An Efficient Contention-Window Based Reporting for Internet of Things Features in Cognitive Radio Networks

Abstract EN

Internet of things (IoT) is a new challenging paradigm for connecting heterogeneous networks.

However, an explosive increase in the number of IoT cognitive users requires a mass of sensing reporting; thus, it increases complexity of the system.

Moreover, bandwidth utilization, reporting time, and communication overhead arise.

To realize spectrum sensing, how to collect sensing results by reducing the communication overhead and the reporting time is a problem of major concern in future wireless networks.

On the other hand, cognitive radio is a promising technology to access the spectrum opportunistically.

In this paper, we propose a contention-window based reporting approach with a sequential fusion mechanism.

The proposed reporting scheme reduces the reporting time and the communication overhead by collecting sensing results from the secondary users with the highest reliability at a fusion center by utilizing Dempster-Shafer evidence theory.

The fusion center broadcasts the sensing results once a global decision requirement is satisfied.

Through simulations, we evaluate the proposed scheme in terms of percentage of the number of reporting secondary users, error probability, percentage of reporting, and spectral efficiency.

As a result, it is shown that the proposed scheme is more effective than a conventional order-less sequential reporting scheme.