Abstract
The Internet of Things (IoT) enables the connectivity of disparate devices and the exchange of real-time data among those devices. It requires large amounts of bandwidth to ensure the quality of service (QoS), but sufficient bandwidth is not always available due to the limited frequency spectrum allocated for wireless data communication. Cognitive radio (CR) is a promising technology that enhances the utilization of the spectrum by allowing unlicensed users/secondary users (SU) access to the licensed primary users (PU) spectrum under certain conditions. The CR based IoT (CR-IoT) network can overcome the spectrum scarcity problem in a conventional IoT network. In a CR-IoT network, energy efficiency must be considered for avoiding interference between the PU and the SU, as the conventional energy detection (ED) technologies consume significant energy for CR operations. To mitigate this problem, we propose a novel energy efficient sequential ED spectrum sensing technique which enhances the sensing duration of each unlicensed CR-IoT user/SU by utilizing the reporting time slot when compared to the conventional non-sequential ED spectrum sensing scheme. In addition, each unlicensed CR-IoT user calculates the weight factor based on the Kullback Liebler Divergence score, which enhances the detection performance. Thereafter, each CR-IoT user in the CR-IoT network sequentially passes on both the local sensing result and the weight factor to the corresponding fusion center (FC) via the allocated reporting channel, which extends the sensing time duration of the CR-IoT user. The FC uses the local sensing result and the weight factor of each CR-IoT user to make a global decision by using the soft fusion rule. The results obtained through simulations show that the proposed sequential ED spectrum sensing scheme achieves a better sensing performance, an enhanced sum rate, an enhanced energy and spectral efficiency when compared to the conventional non-sequential ED spectrum sensing scheme with interference constraints.
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