With the increasing applications in the domains of ubiquitous and context-aware computing, Internet of Things (IoT) is gaining importance. The study to efficiently exploit and manage a spectrum resources for industrial IoT (IIoT) applications is currently in the interest of research community. As increasing number of IIoT devices is heading towards the future-connected society with the cost of high system complexity, to meet the growing demands of wireless communication in future, cognitive IoT (CIoT) technology is considered as a choice. Reliable detection of the vacant spectrum holes is a vital task in the CIoT network with data. However, the performance of spectrum sensing severely degraded with the existence of malicious users (MUs) which falsifies the sensing results by reporting false data to the fusion center (FC). In this paper, we focus on the use of particle swarm optimization (PSO) to safeguard the cooperative spectrum sensing (CSS) from the negative effects caused by the MUs. The effectiveness of the proposed scheme is verified numerically in various scenarios with different types of MUs through analysis and simulations.

中文翻译：

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在具有数据的认知物联网网络中存在恶意用户时的粒子群优化

随着无处不在和上下文感知计算领域的应用越来越多，物联网 (IoT) 变得越来越重要。为工业物联网 (IIoT) 应用有效开发和管理频谱资源的研究目前符合研究界的兴趣。随着越来越多的 IIoT 设备以高系统复杂性为代价走向未来连接的社会，为了满足未来不断增长的无线通信需求，认知物联网 (CIoT) 技术被认为是一种选择。可靠地检测空闲频谱空洞是 CIoT 网络中的一项重要任务。然而，由于恶意用户（MU）的存在，频谱感知的性能严重下降，恶意用户通过向融合中心（FC）报告虚假数据来伪造感知结果。在本文中，我们专注于使用粒子群优化 (PSO) 来保护协作频谱感知 (CSS) 免受 MU 造成的负面影响。通过分析和仿真，在不同类型动车组的各种场景下，数值验证了所提出方案的有效性。