Priority of the Network Selection Based on the Rules Select Using Deep Reinforcement Learning
DOI:
https://doi.org/10.54536/ajicti.v1i1.7983Keywords:
Cognitive Radio Network, Deep Reinforcement Learning, Fuzzy logic, Heterogeneous Networks, Internet of Things, Probability CollisionAbstract
The priority of the network selection based on the rules selection with specific time identified in the loop of procedure selection. Furthermore, there is several criteria of selection network, offers a network selection technique that is based on deep reinforcement learning. Therefore, Traditional algorithms for network selection make use of selection approaches are dependent on prior information about the network under consideration. Thus, the criteria of selection networks, once mobile terminal (MT), moves their location, it has gathers much higher various handoff information. However, on this situation MT spend much time for selecting suitable network in complex heterogeneous networks. However, to simplify the connection management and handoff dissection automatically is organized on this research work, as to provide the intelligent method for auto select connectivity for the best networks auto selection. Here, we describe the design of automatic intelligent hybrid connection between different radio resource; the challenge considering there based on the parameters on which candidate network discover from the list compression between others networks. Thus, during the mobile terminal (MT) moving. A number of parameters are measured scanning periodically. And, if there any of these parameters is logically exist predefined on the rule base of the network of connectivity for their auto select, then the decision is triggered for the decision of the handoff it becomes more reliability for connection. However, the contribution of this work. Researcher described the methods of to introduce a novel cooperative of proposed intelligent selection mechanism for multi-connection with hybrid connectivity.
Downloads
References
Amer, R., El-Sherif, A. A., Ebrahim, H., & Mokhtar, A. (2016). Cooperation and underlay mode selection in cognitive radio network. In Proceedings of the 2016 Fifth International Conference on Future Generation Communication Technologies (FGCT). IEEE. https://doi.org/10.1109/FGCT.2016.7605066
Babu, E. S., Barthwal, A., & Kaluri, R. (2023b). Sec-edge: Trusted blockchain system for enabling the identification and authentication of edge-based 5G networks. Computer Communications, 199, 10–29. https://doi.org/10.1016/j.comcom.2022.12.001
Bendaoud, F. (2017). A modified-SAW for network selection in heterogeneous wireless networks. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 15(2), 8–17.
Bendaoud, F. (2018). Management of joint radio resources in heterogeneous networks beyond 3G (Doctoral dissertation, University of Tlemcen).
Bendaoud, F. (2021). Adaptive KNN-based algorithm for network selection in next generation networks. Journal of High-Speed Networks, (4), 1–14.
Bendaoud, F., Abdennebi, M., & Didi, F. (2015). Network selection using game theory. In Proceedings of the 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT) (pp. 1–6). IEEE.
Bendaoud, F., Abdennebi, M., & Didi, F. (2020). Mobility aware network selection in a heterogeneous wireless environment. Transport and Telecommunication Journal, 21(1).
Chen, L., Zhang, D. G., Zhang, J., Zhang, T., Wang, W. J., & Cao, Y. H. (2023). A novel offloading approach of IoT user perception task based on quantum behavior particle swarm optimization. Future Generation Computer Systems, 141, 577–594. https://doi.org/10.1016/j.future.2022.12.016
Chen, S., Shi, Y., Hu, B., & Ai, M. (2016). Mobility management: Principle, technology and applications. Springer-Verlag.
Darabkh, K. A., Al-Khazaleh, H. F., Al-Zubi, R. T., Alnabelsi, S. H., & Salameh, H. B. (2023). Efficient routing protocol for optimal route selection in cognitive radio networks over IoT environment. Wireless Personal Communications, 129(1), 209–253. https://doi.org/10.1007/s11277-022-10093-6
Evangeline, C. S., & Kumaravelu, V. B. (2022). A two-phase fuzzy-based access network selection scheme for vehicular ad hoc networks. Peer-to-Peer Networking and Applications, 15(1), 107–133.
Kooshari, A., Fartash, M., Mihannezhad, P., Chahardoli, M., AkbariTorkestani, J., & Nazari, S. (2024). An optimization method in wireless sensor network routing and IoT with water strider algorithm and ant colony optimization algorithm. Evolutionary Intelligence, 17(3), 1527–1545. https://doi.org/10.1007/s12065-023-00847-x
Mbaye, M., & Krief, F. (2009). A collaborative knowledge plane for autonomic networks.
Nguyen-Vuong, Q. T., Ghamri-Doudane, Y., & Agoulmine, N. (2008). On utility models for access network selection in wireless heterogeneous networks. In Proceedings of the IEEE Network Operations and Management Symposium (NOMS 2008) (pp. 144–151). IEEE.
Qadir, J. (2015, September 3). Artificial intelligence based cognitive routing for cognitive radio networks.
Qiu, T., Chen, N., Li, K., Qiao, D., & Fu, Z. (2016). Heterogeneous ad hoc networks: Architectures, advances and challenges.
Ramya, R., & Padmapriya, K. (2023). An implementation of energy-efficient fuzzy-optimized routing in wireless sensor networks using particle swarm optimization (PSO) and whale optimization algorithm (WOA). Journal of Intelligent & Fuzzy Systems, 44(1), 595–610. https://doi.org/10.3233/JIFS-220963
Ren, K., Liu, X., Liang, W., Xu, M., Jia, X., & Xing, K. (Eds.). (2013). Wireless algorithms, systems, and applications. Springer-Verlag.
Salih, Y. K., Hang See, O., Ibrahim, R. W., Yussof, S., & Iqbal, A. (2015). A novel noncooperative game competing model using generalized simple additive weighting method to perform network selection in heterogeneous wireless networks. International Journal of Communication Systems, 28(6), 1112–1125.
Vasilakos, A. V., et al. . (Eds.). (2009). Autonomic communication: Autonomic networks. Springer Science+Business Media. https://doi.org/10.1007/978-0-387-09753-4_3
Wang, C., Sohraby, K., Jana, R., Ji, L., & Daneshmand, M. (2011). Network selection for secondary users in cognitive radio systems. In Proceedings of IEEE INFOCOM 2011. IEEE. https://doi.org/10.1109/INFCOM.2011.5935106
Wang, J., Pei, H., Wang, R., Lin, R., Fang, Z., & Shu, F. (2024). Defense management mechanism for primary user emulation attack based on evolutionary game in energy harvesting cognitive industrial Internet of Things. IEEE Transactions on Network and Service Management.
Wang, L., & Kuo, G. S. G. (2012). Mathematical modeling for network selection in heterogeneous wireless networks—A tutorial. IEEE Communications Surveys & Tutorials, 15(1), 271–292.
Yu, H. W., & Zhang, B. (2019). A hybrid MADM algorithm based on attribute weight and utility value for heterogeneous network selection. Journal of Network and Systems Management, 27(3), 756–783.
Zeng, Z., & Wang, G. (2013). Dynamic spectrum access strategy for multi-channel cognitive radio networks. International Journal of Future Generation Communication and Networking, 6(4), 157–166.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Anas A. Nicola (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
