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A Metaheuristic Handover Model Using Network Augmentation and Game Theory for Seamless Connectivity in Heterogeneous Networks

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Abstract

Mobile wireless networks encounter a critical challenge in maintaining uninterrupted connectivity during node movement and network disruptions. Analytical engines are tailored to assess both node-level parameters like movement and communication patterns, as well as network-level aspects such as bandwidth and signal strength indicators. These analyses inform a decision engine that facilitates node handovers between networks, guided by specific parameters and constraints based on prevailing network conditions. This approach optimizes network connectivity, improving essential Quality of Service (QoS) measures like reduced delay, increased throughput, minimized energy consumption, and enhanced packet delivery ratio. Ensuring smooth network transitions requires advance notification to the target network, allowing preparations for incoming nodes and enhancing the likelihood of uninterrupted communication. However, crafting an effective pre-intimation engine often involves complexity, potentially impacting QoS metrics. To address this, our paper introduces a metaheuristic handover model utilizing network augmentation and game theory, deploying incremental learning to streamline handovers' complexities. Across various heterogeneous network scenarios, this model showcases a 10% boost in energy efficiency, 15% higher throughput, an 8% delay reduction, and a 3% improvement in packet delivery ratio compared to standard models. Ongoing evaluations aim to deepen insights into the model's performance across diverse network scenarios. Future research will focus on integrating additional network parameters and optimizing the learning algorithm for further efficiency gains."

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References

  1. Wu, Y., Zhao, G., Ni, D., & Du, J. (2021). Dynamic handoff policy for RAN slicing by exploiting deep reinforcement learning. EURASIP Journal on Wireless Communications and Networking, 2021(1), 1–17.

    Article  Google Scholar 

  2. Tan, X., Chen, G., & Sun, H. (2020). Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-020-01822-1

    Article  Google Scholar 

  3. Valiveti, H. B., & Kumar, B. A. (2021). Handoff strategies between wireless fidelity to light fidelity systems for improving video streaming in high-speed vehicular networks. International Journal of Communication Systems, 34(6), e4285.

    Article  Google Scholar 

  4. Devi, M. K., & Kandaswamy, U. (2020). Modified Artificial Bee Colony with firefly algorithm based spectrum handoff in cognitive radio network. International Journal of Intelligent Networks., 1, 67–75. https://doi.org/10.1016/j.ijin.2020.07.002

    Article  Google Scholar 

  5. Ali, E. S., Hasan, M. K., Hassan, R., Saeed, R. A., Hassan, M. B., Islam, S., et al. (2021). Machine learning technologies for secure vehicular communication in internet of vehicles: recent advances and applications. Security and Communication Networks, 2021, 1–23.

    Google Scholar 

  6. Yajnanarayana, V., Rydén, H., & Hévizi, L. (2020). 5G handover using reinforcement learning. In 2020 IEEE 3rd 5G World Forum (5GWF) (pp. 349–354). IEEE.

  7. Li, D., Li, D., & Xu, Y. (2019). Machine learning based handover performance improvement for LTE-R. In 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) (pp. 1–2). IEEE.

  8. Wang, L., Han, D., Zhang, M., Wang, D., & Zhang, Z. (2021). Deep reinforcement learning-based adaptive handover mechanism for VLC in a hybrid 6G network architecture. IEEE Access, 9, 87241–87250.

    Article  Google Scholar 

  9. Gurumallu, P. K., & Shankaraiah. (2022). NLADSS: Design of connectivity as a service (CaaS) model using node-level augmentation & dynamic sleep scheduling for heterogeneous wireless network handoffs. International Journal of Intelligent Engineering and Systems, 15(5), 273–283. https://doi.org/10.22266/ijies2022.1031.25

    Article  Google Scholar 

  10. Shi, Q., Shao, W., Fang, B., Zhang, Y., & Zhang, Y. (2019). Reinforcement learning based spectrum handoff scheme with measured PDR in cognitive radio networks. Electronics Letters. https://doi.org/10.1049/el.2019.2259

    Article  Google Scholar 

  11. Han, Z., Lei, T., Lu, Z., Wen, X., Zheng, W., & Guo, L. (2019). Artificial intelligence-based handoff management for dense WLANs: A deep reinforcement learning approach. IEEE Access, 7, 31688–31701.

