ROBUST APPROACH FOR SPECTRUM SENSING AND SPECTRUM ALLOCATION APPROACH IN COGNITIVE RADIO WIRELESS SENSOR NETWORKS

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Future services and applications dependent on the Internet of Things (IoT) stand to benefit significantly from the use of Wireless Sensor Networks (WSNs). However, WSNs operating in unlicensed frequency bands are increasingly vulnerable to interference due to spectrum congestion. Cognitive Radio Wireless Sensor Networks (CR-WSNs) provide a promising solution by allowing sensor nodes to opportunistically access licensed spectrum bands. Yet, equipping energy-constrained sensor nodes with cognitive capabilities such as channel sensing, opportunistic access, and channel switching poses significant performance and energy-efficiency challenges. The integration of WSNs with the Cognitive Internet of Things (CIoT) demands the development of robust MAC and spectrum access architectures that allow coexistence with legacy wireless systems. Existing spectrum access paradigms often suffer from increased energy consumption and higher collision rates due to interference from competing users. Moreover, limited research has been conducted on multi-channel CR-WSNs, leading to suboptimal spectrum utilization. This paper proposes an Energy-Efficient Spectrum Access (EESA) model tailored for multi-channel mobile CR-WSNs, aimed at improving the overall performance of energy-constrained cognitive radio networks. Experimental results demonstrate that EESA outperforms conventional models in terms of throughput and energy efficiency, making more effective use of available spectrum resources. To address the issue of dynamic spectrum access in mobile settings, this study introduces the Dynamic and Efficient Channel Access (DECA) method. DECA integrates both temporal and spatial information to minimize user interference and improve performance. Experimental evaluations show that DECA significantly reduces packet collisions and enhances successful packet transmissions, throughput, and energy efficiency compared to existing techniques. However, DECA does not inherently provide fairness in channel access. To overcome this limitation, the research introduces the Throughput Maximization Channel Access Fairness (TMCAF) model, which reduces interference by modeling secondary user behavior patterns. TMCAF incorporates both shared and non-shared channel access strategies to enhance network performance. Results indicate that TMCAF improves throughput and reduces network collisions compared to state-of-the-art models. However, TMCAF still lacks optimal performance guarantees. Recent advances in Deep Learning (DL), Reinforcement Learning (RL), and Game Theory (GT) have been employed for intelligent channel access in CR-WSNs. However, these approaches typically face two key limitations: Lack of balance between maximizing secondary user (SU) throughput and minimizing primary user (PU) interference in multi-channel environments. Inability to ensure fair network access for SUs in energy-constrained CR-WSNs. To address these issues, this study proposes a novel Throughput Maximization Channel Access Fairness using Game Theory (TMCAF-GT) approach. The TMCAF-GT method incorporates both shared and non-shared access techniques, leveraging game-theoretic modeling to optimize spectrum usage while ensuring access fairness and energy efficiency.