Multi-Channel Cooperative Spectrum Sensing and Scheduling for Cognitive IoT Networks

Year 2024, Volume: 12 Issue: 2, 177 - 88, 30.08.2024

Abdulkadir Celik

https://doi.org/10.17694/bajece.1438843

Abstract

This paper presents a novel multi-channel cooperative spectrum sensing and scheduling (MC$_2$S$_3$) framework for spectrum and energy harvesting cognitive Internet of Things (IoT) networks. We address the challenge of maximizing network throughput by formulating a combinatorial problem that jointly optimizes the sensing scheduling of primary channels (PCs), the assignment of IoT devices for sensing scheduled PCs, and the clustering and allocation of IoT nodes to efficiently use discovered idle PCs; subject to spectrum utilization and collision avoidance constraints. Recognizing the inherent complexity of the underlying NP-hard mixed-integer non-linear programming (MINLP) problem, we propose a decomposition strategy that decouples the master problem into PC exploration and exploitation sub-problems. In the exploration phase, we derive closed-form solutions for optimal sensing durations and detection thresholds that satisfies spectrum utilization and collision avoidance constraints, which are then used to develop a priority metric to rank PCs. The proposed PC ranking informs a sequential PC scheduling and IoT sensing assignment approach that exploits a linear bottleneck assignment (LBA) strategy, proceeding until further scheduling does not enhance network utility. For the exploitation phase, we leverage a non-orthogonal multiple access (NOMA) strategy to multiplex multiple IoT nodes on a single PC, employing an iterative linear sum assignment (LSA) method for efficient allocation to maximize utilization of idle PCs. Numerical results validate the efficacy of our proposed methodologies, reaching an accuracy of approximately 99% in the order of milliseconds, significantly outperforming time complexity of brute-force benchmarks.

