Abstract
The innovation of services offered by cellular networks gained the attention of researchers in the communication field. Thus, mobile industries deal with remarkable technological competition regarding service quality. The quality is determined by how superior, consistent, and quick a service is delivered to the user. Thus, mobility management is a basic factor as it deals with imperative information for managing user’s mobility. However, due to the expansion of connected devices, the users are set up densely which inspires the researcher for devising a novel mode switching model. This paper devises a novel mode switching model using the Naive Bayes classifier. Here, the switching of modes is based on certain quality parameters, like link utilization, bandwidth, delay, energy consumption, and signal strength. Whenever the network switches the communication link from cellular-mode to user-mode, it must maintain the quality parameters. For enhancing the performance of network mobility management, a mobility management model is devised in which user mobility is computed. Thus, the proposed method is essential for supporting improved user mobility during communication The proposed mode switching using Naïve Bayes classifier provides superior performance with a minimal delay of 0.164 s, maximal power of 58.786 bpm, maximal link utilization ratio of 0.727 and maximal throughput of 1,641,723 respectively.
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Berrocal-Plaza, V., Vega-Rodríguez, M. A., & Sánchez-Pérez, J. M. (2015). Optimizing the mobility management task in networks of four world capital cities. Journal of Network and Computer Applications, 51, 18–28.
Liang, B., & Haas, Z. J. (1999) Predictive distance-based mobility management for PCS networks. In Proceedings of eighteenth annual joint conference of the IEEE computer and communications societies (Vol. 3, pp. 1377–1384).
Barua, S., & Braun, R. (2016). A novel approach of mobility management for the D2D communications in 5G mobile cellular network system. In 18th Asia-Pacific network operations and management symposium (APNOMS), Kanazawa.
Barua, S., & Braun, R. (2017). Mobility management of D2D communication for the 5G cellular network system: A study and result. In Proceedings of 17th international symposium on communications and information technologies (ISCIT), Cairns, QLD.
Berrocal-Plaza, V., Vega-Rodriguez, M. A., & Sanchez-Perez, J. M. (2014). Studying the reporting cells strategy in a realistic mobile environment. In Proceedings of the 6th world congress on nature and biologically inspired computing, (NABIC) (pp. 29–34). IEEE.
Dahi, Z. A., Mezioud, C., & Alba, E. (2016). A novel adaptive genetic algorithm for mobility management in cellular networks. In Proceedings of the 11th international conference on hybrid artificial intelligent systems, (HAIS) (pp. 225–237). Springer.
Hac, A., & Zhou, X. (1997). Locating strategies for personal communication networks, a novel tracking strategy. IEEE Journal on Selected Areas in Communications, 15(8), 1425–1436.
Dahi, Z. A., Alba, E., & Draa, A. (2018). A stop-and-start adaptive cellular genetic algorithm for mobility management of GSM-LTE cellular network users. Expert Systems with Applications, 106, 290–304.
Zhang, H., Chu, X., Guo, W., & Wang, S. (2015). Coexistence of wi-fi and heterogeneous small cell networks sharing unlicensed spectrum. IEEE Communications Magazine, 53(3), 158–164.
Ameigeiras, P., Ramos-munoz, J. J., Schumacher, L., Prados-Garzon, J., Navarro-Ortiz, J., & Lopez-soler, J. M. (2015). Link-level access cloud architecture design based on SDN for 5G networks. IEEE Network, 29(2), 24–31.
Boccardi, F., Heath, R. W., Lozano, A., Marzetta, T. L., & Popovski, P. (2014). Five disruptive technology directions for 5G. IEEE Communications Magazine, 52(2), 74–80.
Hoang, T. D., Le, L. B., & Le-Ngoc, T. (2016). Resource allocation for D2D communication underlaid cellular networks using graph-based approach. IEEE Transactions on Wireless Communications, 15(10), 7099–7113.
Biswash, S. K., & Jayakody, D. N. K. (2018). Performance based user-centric dynamic mode switching and mobility management scheme for 5G networks. Journal of Network and Computer Applications, 116, 24–34.
