not-yet-known not-yet-known not-yet-known unknown In order to explore more spectrum resources to support sensors and its related applications, cognitive wireless sensor networks (CWSNs) have emerged to identify available channels being underutilized by the primary user (PUs). To improve the detection accuracy of the PU signal, cooperative spectrum sensing (CSS) among sensors paradigm is proposed to make a global decision about the PU status for CWSNs. However, CSS is susceptible to Byzantine attack from malicious sensor nodes due to its open nature, resulting in wastage of spectrum resources or causing harmful interference to PUs. To suppress the negative impact of Byzantine attack, this paper proposes a beta distribution function (BDF) for CSS among multiple sensors, which includes a sequential process, beta reputation model, and weight evaluation. Based on sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on the global decision, respectively. Finally, the numerical simulation results demonstrate that compared to SPRT and weighted sequential probability ratio test (WSPRT), the proposed BDF has outstanding effects in terms of the error probability, and average number of samples under various attack ratios and probabilities.

With the rapid growth of internet of thing (IoT) wireless devices, cooperative spectrum sensing (CSS) has emerged as a promising solution to leverage the spatial diversity of multiple IoT sensing nodes (SNs) for spectrum availability. However, the cooperative paradigm also incurs increased cooperative costs between each SN and the fusion center (FC), leading to decreased cooperative efficiency and achievable throughput, especially in large-scale cognitive IoTs (CIoTs). To address these challenges, we present a sequential detection with feedback information (SD-FI) approach in this paper. To achieve this objective, we propose a two-way CSS model that formulates an optimization problem of Bayes cost in a quickest detection framework with feedback. To solve this optimization problem, we derive the structure of the optimal local decision rule from the local decision function and determine the optimal detection threshold in conjunction with the cost function. Following the optimal threshold pair, we implement the optimal SD-FI and theoretically demonstrate the uniqueness of the optimal threshold and optimal sensing time. Simulation results demonstrate superiority of our proposed approach in terms of cooperative performance (i.e., detection performance and Bayes cost) and sample size. Notably, even with limited sensing time, our proposed SD-FI exhibits high throughput, highlighting its effectiveness in enhancing spectrum availability and utilization in cognitive IoTs.