    Article  Google Scholar 

  12. Soujanya, J., & Shankaraiah, S. (2022). A traceability system for processed products based on blockchain technology. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1–6). IEEE.

  13. Bazán, J. V. C., Rasgado, C., Salas, S. L., Lamont, F. G., & Bueno, J. C. (2019). Artificial intelligence techniques in handover decision: A brief re-view. Revista Ingeniantes, 6(1), 1.

    Google Scholar 

  14. Oyewobi, S. S., Hancke, G. P., Abu-Mahfouz, A. M., & Onumanyi, A. J. (2019). An effective spectrum handoff based on reinforcement learning for target channel selection in the industrial Internet of Things. Sensors, 19(6), 1395.

    Article  Google Scholar 

  15. Boutaba, R., Salahuddin, M., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo Rendon, O. (2018). A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. Journal of Internet Services and Applications. https://doi.org/10.1186/s13174-018-0087-2

    Article  Google Scholar 

  16. Alkhateeb, A., Beltagy, I., & Alex, S. (2018). Machine learning for reliable mmwave systems: Blockage prediction and proactive handoff. In 2018 IEEE Global conference on signal and information processing (GlobalSIP) (pp. 1055–1059). IEEE.

  17. Memon, S., & Maheswaran, M. (2019). Using machine learning for handover optimization in vehicular fog computing. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 182–190).

  18. Sun, Y., Feng, G., Qin, S., Liang, Y. C., & Yum, T. S. P. (2017). Reinforcement learning based handoff for millimeter wave heterogeneous cellular networks. In GLOBECOM 2017–2017 IEEE Global Communications Conference (pp. 1–6). IEEE.

  19. Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 21(3), 2224–2287.

    Article  Google Scholar 

  20. Ozturk, M., Gogate, M., Onireti, O., Adeel, A., Hussain, A., & Imran, M. (2019). A novel deep learning driven low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA). Neurocomputing. https://doi.org/10.1016/j.neucom.2019.01.031

    Article  Google Scholar 

  21. Pries, R., Staehle, D., Tran-Gia, P., & Gutbrod, T. (2008). A Seamless Vertical Handover Approach. In L. Cerdà-Alabern (Ed.), Wireless Systems and Mobility in Next Generation Internet EuroNGI 2008 Lecture Notes in Computer Science. (Vol. 5122). Berlin: Springer. https://doi.org/10.1007/978-3-540-89183-3_14

    Chapter  Google Scholar 

  22. Sun, Y., Feng, G., Qin, S., Liang, Y. C., & Yum, T. S. P. (2017). The SMART handoff policy for millimeter wave heterogeneous cellular networks. IEEE Transactions on Mobile Computing, 17(6), 1456–1468.

    Article  Google Scholar 

  23. Sun, J., Qian, Z., Wang, X., & Wang, X. (2020). Es-dqn-based vertical handoff algorithm for heterogeneous wireless networks. IEEE Wireless Communications Letters, 9(8), 1327–1330.

    Article  Google Scholar 

  24. Chen, J., Wang, Y., Li, Y., & Wang, E. (2018). QoE-aware intelligent vertical handoff scheme over heterogeneous wireless access networks. IEEE Access, 6, 38285–38293.

    Article  Google Scholar 

  25. Koushik, A. M., Matyjas, J. D., Hu, F., & Kumar, S. (2017). Channel/beam handoff control in multi-beam antenna based cognitive radio networks. IEEE Transactions on Cognitive Communications and Networking, 4(1), 30–42.

    Google Scholar 

  26. Koushik, A. M., Hu, F., & Kumar, S. (2017). Intelligent spectrum management based on transfer actor-critic learning for rateless transmissions in cognitive radio networks. IEEE Transactions on Mobile Computing, 17(5), 1204–1215.

