AI-Powered Capabilities for IoT Spectrum Sensing Applications

The rapid expansion of Internet of Things (IoT) networks has brought significant challenges, including spectrum congestion, interference and scalability limitations due to the increasing density of connected IoT devices. Traditional radio spectrum management approaches, such as duty cycle constraints, redundancy-based communication mechanisms and energy detection techniques, have proven insufficient to effectively address these issues. This paper proposes a framework that can be used to optimize spectrum use through the integration of AI algorithms for IoT technology identification. The proposed framework, tested in real-world operating conditions, addresses critical challenges specific to spectrum sensing applications, including low signal-to-noise configurations, thereby improving performance in large-scale, high density IoT deployments.