Particle Swarm Optimization in the Presence of Malicious Users in Cognitive IoT Networks with Data

Abstract

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.