    Google Scholar 

  27. Lin, S., Che, N., Yu, F., & Jiang, S. (2019). Fairness and load balancing in SDWN using handoff-delay-based association control and load monitoring. IEEE Access, 7, 136934–136950.

    Article  Google Scholar 

  28. Sun, X., & Ansari, N. (2017). Adaptive avatar handoff in the cloudlet network. IEEE Transactions on Cloud Computing, 7(3), 664–676.

    Article  Google Scholar 

  29. Mansouri, M., & Leghris, C. (2020). A use of fuzzy TOPSIS to improve the network selection in wireless multiaccess environments. Journal of Computer Networks and Communications, 2020, 1–12.

    Article  Google Scholar 

  30. Kassar, M., Kervella, B., & Pujolle, G. (2008). An intelligent handover management system for future generation wireless networks. EURASIP Journal on Wireless CommunicationsNetworking. https://doi.org/10.1155/2008/791691

    Article  Google Scholar 

  31. Niu, X. (2020). A secure and reliable transmission scheme for low loss high performance wireless communication system based on IoT. Journal of Ambient Intelligence and Humanized Computing, 1–8.

  32. Hebb, D. O. (2005). The organization of behavior: A neuropsychological theory. Psychology Press.

    Book  Google Scholar 

  33. Toosi, A., Bottino, A. G., Saboury, B., Siegel, E., & Rahmim, A. (2021). A brief history of AI: How to prevent another winter (a critical review). PET Clinics, 16(4), 449–469.

    Article  Google Scholar 

  34. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12–12.

    Google Scholar 

  35. Mazumdar, E., Ratliff, L. J., & Sastry, S. S. (2020). On gradient-based learning in continuous games. SIAM Journal on Mathematics of Data Science, 2(1), 103–131.

    Article  MathSciNet  Google Scholar 

  36. Zheng, Q., Zhao, P., Li, Y., Wang, H., & Yang, Y. (2021). Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Neural Computing and Applications, 33(13), 7723–7745.

    Article  Google Scholar 

  37. Zheng, Q., Zhao, P., Zhang, D., & Wang, H. (2021). MR-DCAE: Manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification. International Journal of Intelligent Systems, 36(12), 7204–7238.

    Article  Google Scholar 

  38. Zheng, Q., Zhao, P., Wang, H., Elhanashi, A., & Saponara, S. (2022). Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation. IEEE Communications Letters, 26(6), 1298–1302.

    Article  Google Scholar 

  39. Zheng, Q., Tian, X., Yu, Z., Wang, H., Elhanashi, A., & Saponara, S. (2023). DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization. Engineering Applications of Artificial Intelligence, 122, 106082.

    Article  Google Scholar 

  40. Zheng, Q., Tian, X., Yu, Z., Jiang, N., Elhanashi, A., Saponara, S., & Yu, R. (2023). Application of wavelet-packet transform driven deep learning method in PM2.5 concentration prediction: A case study of Qingdao China. Sustainable Cities and Society, 92, 104486.

    Article  Google Scholar 

  41. Navarro-Ortiz, J., Romero-Diaz, P., Sendra, S., Ameigeiras, P., Ramos-Munoz, J. J., & Lopez-Soler, J. M. (2020). A survey on 5G usage scenarios and traffic models. IEEE Communications Surveys & Tutorials, 22(2), 905–929.

    Article  Google Scholar 

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Prasanna Kumar G contributed to this paper as part of his Ph.D. research, while receiving guidance and supervision from Dr. Shankaraiah.

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Correspondence to G. Prasanna Kumar.

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Prasanna Kumar, G., Shankaraiah, N. A Metaheuristic Handover Model Using Network Augmentation and Game Theory for Seamless Connectivity in Heterogeneous Networks. Wireless Pers Commun 134, 133–150 (2024). https://doi.org/10.1007/s11277-024-10896-9

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