Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks
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The cognitive radio network (CR) is a primary and promising technology to distribute the spectrum assignment to an unlicensed user (secondary users) which is not utilized by the licensed user (primary user).The cognitive radio network frames a reactive security policy to enhance the energy monitoring while using the CR network primary channels. The CR network has a good amount of energy capacity using battery resource and accesses the data communication via the time-slotted channel. The data communication with moderate energy-level utilization during transmission is a great challenge in CR network security monitoring, since intruders may often attack the network in reducing the energy level of the PU or SU. The framework used to secure the communication is using the discrete-time partially observed Markov decision process. This system proposes a modern data communication-secured scheme using private key encryption with the sensing results, and eclat algorithm has been proposed for energy detection and Byzantine attack prediction. The data communication is secured using the AES algorithm at the CR network, and the simulation provides the best effort-efficient energy usage and security.
KeywordsMachine learning Wireless communication Cognitive radio networks Byzantine attack Éclat algorithm
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The authors declare that they have no conflict of interest.
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The authors follow the ethical information provided in the journal and hereby abide the same with the journal. I confirm that this work is original and has not been published elsewhere nor it is currently under consideration for publication elsewhere.
- 2.Yan M et al (2011) Game-theoretic approach against selfish attacks in cognitive radio networks. In: IEEE/ACIS 10th International conference computer and information science (ICIS)Google Scholar
- 5.Alahmadi A, Abdelhakim M, Ren J, Li T (2013) Mitigating primary user emulation attacks in cognitive radio networks using advanced encryption standard. In: Global communications conference (GLOBECOM)Google Scholar
- 8.Patel K, Durvesh A (2016) Detection of multiple selfish attack nodes in cognitive radio. IJARIIE-ISSN (O)-2395-4396 2(2)Google Scholar
- 11.Holcomb S, Rawat DB (2016) Recent security issues on cognitive radio networks: a survey. In: Proceedings of Southeast Con, pp 1–6Google Scholar
- 12.Kim JM, Lee HS, Yi J, Park M (2006) Power adaptive data encryption for energy-efficient and secure communication in solar-powered wireless sensor networks. J Sens vol. 2016, Feb. 2016, Art. no. 2678269. NIST Standards (2001). Advanced Encryption Standard (AES)Google Scholar
- 14.Lee P, Eu ZA, Han M, Tan H-P (2011) Empirical modeling of a solar-powered energy harvesting wireless sensor node for time-slotted operation. In: Proceedings of IEEE wireless communications and networking conference, pp 179–184Google Scholar
- 15.Abdullah HN, Abed HS (2016) Improvement of energy consumption in cognitive radio by reducing the number of sensed samples. In: Proceedings of Al-Sadeq international conference multidisciplinary IT communication science and applications (AIC-MITCSA), pp 1–6Google Scholar
- 19.Amiri (2010) Measurements of energy consumption and execution time of different operations on Tmote Sky sensor nodes. Ph.D. dissertation, Faculty Informatiky, Masaryk Univerzita, Brno, Czech RepublicGoogle Scholar
- 20.Sen J (2013) A survey on security and privacy protocols for cognitive wireless sensor networks. J Netw Inf Secur 1:1–43Google Scholar
- 21.Gill RK, Chawla P, Sachdeva M. Study of LEACH routing protocol for wireless sensor networksGoogle Scholar
- 23.Sánchez-Salas DA, Cuevas-Ruíz JL, González-Mendoza M (2012) Wireless channel model with Markov chains using MATLAB. In: MATLAB—a fundamental tool for scientific computing and engineering applications, vol 2. Rijeka, Croatia: InTech, 2012, ch. 11Google Scholar
- 27.Vimal S et al (2016) Secure data packet transmission in MANET using enhanced identity-based cryptography. Int J New Technol Sci Eng 3(12):35–42Google Scholar
- 33.Lin S, Li Y, Li Y, Ai B, Zhong Z (2014) Finite-state Markov channel modeling for vehicle-to-infrastructure communications. In: Proceedings of IEEE 6th international symposium wireless vehicular communications (WiVeC), pp 1–5Google Scholar