Large-scale communication networks like the one in public safety are under the requirements of rapid, reliable, and interference-free spectrum access in the presence of natural disasters, large-scale accidents, and security threats. Olden fixed-spectrum allocation schemes do not fulfill these demands, particularly in cases of emergencies when there is congestion and breakdown of infrastructure systems in the network. The CRNs provide dynamic spectrum access, whereas centralized decision-making provides latency and scale constraints. In this study, a proposed model is an Edge-Driven Spectrum Optimization (EDSO) model which is specific to CRNs in a public safety and emergency communication setting. The advantage of the new model is the use of edge computing nodes to do localized, real-time spectrum sensing, decision making, and allocation to achieve a drastically lower response time and a better spectrum utilization. In contrast to the traditional centralized CRN models, the EDSO model takes advantage of edge intelligence to provide faster response to spectrum availability to enhance resilience during high-stress situations. The proposed EDSO model is evaluated against the conventional centralized CRN models using simulation in MATLAB 2023. The performance metrics analyzed include spectrums sensing accuracy, throughput, utilization efficiency, spectrum, end to end latency and energy consumption. Experiments have shown that EDSO model is better than the current models with up to 35 percent lower latency, 20 percent higher spectrum use, and 25 percent energy consumption in dynamic emergency conditions. This paper is a solid support of how edge intelligence should be incorporated into CRNs to make them more resilient in communication with first responders and the general population safety organizations.
Keywords
Cognitive Radio Networks, Edge Computing, Spectrum Optimization, Emergency Communication, Public Safety Networks.
S. Balasubramaniam, S. B. A V, C. Venkatachalam, and B. A, “Cognitive Edge-Driven Data Handling across Distributed Sensor Nodes for Robust Communication Networks,” ECS Journal of Solid-State Science and Technology, vol. 14, no. 4, p. 047007, Apr. 2025, doi: 10.1149/2162-8777/adc3c4.
M. Ali, F. Naeem, G. Kaddoum, and E. Hossain, “Metaverse Communications, Networking, Security, and Applications: Research Issues, State-of-the-Art, and Future Directions,” IEEE Communications Surveys & Tutorials, vol. 26, no. 2, pp. 1238–1278, 2024, doi: 10.1109/comst.2023.3347172.
W. S. Admass, Y. Y. Munaye, and A. A. Diro, “Cyber security: State of the art, challenges and future directions,” Cyber Security and Applications, vol. 2, p. 100031, 2024, doi: 10.1016/j.csa.2023.100031.
Alauthman and T. Shraa, “Deep Reinforcement Learning-Driven Dynamic Spectrum Access in Dense Wi-Fi Environments,” IEEE Access, vol. 13, pp. 178663–178680, 2025, doi: 10.1109/access.2025.3621489.
F.-Y. Wang, L. Yang, X. Cheng, S. Han, and J. Yang, “Network softwarization and parallel networks: beyond software-defined networks,” IEEE Network, vol. 30, no. 4, pp. 60–65, Jul. 2016, doi: 10.1109/mnet.2016.7513865.
J. Liu, Y. Lu, H. Wu, B. Ai, A. Jamalipour, and Y. Zhang, “Joint Task Coding and Transfer Optimization for Edge Computing Power Networks,” IEEE Transactions on Network Science and Engineering, vol. 12, no. 4, pp. 2783–2796, Jul. 2025, doi: 10.1109/tnse.2025.3554100.
H. Fang et al., “Self-Healing in Knowledge-Driven Autonomous Networks: Context, Challenges, and Future Directions,” IEEE Network, vol. 38, no. 6, pp. 425–432, Nov. 2024, doi: 10.1109/mnet.2024.3416850.
M. Zneit, Md. N. Absur, S. Saha, and S. Debroy, “Static Object Classification Using WiFi Signals,” 2025 IEEE 50th Conference on Local Computer Networks (LCN), pp. 1–7, Oct. 2025, doi: 10.1109/lcn65610.2025.11146325.
Z. Wan, S. Liu, Z. Xu, W. Ni, Z. Chen, and F. Wang, “Semantic Communication Method Based on Compression Ratio Optimization for Vision Tasks in the Artificial Intelligence of Things,” IEEE Transactions on Consumer Electronics, vol. 70, no. 2, pp. 4934–4944, May 2024, doi: 10.1109/tce.2023.3328905.
W. Abbass, N. Abbas, U. Majeed, W. Nawaz, Q. Abbas, and A. Hussain Farooqi, “A Cyber Resilient Framework for V2X Enabled Roundabouts in Intelligent Transportation Systems,” IEEE Access, vol. 13, pp. 154775–154802, 2025, doi: 10.1109/access.2025.3604095.
D. K. Sah, M. Vahabi, and H. Fotouhi, “A Comprehensive Review on 5G IIoT Test-Beds,” IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 4139–4163, May 2025, doi: 10.1109/tce.2025.3572058.
Y. Jie and A. E. Kamal, “Multi-objective multicast routing optimization in Cognitive Radio Networks,” 2014 IEEE Wireless Communications and Networking Conference (WCNC), pp. 2576–2581, Apr. 2014, doi: 10.1109/wcnc.2014.6952814.
Rauniyar, J. M. Jang, and S. Y. Shin, “Optimal Hard Decision Fusion Rule for Centralized and Decentralized Cooperative Spectrum Sensing in Cognitive Radio Networks,” Journal of Advances in Computer Networks, vol. 3, no. 3, pp. 207–212, 2015, doi: 10.7763/jacn.2015.v3.168.
S. Shang and M. Zhou, “A resource allocation algorithm based on hybrid spider wasp optimization for cognitive radio networks,” Physical Communication, vol. 70, p. 102625, Jun. 2025, doi: 10.1016/j.phycom.2025.102625.
Singh, R. Kulshrestha, and V. Poonia, “Multi-objective optimization of advanced sleep mode for energy saving in cognitive radio network,” Computer Communications, vol. 241, p. 108232, Sep. 2025, doi: 10.1016/j.comcom.2025.108232.
M. K. Chaudhary and A. K. Bhatt, “Augmenting TCP Communication Efficiency in Cognitive Radio Networks Using Iterative Dimensional Neural Optimization,” International Journal of Communication Systems, vol. 38, no. 12, Jul. 2025, doi: 10.1002/dac.70158.
L. Zeng and S. McGrath, “Spectrum efficiency optimization in multiuser Ultra Wideband cognitive radio networks,” 2010 7th International Symposium on Wireless Communication Systems, pp. 1006–1010, Sep. 2010, doi: 10.1109/iswcs.2010.5624348.
R. A. Devi, “Increasing Throughput Efficiency of Ad-hoc Cognitive Radio Networks Using Neural Network Techniques,” IOSR Journal of Electronics and Communication Engineering, vol. 8, no. 4, pp. 59–66, 2013, doi: 10.9790/2834-0845966.
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Ishwarya TM
Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.
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Ishwarya TM, “Edge Driven Spectrum Optimization in Cognitive Radio Networks for Public Safety and Emergency Communication Systems”, Journal of Computer and Communication Networks, pp. 174-185, 2025, doi: 10.64026/JCCN/2025017.