Park, J., Jung, S. Y., Kim, S. L., Bennis, M., & Debbah, M. (2016). User-centric mobility management in ultra-dense cellular networks under spatio-temporal dynamics. In Proceedings of IEEE global communications conference (GLOBECOM) (pp. 1–6). IEEE.
Shelke, P. M., & Prasad, R. S. (2019). DBFS: Dragonfly Bayes Fusion System to detect the tampered JPEG image for forensic analysis. Evolutionary Intelligence, 1–17.
Dahi, Z. A., Mezioud, C., & Alba, E. (2016). A novel adaptive genetic algorithm for mobility management in cellular networks. In Proceedings of the 11th international conference on hybrid artificial intelligent systems, (HAIS) (pp. 225–237).
Lin, Y. B. (1996). Mobility management for cellular telephony networks. IEEE Parallel & Distributed Technology: Systems & Applications, 4(4), 65–73.
Menaga, D., & Revathi, S. (2020). Deep learning: A recent computing platform for multimedia information retrieval. In book: Deep learning techniques and optimization strategies in big data analytics (pp. 124–141). https://doi.org/10.4018/978-1-7998-1192-3.ch008.
Alba, E., & Dorronsoro, B. (2005). The exploration/exploitation trade off in dynamic cellular genetic algorithms. IEEE Transactions on Evolutionary Computation, 9(2), 126–142.
Pan, Z., Saito, M., Liu, J., & Shimamoto, S. (2019). P-persistent energy-aware handover decisions employing RF fingerprint for adaptive-sized heterogeneous cellular networks. IEEE Access, 7, 52929–52944.
Biswash, S. K., Sarkar, M., & Sharma, D. K. (2018). Artificial immune system (AIS)-based location management scheme in mobile cellular networks. Iran Journal of Computer Science, 1(4), 227–236.
Mohamed, A., Imran, M. A., Xiao, P., & Tafazolli, R. (2018). Memory-full context-aware predictive mobility management in dual connectivity 5G networks. IEEE Access, 6, 9655–9666.
Ouali, K., Kassar, M., & Sethom, K. (2018). Handover performance analysis for managing D2D mobility in 5G cellular networks. IET Communications, 12(15), 1925–1936.
Calabuig, D., Barmpounakis, S., Gimenez, S., Kousaridas, A., Lakshmana, T. R., Lorca, J., et al. (2017). Resource and mobility management in the network layer of 5G cellular ultra-dense networks. IEEE Communications Magazine, 55(6), 162–169.
Wu, Q., Wu, C.-M., & Luo, W. (2018). Distributed mobility management with ID/locator split network-based for future 5G networks. Telecommunication Systems, 71, 459–474.
Hussain, S., Hamid, Z., & Khattak, N. S. (2006). Mobility management challenges and issues in 4G heterogeneous networks. In Proceedings of the first international conference on Integrated internet ad hoc and sensor networks (pp. 14). ACM.
Payaswini, P., & Manjaiah, D. H. (2014). Challenges and issues in 4G networks mobility management. arXiv preprint arXiv:1402.3985.
ElSawy, H., Hossain, E., & Alouini, M. S. (2014). Analytical modeling of mode selection and power control for underlay D2D communication in cellular networks. IEEE Transactions on Communications, 62(11), 4147–4161.
Yao, H., Fang, C., Guo, Y., & Zhao, C. (2016). An optimal routing algorithm in service customized 5G networks. Mobile Information Systems, 2016, 1–7.
Puntumapon, K., & Pattara-Atikom, W. (2008). Classification of cellular phone mobility using naive Bayes model. In Proceedings of VTC spring-IEEE vehicular technology conference (pp. 3021–3025).
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Sapkale, P.V., Kolekar, U.D. Modified Naïve Bayes Classifier for Mode Switching and Mobility Management Using Cellular Networks. Wireless Pers Commun 116, 2345–2366 (2021). https://doi.org/10.1007/s11277-020-07794-1
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DOI: https://doi.org/10.1007/s11277-020-07794-1