The evolution of Dynamic Spectrum Sensing: A two-decade survey from foundations to frontiers

The growing demand for wireless connectivity has heightened the need for efficient spectrum utilization. Dynamic Spectrum Sensing (DSS), a fundamental capability of Cognitive Radio Networks (CRNs), enables real-time identification of available spectrum without interfering with licensed users. While DSS has evolved significantly, a comprehensive overview capturing its full operational context remains lacking. This survey bridges that gap by systematically analyzing 62 peer-reviewed studies published between 2005 and 2024, selected based on explicit or implicit engagement with DSS and rigorous peer-review criteria. Employing a PRISMA-based methodology, we examine major sensing strategies, including energy detection, cooperative spectrum sensing, and machine learning, based methods, alongside application domains, technologies, and evaluation methods. Special attention is given to how DSS systems address dynamic environments, including time-varying channels and real-time decision-making. Key contributions include a comprehensive literature analysis covering research trends, experimental and implementation practices, as well as environmental variability. The survey also identifies critical open challenges, including security vulnerabilities in cooperative sensing, energy constraints in IoT deployments, and limited adaptability to dynamic and mobile environments. It offers a consolidated foundation for advancing DSS research and practice, guiding future efforts toward resilient, energy-aware, and adaptive sensing solutions for emerging contexts such as 6G networks, IoT, and satellite communications.