Integrating cognitive radio technology with energy harvesting capabilities to the Internet of Things networks offers a promising avenue for tackling spectrum scarcity and battery limitation issues. This study explores a single-channel cognitive radio network including two secondary users who generate two distinct traffic types: packets with deadlines and status update information. Packets are dropped only when failing to reach their destination within the deadline. Leveraging the drift-plus-penalty approach, we develop a scheduling policy to minimize the average drop rate of these packets subject to a data freshness constraint. Simulation results depict how variations in main system parameters, such as the primary user’s transmission power and energy arrival rate, impact the average drop rate. Additionally, experimental simulations confirm that our proposed policy outperforms the performance of a suggested baseline policy with a notable gap.

In this work, we investigate the security performance of an underlay cognitive radio network (CRN) with ambient backscatter communication (AmBC), where a backscattering device (BD) shares the spectrum and the receivers with the secondary user. Different from the related work, we consider a secondary user transmitter (ST) with multiple antenna in an AmBC-CRN with multiple receivers and multiple passive eavesdroppers. The ST performs joint antenna-user selection to enhance its security performance and overcome the performance degradation caused by BD interference. Considering the Nakagami-m fading model, closed-form expressions are derived for the secrecy outage probability for both the ST and the BD transmissions. Monte Carlo simulations are performed to validate the derived closed-form expressions. Numerical results show that employing joint antenna-user selection enhances the ST security performance by exploiting antenna and user diversity.

Currently, the pervasive adoption of internet of things technology sheds light on the significance of real-time status update systems. The age of information (AoI) metric has been devised to reflect the strict information timeliness of such systems. Bufferless packet management scheme has demonstrated higher effectiveness in the AoI minimization. However, the existing preemptive (PR) and non-preemptive (NP) service disciplines operate independently of the system state, thereby reducing the system’s adaptability to varying system parameter settings, such as service time distribution and traffic loading condition. Therefore, we propose an elapsed-time-based hybrid PR/NP discipline to govern the service preemptions in a single-source M/Er/1/1 queueing scheme. The preemption is declined when the elapsed service time surpasses a predetermined threshold (controlling parameter). The average AoI is analyzed using the stochastic hybrid system approach. The numerical study demonstrated that the proposed discipline, owing to its threshold parameter, fulfills the optimality of the AoI performance, especially under highly dispersed Erlang service time distributions and higher traffic loading conditions.