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The term “Internet of Things” (IoT) refers to the next phase of the internet's evolution (Ding et al., 2023). This theory (Liu et al., 2023b) posits that everyday objects endowed with processing, communication, and sensing capabilities may gather large volumes of data, ranging from the most fundamental physical variables to the most abstract abstractions. For future devices to be helpful, the authors may extrapolate from the criteria for IoT that they will need to be able to evaluate not only vast amounts of data but also complex information. Consequently, one may consider cognitive radio networks (CRNs) to be a more compact version of what is known as IoT. To gain spectrum access, CRNs must interact with the principal network. Spectrum sensing and dynamic spectrum access are the two strategies that make up the relationship between cognitive radio and a single core network (Wang et al., 2023a; Yang et al., 2019). This connection is facilitated by cognitive radio's ability to learn from its environment. The interaction between participants is what makes cognitive radio viable. The spectrum sensing outputs determine the implemented access behavior, as there is only one primary network. Dynamic spectrum access refers to this phenomenon, which happens when a primary user (PU) interrupts a secondary user (SU) during a communication procedure (Akyildiz et al., 2006). As a direct result of this, the SU will soon no longer have access to the channel. According to Ahmad et al. (2015), tailoring dynamic spectrum access to each main network's needs could enhance the performance of CRNs. This will allow the CRNs to communicate more effectively with the main networks. In contexts with multiple major networks, the network selection strategy can be applied. This is because the network selection strategy prioritizes the networks that have the highest priority.
Connecting primary networks with CRNs has the potential to increase network diversity, broaden the spectrum of possibilities available to the CRN system, and improve the quality of service for cognitive consumers. People have become increasingly interested in network selection for CRNs (Huang et al., 2023; Salari & Chan, 2023; Xiang et al., 2023; Kumar & Singh, 2023; Wei et al., 2023; Li et al., 2023; Dasari & Kaluri, 2023). Wei et al. (2012) simulated user behavior and evaluate the performance of the system using a range of various algorithms for dynamic spectrum access. The authors conducted this evaluation to ascertain the system's compliance with their requirements. After running the simulations to completion, the authors will formulate the most suitable access method for the various user types. The goal of this study (Wang et al., 2009) is to investigate and propose a network selection method that aims to reduce the interference that cognitive users cause to PUs.
Although it proposes a spectrum choice technique for multi-channel selection, Zeng & Wang (2013) completely disregard the impact that sensing mistakes have on spectrum use decisions. Zhang and Luo (2013) examine the impact of increased user presence on spectrum switching performance. In addition to this, Zhang and Lou (2013) provide two channel selection algorithms whose primary purpose is to increase the system's throughput to its full potential. This research, on the other hand, does not consider the likelihood of erroneous perceptions and instead functions based on the assumption that perceptual processes are error-free. The authors conducted mobile computing modeling with the assumption of flawless spectrum sensing (Wang et al., 2015). The study's findings have led to the development of a new method for network selection. This method is based on the gradient Markov performance potential theory. This inquiry will test the primary hypothesis of achieving complete spectrum sensing (Wang et al., 2024; Wang et al., 2023b).