Improving Data Extraction Efficiency of Cache Nodes in Cognitive Radio Networks Using Big Data Analysis

Abstract

In cognitive radio networks, unlicensed users are allowed to use underutilized licensed spectrum until licensed users' transmission quality of service is not compromised. As soon as the conflict goes beyond a certain limit, SU must leave the spectrum and move to the other nearby free band. At the time of interruption, sensing the nearby free channels and switching to them will take some time, hence the ongoing data will be interrupted, which will delay the data transmission. To minimize this delay, creating cache of the SU signal at multiple nodes in a cluster has shown significant improvement in reducing the transmission delay if cache placement is done systematically. This systematic and accurate placement of cache is possible if the data accumulated is accessed and processed quickly. Taking into account the vastness of cluster networks, a huge amount of data will be required to be accessed and processed. Cognitive Radio networks are very complex structures when it comes to the information sharing amongst the secondary users and with the cluster head. Taking into account, whether unlicensed users share their information with other secondary users, and in case if they do, how much proportion of it they allow the fusion center to process, several big data scenarios exist. This paper discusses the possible information sharing scenarios in cognitive radio network systems and their possible Big Data Solutions.