An Efficient Spectrum Sharing Approach for Cognitive radio enabled Vehicular Cloud

Vehicular ad hoc networks (VANETs) is an active area of research that enables road safety and infotainment applications for vehicular users. Although a significant amount of efforts is given in the literature, many non-trivial problems and challenges are still to be solved due to the inherent characteristics of vehicular networks like high mobility and dynamic topology change. Cognitive radio (CR) and cloud computing (CC) are two of the latest technologies used in vehicular networks to address those challenges. CR enables VANETs to deal with opportunistic spectrum management for utilizing the unused spectrum of licensed users by secondary users to solve the scarcity of spectrum. Vehicular cloud (VC) uses cloud computing to enable vehicles with excess storage or bandwidth to share the resources with other vehicles, who are willing to use that extra resource at some pre-determined rate of price. One of the main challenges of cognitive radio-enabled vehicular ad hoc networks (CR-VANETs) is efficiently assigning wireless channels to the secondary users (vehicles) on demand so that maximum vehicles can communicate with the available spectrum without interference. Many approaches have already been proposed in the literature. Centralized algorithms suffer from lack of scalability and utilization of resources. On the other hand, distributed approaches need coordination and coverage issue. In this paper, we have developed a Cognitive Radio-enabled Vehicular Cloud Networking (CR-VCN) framework to assign channels in a distributed way to the vehicular cloud members to maximize efficiency in terms of overall network throughput and minimize interferences among the users. Extensive simulation and analysis shows the effectiveness of the approach in terms of some performance measures.