Firefly-inspired stochastic resonance for spectrum sensing in CR-based IoT communications

The exponential increase in the number of the Internet of Things (IoT) necessitates dynamic and shared spectrum access at the edge of network. In cognitive radio (CR)-based IoT communications, spectrum sensing (SS) plays a pivotal role, if designed carefully, to enable a coexistence between licensed users (LUs) and unlicensed IoT devices for efficient and dynamic spectrum utilization. Though several SS techniques have been proposed in the literature, energy detection (ED) is renowned for its time and resource efficiency. Despite its suitability for IoT devices owing to its low hardware complexity and absence of a priori LU information, the detection performance of ED is poor at very low signal-to-noise ratio (SNR) channel conditions. While cooperative sensing can alleviate the performance problem of ED sensing in IoT network, significant detection cannot be achieved under adverse channel environments using non-cooperative IoT applications. Recently, stochastic resonance (SR) has been employed in CRs to boost the performance of SS in weak signal detection. In this paper, we propose a metaheuristic firefly algorithm (FFA) to determine the SR parameters through an objective function defined by the output SNR of a dynamic IoT system. In particular, we use an optimization scheme to optimally compute a noise level to achieve the best SR effect. Hence, the proposed FFA-based optimization problem significantly improves the sensing time and utilization of IoT communication channels in the weak heterogeneous IoT application introductions into the market. Our proposed system achieves a better detection probability of 80% compared to the 50% obtained through previous SR-based ED research works taking into account of SNR value of − 20 dB and a 10% false alarm probability (QFA). Moreover, for SNR value of − 20 dB, the sensing error probability of our proposed technique (20%) is 30% less than the previous SR-based ED considering QFA = 5%.