International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 5, 8 Oct. 2026
Cover page and Table of Contents: PDF (size: 1364KB)
ProCLAMUS: A Matrix Completion Based Proactive Spectrum Allocation Protocol for Security of IoT based CRNs
PDF (1364KB), PP.163-181
Views: 0 Downloads: 0
Author(s)
Charanjeet Singh Chahil
1,*
Milan Patnaik
2
Deepali Gupta
1
Vashek Matyas
2
Index Terms
Cognitive Radio Networks, Matrix Completion, Internet of Things, Spectrum Sharing, Spectrum Decision, Spectrum Allocation.
Abstract
Proactive prediction based channel allocation for Cognitive Radio Networks (CRNs) remains a challenging area of research. In Internet of Things (IoT) scenarios, prediction algorithms must balance resource efficiency with high accuracy. The proposed algorithm, ProCLAMUS, a Matrix Completion (MC) Based Proactive Spectrum Allocation Protocol for CRNs, couples nuclear norm matrix completion with short horizon sliding window prediction. By reconstructing sparse spectrum sensing data and down weighting inconsistent reports, ProCLAMUS enables infrequent sensing and decentralized decisions without a fusion center, reducing energy and attack surface. In a network of 40 Secondary Users (SUs), 400 Primary Users (PUs) and 400 channels with malicious data ranging from 5 to 50%, ProCLAMUS sustains the highest channel utilization (avg 89.96%) with the lowest backoff rate (3.81 s^(-1)) and sensing delay (0.58 channels/success). ProCLAMUS achieves the lowest radio energy (16.82×10^(-3) J/s), a 44–53% reduction when compared with recently proposed techniques, while using 33–43% less memory. These gains arise from sparse sensing, fusion (majority voting) and conservative allocation. The results demonstrate superior energy efficiency and robust prediction under malicious data conditions during the Spectrum Sensing Data Falsification Attack.
Cite This Paper
Charanjeet Singh Chahil, Milan Patnaik, Deepali Gupta, Vashek Matyas, "ProCLAMUS: A Matrix Completion Based Proactive Spectrum Allocation Protocol for Security of IoT based CRNs", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.5, pp. 163-181, 2026. DOI:10.5815/ijieeb.2026.05.10
Reference
[1]J. Mitola and G. Q. Maguire, “Cognitive radio: making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13–18, 1999.
[2]I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Comput. Netw., vol. 50, no. 13, pp. 2127–2159, 2006.
[3]Federal Communications Commission, “Spectrum Policy Task Force Report,” ET Docket No. 02-135, Nov. 2002.
[4]M. A. McHenry and D. McCloskey, “Spectrum occupancy measurements: Chicago, Illinois, November 16–18, 2005,” Shared Spectrum Company Tech. Rep., 2005.
[5]S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, 2005.
[6]I. F. Akyildiz, W.-Y. Lee, and K. R. Chowdhury, “CRAHNs: Cognitive radio ad hoc networks,” Ad Hoc Netw., vol. 7, no. 5, pp. 810–836, 2009.
[7]K. Tumuluru, P. Wang, and D. Niyato, “A survey on spectrum sensing algorithms for cognitive radio,” in Proc. IEEE ICCS, 2010, pp. 1–5.
[8]F. F. Digham, M.-S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Trans. Commun., vol. 55, no. 1, pp. 21–24, 2007.
[9]G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio networks,” in Proc. IEEE DySPAN, 2005, pp. 137–143.
[10]T. C. Clancy and N. Goergen, “Security in cognitive radio networks: Threats and mitigation,” in Proc. IEEE CrownCom, 2008, pp. 1–8.
[11]R. Chen, J.-M. Park, and J. H. Reed, “Defense against primary user emulation attacks in cognitive radio networks,” IEEE J. Sel. Areas Commun., vol. 26, no. 1, pp. 25–37, 2008.
[12]Y.-C. Liang, Y. Zeng, E. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1326–1337, 2008.
[13]Y. Ding et al., “On the limits of predictability in real-world radio spectrum state dynamics,” IEEE Commun. Mag., vol. 53, no. 7, pp. 178–183, 2015.
[14]M. Patnaik, V. Kamakoti, V. Matyáš, and V. Řchák, “PROLEMus: A proactive learning-based MAC protocol against PUEA and SSDF attacks in energy constrained cognitive radio networks,” IEEE Trans. Cogn. Commun. Netw., vol. 5, no. 2, pp. 400–412, 2019.
[15]M. Patnaik, G. Prabhu, C. Rebeiro, V. Matyáš, and V. Kamakoti, “ProBLeSS: A proactive blockchain based spectrum sharing protocol against SSDF attacks in cognitive radio IoBT networks,” IEEE Netw. Lett., vol. 2, no. 2, pp. 67–70, 2020.
[16]M. Patnaik, K. Bharati, and V. Matyáš, “ProMETHEUS: A secure lightweight spectrum allocation protocol against SSDF attacks in cognitive radio IoT networks,” IEEE Commun. Lett., vol. 27, no. 11, pp. 2919–2923, Nov. 2023.
[17]A. Tani and D. Marabissi, “Distributed cooperative spectrum sensing via push–sum consensus for full-duplex cognitive aerial base stations,” Future Internet, vol. 18, no. 1, Art. no. 10, Dec. 2025.
[18]M. U. Muzaffar and R. Sharqi, “A review of spectrum sensing in modern cognitive radio networks,” Telecommun. Syst., vol. 85, pp. 347–363, 2024.
