Firefly-inspired stochastic resonance for spectrum sensing in CR-based IoT communications
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A P P L Y I N G A R T I F I CI A L I N T E L L I G E N C E T O T H E I N T E R N E T O F T H I N G S
Firefly-inspired stochastic resonance for spectrum sensing in CR-based IoT communications Haftu Tasew Reda1
•
Abdun Mahmood1 • Abebe Diro1 • Naveen Chilamkurti1 • Suresh Kallam2
Received: 5 August 2019 / Accepted: 24 October 2019 Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract 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%. Keywords Internet of things Spectrum sensing Energy detection Stochastic resonance Firefly algorithm Licensed user Secondary user
1 Introduction Currently, a staggering number of the Internet of Things (IoT) have been connected to a radio frequency (RF) environment at a much faster rate than ever before. A study in [1] shows that approximately 50 billion IoT connections
will exist by the end of 2020. Despite the ever-increasing nature of demand for dominantly wireless IoT system applications, the availability and bandwidth of RF spectrum is a major bottleneck for massive data-oriented IoT devices. As the majority of these devices operate
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