Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing
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Abstract
The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature.
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Buttar, Avtar Singh. "Fundamental operations of cognitive radio: A survey." 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2019, pp. 1-5.
Gao, Jing. "On the successful transmission probability of cooperative cognitive radio ad hoc networks." Ad Hoc Networks 58 (2017): 99-104.
Patil, Vilaskumar M., and Siddarama R. Patil. "A survey on spectrum sensing algorithms for cognitive radio." 2016 international conference on advances in human machine interaction (HMI). IEEE, 2016, pp. 1-5.
Homayounzadeh, Alireza, and Mehdi Mahdavi. "Performance analysis of cooperative cognitive radio networks with imperfect sensing." 2015 International Conference on Communications, Signal Processing, and their Applications (ICCSPA'15). IEEE, 2015.
Y. Zhao, M. Song, C. Xin, and M. Wadhwa, “Spectrum sensing based on three-state model to accomplish all-level fairness for co-existing multiple cognitive radio networks,” in IEEE Proc. 2012 INFOCOM, pp. 1782– 1790.
Jan Soli?ski, & Dr. Nitin Sherje. (2022). A Low Voltage Novel High-Performance Hybrid Full Adder for VLSI Circuit. Acta Energetica, (03), 09–14. Retrieved from http://actaenergetica.org/index.php/journal/article/view/471
Akyildiz, Ian F., Brandon F. Lo, and Ravikumar Balakrishnan. "Cooperative spectrum sensing in cognitive radio networks: A survey." Physical communication 4.1 (2011): 40-62.
Li, Zan, et al. "Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks." IET Communications 12.19 (2018): 2485-2492.
Amrutha, V., and K. V. Karthikeyan. "Spectrum sensing methodologies in cognitive radio networks: A survey." 2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT). IEEE, 2017.
Tadeusz Chmielniak, & Nadica Stojanovic. (2022). Design of Computer Aided Design in the Field of Mechanical Engineering . Acta Energetica, (01), 08–16. Retrieved from http://actaenergetica.org/index.php/journal/article/view/460
Leibovitz, John S. "The great spectrum debate: A commentary on the fcc spectrum policy task force's report on spectrum rights and responsibilities." Yale JL & Tech. 6 (2003): 390.
Uribe, José de Jesús Rugeles, Edward Paul Guillen, and Leonardo S. Cardoso. "A technical review of wireless security for the internet of things: Software defined radio perspective." Journal of King Saud University-Computer and Information Sciences (2021).
Ali, Abdelmohsen, and Walaa Hamouda. "Advances on spectrum sensing for cognitive radio networks: Theory and applications." IEEE communications surveys & tutorials 19.2 (2016): 1277-1304.
Wang, Xiong, et al. "Millimeter wave communication: A comprehensive survey." IEEE Communications Surveys & Tutorials 20.3 (2018): 1616-1653.
T. Qin, H. Yu, C. Leung, “Towards a trust-aware cognitive radio architecture,” ACM SIGMOBILE Mobile Computing and Communications Review. vol. 13, no. 2, PP. 86-95, Sept. 2009.
K. Zeng, Q. H Peng, Y. X Tang, “Mitigating spectrum sensing data falsification attacks in hard-decision combining cooperative spectrum sensing,” Science China, vol. 57, no. 4, pp. 1-9, April 2014.
Q. Q Pei, B. B Yuan, L. Li, H. N Li, “A sensing and etiquette reputationbased trust management for centralized cognitive radio networks,” Neurocomputing, vol. 101, no. 3, pp.129-138, Feb. 2013.
H. Chen, M. Zhou, L. Xie, et al, “Joint spectrum sensing and resource allocation scheme in cognitive radio networks with spectrum sensing data falsification attack,” IEEE Transactions on Vehicular Technology, vol. 65, no. 11, pp. 9181-9191, Jan. 2016.
J. Y Feng, G. Y Lu, H Chang, “Behave well: How to win a pop vacant band via cooperative spectrum sensing,” KSII Transactions on Internet and Information Systems, vol. 9, no. 2, pp. 1321-1336, May 2015.
S. Kar, S. Sethi, R. K Sahoo, “A multi-factor trust management scheme for secure spectrum sensing in cognitive radio networks,” Wireless Personal Communications, vol. 97, no. 2, pp. 2523-2540, June 2017.
Mourougayane, Kaliappan, et al. "A robust multistage spectrum sensing model for cognitive radio applications." AEU-International Journal of Electronics and Communications 110 (2019): 152876.
P. Kaligineed i, M. Khabbazian, and V. Bhargava, “Malicious user detection in a cognitive radio cooperative sensing system,” IEEE Trans. Wireless Commun., vol. 9, pp. 2488–2497, 2010.
O. Fatemieh, R. Chandra, and C. Gunter, “Secure collaborative sensing for crowd sourcing spectrum data in white space networks,” in Proc. 2010 IEEE DySPAN, pp. 1–12.
H. Li and Z. Han, “Catch me if you can: an abnormality detection approach for collaborative spectrum sensing in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 9, pp. 3554–3565, 2010.
S. Liu, Y. Chen, W. Trappe, and L. Greenstein, “Aldo: an anomaly detection framework for dynamic spectrum access networks,” in Proc. 2009 IEEE INFOCOM, pp. 675–683, 2009.
