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Cognitive Radio Engine Design for IoT Using Real-Coded Biogeography-Based Optimization and Fuzzy Decision Making

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Abstract

The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine, while posing new challenges. Cognitive radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of cognitive radio networks into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. Evolutionary algorithms are suitable techniques to solve the above-mentioned problem. In this paper, we design a CR engine for wireless multicarrier transceivers using real-coded biogeography-based optimization (RCBBO). The CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. RCBBO uses a mutation operator in order to improve the diversity of the population and enhance the exploration ability of the original BBO algorithm. The simulation results show that the RCBBO driven CR engine can obtain better results than the original BBO and outperform results from the literature. Moreover, RCBBO is more efficient when applied to high-dimensional problems in cases of multicarrier system.

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References

  1. Alberti, A., Mazzer, D., Bontempo, M., de Oliveira, L., da Rosa Righi, R., & Cerqueira, Sodr A. J. (2017). Cognitive radio in the context of internet of things using a novel future internet architecture called novagenesis. Computers and Electrical Engineering, 57, 147–161. doi:10.1016/j.compeleceng.2016.07.008.

    Article  Google Scholar 

  2. Ashrafinia, S., Pareek, U., Naeem, M., & Lee, D. Source and relay power selection using biogeography-based optimization for cognitive radio systems. In 2011 IEEE vehicular technology conference (VTC Fall) (pp. 1–5).

  3. Baban, S., Denkoviski, D., Holland, O., Gavrilovska, L., & Aghvami, H. (2013). Radio access technology classification for cognitive radio networks. In 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), London (pp. 2718–2722). doi:10.1109/PIMRC.2013.6666608.

  4. Bhattacharya, A., & Chattopadhyay, P. K. (2010). Biogeography-based optimization for different economic load dispatch problems. IEEE Transactions on Power Systems, 25(2), 1064–1077.

    Article  Google Scholar 

  5. Boussad, I., Chatterjee, A., Siarry, P., & Ahmed-Nacer, M. (2011). Hybridizing biogeography-based optimization with differential evolution for optimal power allocation in wireless sensor networks. IEEE Transactions on Vehicular Technology, 60(5), 2347–2353.

    Article  Google Scholar 

  6. Chen, S., Newman, T. R. Evans J. B. & Wyglinski, A. M. (2010). Genetic algorithm-based optimization for cognitive radio networks. In 2010 IEEE Sarnoff Symposium, Princeton, NJ (pp. 1–6). doi:10.1109/SARNOF.2010.5469780.

  7. Chen, W., Li, T., & Yang, T. (2015). Intelligent control of cognitive radio parameter adaption: Using evolutionary multi-objective algorithm based on user preference. Ad Hoc Networks, 26, 3–16.

    Article  Google Scholar 

  8. da Silva Maximiano, M., Vega-Rodrguez, M. A., Gmez-Pulido, J. A., & Snchez-Prez, J. M. (2013). A new multiobjective artificial bee colony algorithm to solve a real-world frequency assignment problem. Neural Computing and Applications, 22(7–8), 1447–1459.

    Article  Google Scholar 

  9. Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18. doi:10.1016/j.swevo.2011.02.002.

    Article  Google Scholar 

  10. García, S., Molina, D., Lozano, M., & Herrera, F. (2008). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15(6), 617–644. doi:10.1007/s10732-008-9080-4.

    Article  MATH  Google Scholar 

  11. Gavrilovska, L., Atanasovski, V., Macaluso, I., & Dasilva, L. (2013). Learning and reasoning in cognitive radio networks. IEEE Communications Surveys and Tutorials, 15(4), 1761–1777. doi:10.1109/SURV.2013.030713.00113.

    Article  Google Scholar 

  12. Gong, W., Cai, Z., Ling, C. X., & Li, H. (2010). A real-coded biogeography-based optimization with mutation. Applied Mathematics and Computation, 216(9), 2749–2758.

