of the effectiveness of the graphical model’s components was conducted, with a
few model components accounting for most of the performance, implying that further simplifications
to the proposed algorithm are possible. / Dissertation (MEng)--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/79657 |
| Date | January 2020 |
| Creators | Sivakumaran, Arun |
| Contributors | Maharaj, Bodhaswar Tikanath Jugpershad, u13108698@tuks.co.za, Alfa, Attahiru S. |
| Publisher | University of Pretoria |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Rights | © 2020 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
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