Keywords

internet of things, cognitive radios, spectrum sensing, sensing scheduling

References

• [1] A. Celik, I. Romdhane, G. Kaddoum, and A. M. Eltawil, “A top-down survey on optical wireless communications for the internet of things,” IEEE Communications Surveys Tutorials, vol. 25, no. 1, pp. 1–45, 2023.
• [2] M. Khasawneh, A. Azab, S. Alrabaee, H. Sakkal, and H. H. Bakhit, “Convergence of iot and cognitive radio networks: A survey of applications, techniques, and challenges,” IEEE Access, vol. 11, pp. 71097– 71112, 2023.
• [3] A. Gharib, W. Ejaz, and M. Ibnkahla, “Distributed spectrum sensing for iot networks: Architecture, challenges, and learning,” IEEE Internet of Things Magazine, vol. 4, no. 2, pp. 66–73, 2021.
• [4] W. Ejaz and M. Ibnkahla, “Multiband spectrum sensing and resource allocation for iot in cognitive 5g networks,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 150–163, 2018.
• [5] N.-N. Dao, W. Na, A.-T. Tran, D. N. Nguyen, and S. Cho, “Energyefficient spectrum sensing for iot devices,” IEEE Systems Journal, vol. 15, no. 1, pp. 1077–1085, 2021.
• [6] F. Zhou, Y. Wu, Y.-C. Liang, Z. Li, Y. Wang, and K.-K. Wong, “State of the art, taxonomy, and open issues on cognitive radio networks with noma,” IEEE Wireless Communications, vol. 25, no. 2, pp. 100–108, 2018. [7] S. Arzykulov, A. Celik, G. Nauryzbayev, and A. M. Eltawil, “Uavassisted cooperative cognitive noma: Deployment, clustering, and resource allocation,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 1, pp. 263–281, 2022.
• [8] P. Chauhan, S. K. Deka, B. C. Chatterjee, and N. Sarma, “Utility driven cooperative spectrum sensing scheduling for heterogeneous multichannel cognitive radio networks,” Telecommunication Systems, vol. 78, no. 1, pp. 25–37, 2021.
• [9] Y. Cao and H. Pan, “Energy-efficient cooperative spectrum sensing strategy for cognitive wireless sensor networks based on particle swarm optimization,” IEEE Access, vol. 8, pp. 214707–214715, 2020.
• [10] H. Kaschel, K. Toledo, J. T. Gomez, and M. J. F.-G. Garc ´ ´ıa, “Energyefficient cooperative spectrum sensing based on stochastic programming in dynamic cognitive radio sensor networks,” IEEE Access, vol. 9, pp. 720–732, 2021.
• [11] A. Ostovar, Y. B. Zikria, H. S. Kim, and R. Ali, “Optimization of resource allocation model with energy-efficient cooperative sensing in green cognitive radio networks,” IEEE Access, vol. 8, pp. 141594– 141610, 2020.
• [12] O. M. Al-Kofahi, H. M. Almasaeid, and H. Al-Mefleh, “Efficient ondemand spectrum sensing in sensor-aided cognitive radio networks,” Computer Communications, vol. 156, pp. 11–24, 2020.
• [13] A. Bagheri and A. Ebrahimzadeh, “Statistical analysis of lifetime in wireless cognitive sensor network for multi-channel cooperative spectrum sensing,” IEEE Sensors Journal, vol. 21, pp. 2412–2421, Jan 2021.
• [14] X. Fernando and G. Laz ˘ aroiu, “Spectrum sensing, clustering algorithms, ˘ and energy-harvesting technology for cognitive-radio-based internet-ofthings networks,” Sensors, vol. 23, no. 18, 2023.
• [15] J. Wu, C. Wang, Y. Yu, T. Song, and J. Hu, “Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks,” IET Communications, vol. 14, no. 6, pp. 1028–1036, 2020.
• [16] W. Ning, X. Huang, K. Yang, F. Wu, and S. Leng, “Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks,” Journal of Communications and Networks, vol. 22, pp. 12–22, Feb 2020.
• [17] Z. Shi, W. Gao, S. Zhang, J. Liu, and N. Kato, “Machine learningenabled cooperative spectrum sensing for non-orthogonal multiple access,” IEEE Transactions on Wireless Communications, vol. 19, pp. 5692–5702, Sep. 2020.
• [18] R. Ahmed, Y. Chen, B. Hassan, and L. Du, “Cr-iotnet: Machine learning based joint spectrum sensing and allocation for cognitive radio enabled iot cellular networks,” Ad Hoc Networks, vol. 112, p. 102390, 2021.
• [19] A. Bagheri, A. Ebrahimzadeh, and M. Najimi, “Game-theory-based lifetime maximization of multi-channel cooperative spectrum sensing in wireless sensor networks,” Wireless networks, vol. 26, no. 6, pp. 4705– 4721, 2020.
• [20] M. Rajendran and M. Duraisamy, “Distributed coalition formation game for enhancing cooperative spectrum sensing in cognitive radio ad hoc networks,” IET Networks, vol. 9, no. 1, pp. 12–22, 2020.
• [21] P. Chauhan, S. K. Deka, B. C. Chatterjee, and N. Sarma, “Cooperative spectrum prediction-driven sensing for energy constrained cognitive radio networks,” IEEE Access, vol. 9, pp. 26107–26118, 2021.
• [22] A. Gharib, W. Ejaz, and M. Ibnkahla, “Scalable learning-based heterogeneous multi-band multi-user cooperative spectrum sensing for distributed iot systems,” IEEE Open Journal of the Communications Society, vol. 1, pp. 1066–1083, 2020.
• [23] D. Goz ¨ upek, S. Buhari, and F. Alag ¨ oz, “A spectrum switching delay- ¨ aware scheduling algorithm for centralized cognitive radio networks,” IEEE Transactions on Mobile Computing, vol. 12, no. 7, pp. 1270–1280, 2013.
• [24] E. C. Y. Peh, Y.-C. Liang, and Y. L. Guan, “Optimization of cooperative sensing in cognitive radio networks: A sensing-throughput tradeoff view,” in 2009 IEEE International Conference on Communications, pp. 1–5, 2009.
• [25] S. W. Kim, “Simultaneous spectrum sensing and energy harvesting,” IEEE Transactions on Wireless Communications, vol. 18, no. 2, pp. 769– 779, 2019.
• [26] D. F. Crouse, “On implementing 2d rectangular assignment algorithms,” IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 4, pp. 1679–1696, 2016.
• [27] M. ApS, “Mosek optimization toolbox for matlab,” User’s Guide and Reference Manual, Version, vol. 4, p. 1, 2019.
• [28] M. Grant and S. Boyd, “CVX: Matlab software for disciplined convex programming, version 2.1.” http://cvxr.com/cvx, Mar. 2014.