    Article  MathSciNet  MATH  Google Scholar 

  13. Goudos, S.K., Baltzis, K.B., Siakavara, K., Samaras, T., Vafiadis, E., & Sahalos, J.N. Reducing the number of elements in linear arrays using biogeography-based optimization. In Proceedings of 6th European conference on antennas and propagation, EuCAP 2012 (pp. 1615–1618).

  14. Hauris, J. F. (2007). Genetic algorithm optimization in a cognitive radio for autonomous vehicle communications. In Proceedings of the 2007 IEEE international symposium on computational intelligence in robotics and automation, CIRA 2007 (pp. 427–431).

  15. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Article  Google Scholar 

  16. Jamuna, K., & Swarup, K. S. (2011). Power system observability using biogeography based optimization. In International conference on sustainable energy and intelligent systems (SEISCON 2011) (pp. 384–389).

  17. Kankanala, P., Srivastava, S. C., Srivastava, A. K., & Schulz, N. N. (2012). Optimal control of voltage and power in a aulti-zonal MVDC shipboard power system. In IEEE Transactions on Power Systems, May 2012 (Vol. 27, No. 2, pp. 642–650). doi:10.1109/TPWRS.2011.2178274.

  18. Kaur, K., Rattan, M., & Patterh, M. S. (2014). Biogeography-based optimisation of cognitive radio system. International Journal of Electronics, 101(1), 24–36.

    Article  Google Scholar 

  19. Kavitha, R., & Thottungal, R. (2015). Fpga based hardware implementation of wthd minimisation in asymmetric multilevel inverter using biogeographical based optimisation. Istanbul University—Journal of Electrical and Electronics Engineering, 14(2), 1799–1807.

    Google Scholar 

  20. Khan, A. A., Rehmani, M. H., & Rachedi, A. (2016). When cognitive radio meets the internet of things? In 2016 international wireless communications and mobile computing conference (IWCMC) (pp. 469–474). doi:10.1109/IWCMC.2016.7577103.

  21. Lee, C. Y., & Yao, X. (2004). Evolutionary programming using mutations based on the levy probability distribution. IEEE Transactions on Evolutionary Computation, 8(1), 1–13. doi:10.1109/TEVC.2003.816583.

    Article  Google Scholar 

  22. Mandal, K. K., Bhattacharya, B., Tudu, B., & Chakraborty, N. (2011). A novel population-based optimization algorithm for optimal distribution capacitor planning. In 2011 International conference on energy, automation and signal, Bhubaneswar, Odisha (pp. 1–6). doi:10.1109/ICEAS.2011.6147075.

  23. Mitola, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. Doctoral dissertation, KTH.

  24. Mitola III, J., & Maguire, G. Q, Jr. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  25. Newman, T. R. (2008). Multiple objective fitness functions for cognitive radio adaptation. Dissertation, University of Kansas.

  26. Newman, T. R., Barker, B. A., Wyglinski, A. M., Agah, A., Evans, J. B., & Minden, G. J. (2007). Cognitive engine implementation for wireless multicarrier transceivers. Wireless Communications and Mobile Computing, 7(9), 1129–1142.

    Article  Google Scholar 

  27. Newman, T. R., Rajbanshi, R., Wyglinski, A. M., Evans, J. B., & Minden, G. J. (2008). Population adaptation for genetic algorithm-based cognitive radios. Mobile Networks and Applications, 13(5), 442–451.

    Article  Google Scholar 

  28. Nokia: Lte evolution for IoT connectivity. Technical report, Nokia (2016). http://resources.alcatel-lucent.com/asset/200178.

  29. Pradhan, P. M., & Panda, G. (2012). Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making. Swarm and Evolutionary Computation, 7, 7–20.

    Article  Google Scholar 

  30. Pradhan, P. M., & Panda, G. (2014). Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: A survey. Ad Hoc Networks, 17, 129–146.