O. Fatemieh, A. Farhadi, R. Chandra, and C. Gunter, “Using classification to protect the integrity of spectrum measurements in white space networks,” Proc. NDSS, vol. 11, 2011.
F. Yu, H. Tang, M. Huang, Z. Li, and P. Mason, “Defense against spectrum sensing data falsification attacks in mobile ad hoc networks with cognitive radios,” in Proc. 2009 IEEE Military Commun. Conf., pp. 1–7.
Y. Zhao, M. Song, and C. Xin, “A weighted cooperative spectrum sensing framework for infrastructure-based cognitive radio networks,” Comput. Commun., vol. 34, pp. 1510–1517, 2011.
H. Wang and Q. Li, “Efficient implementation of public key cryptosystems on MICAz and TelosB motes,” College of William and Mary, Williamsburg, VA, Tech. Rep., Oct. 2005.
H. Wang, B. Sheng, C. C. Tan, and Q. Li, “WM-ECC: an elliptic curve cryptography suite on sensor motes,” College of William and Mary, Williamsburg, VA, Tech. Rep., 2007.
K. Zeng, P. Pawelczak and D. Cabric, ”Reputation-based cooperative spectrum sensing with trusted nodes assistance,” IEEE Commun. Lett., vol. 14, no. 3, pp. 226-228, March 2010.
D. Das and S. Deshmukh, “Ambiguity-region analysis for double threshold energy detection in cooperative spectrum sensing,” 2017 9th International Conference on Communication Systems and Networks (COMSNETS), Bangalore, pp. 123-127, 2017.
R. Zhang, J. Zhang, Y. Zhang and C. Zhang, ”Secure crowdsourcingbased cooperative pectrum sensing,” in Proc. IEEE INFOCOM, pp. 2526-2534, 2013.
M. F. Amjad, B. Aslam and C. C. Zou, “Reputation Aware Collaborative Spectrum Sensing for Mobile Cognitive Radio Networks,” MILCOM 2013 - 2013 IEEE Military Communications Conference, San Diego, CA, pp. 951-956, 2013.
O. Fatemieh, R. Chandra and C. A. Gunter, “Secure Collaborative Sensing for Crowd Sourcing Spectrum Data in White Space Networks,” 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN), Singapore, pp. 1-12, 2010.
F. Benedetto, G. Giunta, A. Tedeschi and E. Guzzon, “Performance improvements of cooperative spectrum sensing in cognitive radio networks with correlated cognitive users,” 2015 38th International Conference on Telecommunications and Signal Processing (TSP), Prague, pp. 1-5, 2015.
F. Benedetto, A. Tedeschi, G. Giunta and P. Coronas, ”Performance improvements of reputation-based cooperative spectrum sensing,” in IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Valencia, 2016, pp. 1-6.
M. A Morid and M. Shajari, “An enhanced e-commerce trust model for community based centralized systems,” Electronic Commerce Research, vol. 12, no. 4, pp. 409-427, Nov. 2012.
X. Y Li, F. Zhou and X. D Yang, “Scalable feedback aggregating (SFA) overlay for large-scale P2P trust management,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 10, pp. 1944-1957, Oct. 2012.
L. C Ma, X. F Liu, Q. Q Pei and Y. Xiang, “Privacy-preserving reputation management for edge computing enhanced mobile crowdsensing,” IEEE Transactions on Services Computing, to appear.
M. Li, Y. Xiang, B. Zhang, et al, “A trust evaluation scheme for complex links in a social network: a link strength perspective,” Applied Intelligence, vol. 44, no. 4, pp. 969-987, June 2016.
Chalapathi, M. M., et al. "Ensemble Learning by High-Dimensional Acoustic Features for Emotion Recognition from Speech Audio Signal." Security and Communication Networks 2022 (2022).
Reddy, K. Uday Kumar, S. Shabbiha, and M. Rudra Kumar. "Design of high-security smart health care monitoring system using IoT." Int. J 8 (2020)
A. J?sang, R. Ismail, “The beta reputation system,” in Proc. the 15th Bled Electronic Commence Conference, June 2002, pp.1-14.
Rudra Kumar, M., Rashmi Pathak, and Vinit Kumar Gunjan. "Machine Learning-Based Project Resource Allocation Fitment Analysis System (ML-PRAFS)." Computational Intelligence in Machine Learning. Springer, Singapore, 2022. 1-14
Suneel, Chenna Venkata, K. Prasanna, and M. Rudra Kumar. "Frequent data partitioning using parallel mining item sets and MapReduce." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2.4 (2017).
Gunjan, Vinit Kumar, and Madapuri Rudra Kumar. "Predictive Analytics for OSA Detection Using Non-Conventional Metrics." International Journal of Knowledge-Based Organizations (IJKBO) 10.4 (2020): 13-23
Miao, Chenglin, et al. "Privacy-preserving truth discovery in crowd sensing systems." ACM Transactions on Sensor Networks (TOSN) 15.1 (2019): 1-32.
Carmines, Edward G., and Richard A. Zeller. “Reliability and validity assessment”. Vol. 17. Sage publications, 1979