Trust Model for Cognitive Radio Networks
Date Issued
2008
Date
2008
Author(s)
Chen, Peng-Yu
Abstract
Devices with cognitive radio capabilities of spectrum sensing to fully utilize radio spectrum have been considered as a key technology in future wireless communications. Primary system and CRs, which can leverage and coexist with legacy systems, thus form cognitive radio networks (CRN) which originally design to improve the spectrum utilization. When we try to further consider network efficiency, the problem turns into optimization in a inter-network manner. In cognitive radio networking function, CRs should establish trust association with neighbors for construction of trusted route. This is an important initial step in network layer design of CRN since the cooperative routing transmission is allowable in the heterogeneous systems. In this thesis, we propose a general trust decision criterion for nodes as receiving association request from neighbors. We exploit unique ID to identify nodes and derive the decision criterion under system-defined constraint. It minimizes the risk of accepting probable malicious users.n the other hand, each node in CRN should observe, analyze, and learn the trust evidence such like packet loss rate, total time delay, and etc. The trust model in this thesis provides a methodology to measure the trust evidence and try to learn and response to the analyzed data meanwhile. It should be not only suitable for dynamic topology variation in CRN but also learn the behavior change in individual node. Then, we can build up the first step when we try to design the network architecture of CRN. When we learn the some special parameters from the trust model, we expect to move forward from trust association to trusted routing in CRN.
Subjects
Trust model
Cognitive radio networks
Bayesian network
Detection theory
Neyman-Pearson criterion
Machine learning
Network layer design
Network efficiency
Type
thesis
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