    Article  Google Scholar 

  31. Proakis, J. G. (1995). Digital Communications (4th ed.). New York: McGraw-Hill.

    MATH  Google Scholar 

  32. Qureshi, F., Iqbal, R., & Asghar, M. (2017). Energy efficient wireless communication technique based on cognitive radio for internet of things. Journal of Network and Computer Applications, 89, 14–25. doi:10.1016/j.jnca.2017.01.003.

    Article  Google Scholar 

  33. Rathi, A., Agarwal, A., Sharma, A., & Jain, P. (2011). A new hybrid technique for solution of economic load dispatch problems based on biogeography based optimization. In TENCON 2011—2011 IEEE region 10 conference (pp. 19–24).

  34. Rawat, P., Singh, K. D., & Bonnin, J. M. (2016). Cognitive radio for M2M and internet of things: A survey. Computer Communications, 94, 1–29. doi:10.1016/j.comcom.2016.07.012.

    Article  Google Scholar 

  35. Reeves, C. R., & Fogarty, T. C. (1994). Genetic algorithms and neighbourhood search. In P. Siarry, & S. Z. Michalewicz (Eds.), Evolutionary Computing: AISB Workshop Leeds, U.K., April 11–13 Selected Papers (pp. 115–130). Berlin: Springer. doi:10.1007/978-3-540-72960-0_19.

    Chapter  Google Scholar 

  36. Silva, M. A. C., dos S Coelho, L., & Freire, R. Z. Biogeography-based optimization approach based on predator-prey concepts applied to path planning of 3-dof robot manipulator. In 2010 IEEE conference on emerging technologies and factory automation (ETFA) (pp. 1–8).

  37. Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713.

    Article  Google Scholar 

  38. Spectrum policy task force report. Technical report (2002).

  39. Tan, X., Zhang, H., & Hu, J. (2014). A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database. Annales des Telecommunications/Annals of Telecommunications, 69(11–12), 593–605.

    Article  Google Scholar 

  40. Vlacheas, P., Giaffreda, R., Stavroulaki, V., Kelaidonis, D., Foteinos, V., Poulios, G., et al. (2013). Enabling smart cities through a cognitive management framework for the internet of things. IEEE Communications Magazine, 51(6), 102–111. doi:10.1109/MCOM.2013.6525602.

    Article  Google Scholar 

  41. Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., et al. (2014). Cognitive internet of things: A new paradigm beyond connection. IEEE Internet of Things Journal, 1(2), 129–143. doi:10.1109/JIOT.2014.2311513.

    Article  Google Scholar 

  42. Yang, Z., Yao, X., & He, J. (2008). The next paradigm shift: From vehicular networks to vehicular clouds. In P. Siarry, & S. Z. Michalewicz (Eds.), Mobile Ad hoc networking: The cutting edge directions (pp. 397–414). Berlin: Springer. doi:10.1007/978-3-540-72960-0_19

    Chapter  Google Scholar 

  43. Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), 82–102.

    Article  Google Scholar 

  44. Yu, Y., Tan, X., Xie, Y., & Chen, J. (2013). Cognitive radio decision engine based on binary chaotic particle swarm optimization. Journal of Information and Computational Science, 10(12), 3751–3761.

    Article  Google Scholar 

  45. Zhang, Z., & Xie, X. (2008). Application research of evolution in cognitive radio based on ga. In 2008 3rd IEEE conference on industrial electronics and applications, ICIEA 2008 (pp. 1575–1579).

  46. Zhao, N., Li, S., & Wu, Z. (2012). Cognitive radio engine design based on ant colony optimization. Wireless Personal Communications, 65(1), 15–24.

    Article  Google Scholar 

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Correspondence to Sotirios K. Goudos.

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Paraskevopoulos, A., Dallas, P.I., Siakavara, K. et al. Cognitive Radio Engine Design for IoT Using Real-Coded Biogeography-Based Optimization and Fuzzy Decision Making. Wireless Pers Commun 97, 1813–1833 (2017). https://doi.org/10.1007/s11277-017-4646-9

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