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.

The LoRa (Long Range) protocol is one of the most widely employed technologies in Internet of Things (IoT) networks, thanks to its low-power and long-range communication capabilities. However, dense LoRa networks face challenges in optimizing performance due to limited energy and spectrum resources. While traditional Reinforcement Learning (RL) algorithms represent a valid approach to promote fully-distributed optimization in LoRa networks, they may struggle to adapt to dynamic network conditions and, depending on complexity, may pose a significant computational burden on constrained IoT devices. In light of this, we propose FULMINA (Fast, mULtiarmed bandit approach for optiMal SpreadIng allocation in lorA networks), a novel fully-distributed framework for Spreading Factor allocation in LoRa networks. FULMINA is based on MultiArmed Bandit solutions, a stateless alternative to conventional RL methods. We conducted an extensive simulation campaign, comparing FULMINA with a Q-Learning-based state-of-the-art baseline. The results show that FULMINA is (i) at least 5 times faster than Q-Learning in converging to optimal solutions, and (ii) capable of achieving up to 70 % improvement in average network energy consumption, with a comparable or only slightly reduced network Packet Delivery Ratio.

Long Range (LoRa) technology, with its low-power and long-range communication capabilities, has emerged as a popular choice for Internet of Things (IoT) applications. In spite of its long communication range, single-hop LoRa networks may be extremely inefficient in high path-loss scenarios, such as urban, indoor and underground environments. One solution to this issue is to instead resort to multi-hop LoRa networks. However, the routing of the generated packets towards the LoRa network gateway poses several challenges, such as balancing the network energy consumption, minimizing the number of hops, and selecting high-quality paths towards the gateway. One way to solve this issue is to implement distributed smart algorithms that can efficiently address all this aspects in a reliable and fully-scalable way. In this regard, this paper introduces MAGELLAN, a novel routing algorithm for multi-hop LoRa networks based on Multi-Armed Bandit (MAB) learning. MAGELLAN aims to achieve efficient packet delivery while minimizing the number of hops and fairly distributing the energy consumption across the network. We have conducted an extensive numerical evaluation by using the LoRaEnergySim simulator, and compared MAGELLAN against various routing approaches, including LMHP, a state-of-the-art routing algorithm. The results show that MAGELLAN achieves a superior performance in terms of energy-fairness and Packet Delivery Ratio (PDR), therefore improving the network lifetime and performance. More in detail, MAGELLAN achieves a reduction of up to 10% in the energy consumption as compared to a the random approach, and of to 14% in the PDR as compared to LMHP approach.