[19]S. K. Jagatheesaperumal, I. Ahmad, M. Höyhtyä, S. Khan, and A. Gurtov, “Deep learning frameworks for cognitive radio networks: Review and open research challenges,” J. Netw. Comput. Appl., vol. 233, Art. no. 104051, 2025.
[20]I. E. Ezhilarasi, A. K. Rai, R. Kumar, and A. A. A. Ari, “A survey on cognitive radio network attack mitigation Using machine/deep learning and blockchain,” EURASIP J. Wireless Commun. Netw., 2023.
[21]N. Y. Al-Matari, A. T. Zahary, and A. A. Al-Shargabi, “A survey on advancements in blockchain-enabled spectrum access security for 6G cognitive radio IoT networks,” Sci. Rep., vol. 14, Art. no. 30990, 2024.
[22]K. Xie, X. Ning, X. Wang, D. Xie, J. Cao, G. Xie, and J. Wen, “Recover corrupted data in sensor networks: A matrix completion solution,” IEEE Trans. Mobile Comput., vol. 16, no. 5, pp. 1434–1448, 2016.
[23]R. H. Keshavan, A. Montanari, and S. Oh, “Matrix completion from a few entries,” IEEE Trans. Inf. Theory, vol. 56, no. 6, pp. 2980–2998, 2010.
[24]A. Narayanan, B. Natarajan, and K. P. Subbalakshmi, “Spatio-temporal spectrum sensing in cognitive radio networks using matrix completion,” in Proc. ACM MSWiM, 2014, pp. 73–80.
[25]Z. Chen, T. Cooklev, C. Chen, and C. Pomalaza-Ráez, “Modeling Primary User Emulation Attacks and Defenses in Cognitive Radio Networks,” in Proc. IEEE Int. Perf. Comput. Commun. Conf. (IPCCC), 2009, pp. 208–215.
[26]S. Shrivastava, S. Tiwari, and D. Bhargava, “A survey on security issues in cognitive radio-based cooperative networks,” IET Commun., vol. 15, no. 7, pp. 900–918, 2021.
[27]P. Ahuja, P. Sethi, and N. Chauhan, “A comprehensive survey of security threats, detection, countermeasures, and future directions for physical and network layers in cognitive radio networks,” Multimed. Tools Appl., vol. 83, no. 11, pp. 32715–32738, 2024.
[28]M. Imran et al., “Anti-jamming for cognitive radio networks with Stackelberg game-assisted DSSS approach,” EURASIP J. Wireless Commun. Netw., vol. 2024, Art. no. 73, 2024.
[29]F. D. Zemo and S. Bakkali, “A survey on security threats in cognitive radio networks based on cooperative spectrum sensing,” Int. J. Commun. Netw. Distrib. Syst., vol. 30, no. 4, pp. 433–466, 2024.
[30]J. Wang, I.-R. Chen, J. J. P. Tsai, and D.-C. Wang, “Trust-based mechanism design for cooperative spectrum sensing in cognitive radio networks,” Comput. Commun., vol. 116, pp. 98–110, 2018.
[31]L. A. Almuqren, M. Maray, F. A. Alotaibi, A. Alzahrani, A. Mahmud, and R. R. Mohammed, “Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks,” IEEE Access, vol. 12, pp. 35300–35308, 2024.
[32]E. J. Candès and B. Recht, “Exact matrix completion via convex optimization,” Commun. ACM, vol. 55, no. 6, pp. 111–119, 2012.
[33]B. Marlin, R. S. Zemel, S. Roweis, and M. Slaney, “Collaborative filtering and the missing-at-random assumption,” arXiv preprint arXiv:1206.5267, 2012.
[34]J. He, L. Balzano, and A. Szlam, “Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video,” in Proc. IEEE CVPR, 2012, pp. 1568–1575.
[35]H. Xu, C. Caramanis, and S. Sanghavi, “Robust PCA via outlier pursuit,” in Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 23, 2010.
[36]M. A. Davenport, Y. Plan, E. van den Berg, and M. Wootters, “1-bit matrix completion,” Inf. Inference, vol. 3, no. 3, pp. 189–223, 2014.
[37]S. Majumder, “One-bit spectrum sensing using Gustafson–Kessel fuzzy clustering for cognitive radio network,” Phys. Commun., vol. 62, Art. no. 102239, 2024.
[38]J. Bennett and S. Lanning, “The Netflix Prize,” in Proc. KDD Cup Workshop, 2007, pp. 3–6.
[39]J. Tan, W. Liu, T. Wang, M. Zhao, A. Liu, and S. Zhang, “A high-accurate content popularity prediction computational modeling for mobile edge computing using matrix completion technology,” Trans. Emerg. Telecommun. Technol., vol. 32, no. 6, e3871, 2021.
[40]J. Cheng, Q. Ye, H. Jiang, D. Wang, and C. Wang, “STCDG: An efficient data gathering algorithm based on matrix completion for wireless sensor networks,” IEEE Trans. Wireless Commun., vol. 12, no. 2, pp. 850–861, Feb. 2013.
[41]M. Kortas, O. Habachi, A. Bouallegue, V. Meghdadi, T. Ezzedine, and J.-P. Cances, “Robust data recovery in wireless sensor network: A learning-based matrix completion framework,” Sensors, vol. 21, no. 3, p. 1016, 2021.