Secure and Energy-Efficient Cognitive Radio Architecture for Scalable IoT Networks in Smart Cities

Abstract

The use of Internet of Things (IoT) applications, especially in smart cities, has heightened the pressure to have dependable spectrum and energy-effective communication. They cannot be served by traditional static spectrum allocation that can only support the dynamic and dense wireless deployments possible on smart urban environments. To this, this paper recommends secure and low-power cognitive radio (CR) architecture capable of addressing the scalability and sustainability of the next-gen IoT networks. Its main purposes are the optimization of spectrum, low-energy consumption, and improved security against radio-layer attacks. The suggested system unites the adaptive spectrum sensing, light-weight cryptographic modules (AES-CCM) and reinforcement learning-enabled energy management. NS-3 and MATLAB simulations are utilized to validate the architecture in terms of performance evaluation against major parameters such as power consumption, spectrum efficiency, and robustness to security attacks. The outcomes indicate a 34% decrease in energy usage of the state-of-the-art networking-IoT systems and a 45% improvement in spectrum utilization with a state-of-the-art networking-IoT systems when compared to baseline CR-IoT systems. Besides, architecture does well in blocking Primary User Emulation (PUE) and jamming attacks with a rate of>93 percent. The solution given based on a combination of a lightweight security and energy-aware control along with cognitive intelligence can be successfully used to make CR-based communication in smart cities more viable, as is indicated by the suggested solution. Future extensions will also look at live mapping on software-defined radio (SDR) and cooperative detection of threats with a federated learning model.