A Multi-reality Approach to CRN Data Representation

Cognitive Radio Networks (CRNs) are emerging as a viable solution for exploiting the available spectrum and solving overcrowding in the operating bands. Several dimensions in the process of identification, management and routing need to be handled to overcome the challenges associated with the management of CRNs. A very important topic in achieving an effective and flexible management solution is related to the data representation of the different CRN dimensions. Scalability, high tolerance to churn and low latencies in accessing the stored data are fundamental characteristics in order to keep up with the dynamic nature of a mobile environment. Accordingly, to achieve a robust data representation that can handle all these requirements in an efficient way we focus our attention on peer-to-peer (P2P) systems. These systems are well known for providing an efficient platform to develop scalable and robust applications. Hence, in this paper we investigate using geometric multi-dimensional data representation and addressing system based on basic CAN functionality. Results obtained in our experiments are reported, providing useful information to improve the resilience to churn of our system.