Whale Optimization Algorithm Aided Markov Chain for Mobility Prediction

Abstract

Trajectory prediction remains a significant operation in mobile communications. In 5G and Beyond (B5G) networks, next-cell prediction for User Equipment (UE) becomes increasingly critical amid the exponential network complexity driven by unprecedented subscriber growth. Markov Chains are selected for their simplicity, interpretability, low computational demands, and proven effectiveness in modeling sequential mobility patterns, which makes them ideal for real-time predictions, despite the existence of more complex alternatives. Paralleling the growing interest in metaheuristic (MH) algorithms for parameter optimization, this paper employs the Whale Optimization Algorithm (WOA) to select the optimal Markov Chain order for each UE trajectory, thereby enhancing next-location prediction. Compared to the traditional fixed-order Markov Chain, the proposed method boosts average prediction accuracy by 20%.

Keywords

• Handover
• Mobility graph
• Markov chain
• Whale optimization algorithm

References

1. 3GPP, “5G system; Network data analytics services; Stage 3,” 3GPP, Sophia Antipolis, France, Tech. Rep. TS 29.520 V19.4.0, Sep. 2025.
2. R. Montoliu and D. Gatica-Perez, “Discovering human places of interest from multimodal mobile phone data,” in Proc. 9th Int. Conf. Mobile Ubiquitous Multimedia (MUM), New York, NY, USA: ACM, 2010.
3. A. Ladekar, B. Mohol, A. Gaikwad, S. Shingade, A. Kulkarni, and Y. Naval, “PULM: Prediction of user’s location using machine learning with Markov model,” in Proc. 6th Int. Conf. Trends Electron. Inform. (ICOEI), 2022, pp. 1195–1199.
4. M. Karimzadeh, Z. Zhao, F. Gerber, and T. Braun, “Mobile users location prediction with complex behavior understanding,” in Proc. IEEE 43rd Conf. Local Comput. Netw. (LCN), 2018, pp. 323–326, doi: 10.1109/LCN.2018.8638045
5. S. Staroletov and R. Chudov, “An anomaly detection and network filtering system for Linux based on Kohonen maps and variable-order Markov chains,” in Proc. 32nd Conf. Open Innov. Assoc. (FRUCT), 2022, pp. 280–290, doi: 10.23919/FRUCT56874.2022.9953860
6. M. H. Nadimi-Shahraki, H. Zamani, Z. Asghari Varzaneh, and S. Mirjalili, “A systematic review of the whale optimization algorithm: Theoretical foundation, improvements, and hybridizations,” Arch. Comput. Methods Eng., vol. 30, pp. 4113–4159, Sep. 2023.
7. Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma, “Mining interesting locations and travel sequences from GPS trajectories,” in Proc. 18th Int. Conf. World Wide Web (WWW), New York, NY, USA: ACM, 2009, pp. 791–800, doi: 10.1145/1526709.1526816
8. Y. Zheng, Q. Li, Y. Chen, X. Xie, and W.-Y. Ma, “Understanding mobility based on GPS data,” in Proc. 10th Int. Conf. Ubiquitous Comput. (UbiComp), New York, NY, USA: ACM, 2008, pp. 312–321, doi: 10.1145/1409635.1409677

9. Y. Zheng, X. Xie, and W. Ma, “GeoLife: A collaborative social networking service among user, location and trajectory,” IEEE Data Eng. Bull., vol. 33, no. 2, pp. 32–39, 2010.
10. M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding individual human mobility patterns,” Nature, vol. 453, pp. 779–782, Jun. 2008, doi: 10.1038/nature06958
11. M.-I. Akodjenou-Jeannin, K. Salamatian, and P. Gallinari, “Flexible grid-based clustering,” in Knowledge discovery in databases: PKDD 2007, J. N. Kok, J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, and A. Skowron, Eds. Berlin, Germany: Springer, 2007, pp. 350–357, doi: 10.1007/978-3-540-74976-9_33
12. Y. Jia et al., “A novel user mobility prediction scheme based on the weighted Markov chain model,” in Proc. IEEE Int. Conf. Trust, Secur. Privacy Comput. Commun. (TrustCom), 2022, pp. 1078–1083, doi: 10.1109/TrustCom56396.2022.00148
13. Q. Liu, G. Chuai, J. Wang, and J. Pan, “Proactive mobility management with trajectory prediction based on virtual cells in ultra-dense networks,” IEEE Trans. Veh. Technol., vol. 69, no. 8, pp. 8832–8842, 2020.
14. J. Yang, J. Xu, M. Xu, N. Zheng, and Y. Chen, “Predicting next location using a variable order Markov model,” in Proc. 5th ACM SIGSPATIAL Int. Workshop GeoStreaming (IWGS), New York, NY, USA: ACM, 2014, pp. 37–42, doi: 10.1145/2676552.2676557
15. L. L. Vy, L.-P. Tung, and B.-S. P. Lin, “Big data and machine learning driven handover management and forecasting,” in Proc. IEEE Conf. Standards Commun. Netw. (CSCN), 2017, pp. 214–219, doi: 10.1109/CSCN.2017.8088624
16. P. A. Lopez et al., “Microscopic traffic simulation using SUMO,” in Proc. 21st Int. Conf. Intell. Transp. Syst. (ITSC), 2018, pp. 2575–2582, doi: 10.1109/ITSC.2018.8569938
17. J. Sun, Y. Zhang, and M. Trik, “PBPHS: A profile-based predictive handover strategy for 5G networks,” Cybern. Syst., vol. 55, no. 5, pp. 1041–1062, 2024, doi: 10.1080/01969722.2022.2129375
18. W. Long, T. Li, Z. Xiao, D. Wang, R. Zhang, A. C. Regan, H. Chen, and Y. Zhu, “Location prediction for individual vehicles via exploiting travel regularity and preference,” IEEE Trans. Veh. Technol., vol. 71, no. 5, pp. 4718–4732, 2022, doi: 10.1109/TVT.2022.3151762
19. S. Bouchelaghem, H. Boudjelaba, M. Omar, and M. Amad, “User mobility dataset for 5G networks based on GPS geolocation,” in Proc. IEEE 27th Int. Workshop Comput. Aided Model. Design Commun. Links Netw. (CAMAD), 2022, pp. 128–133, doi: 10.1109/CAMAD55695.2022.9966906
20. N. Rajule, M. Venkatesan, R. Menon, and A. Kulkarni, “Mobility prediction in cellular networks: A survey,” in Proc. Int. Conf. Recent Trends Electron. Commun. (ICRTEC), 2023, pp. 1–9.
21. M. Karimzadeh, Z. Zhao, F. Gerber, and T. Braun, “Pedestrians’ complex behavior understanding and prediction with a hybrid Markov chain,” in Proc. 14th Int. Conf. Wireless Mobile Comput. Netw. Commun. (WiMob), 2018, pp. 200–207.
22. W. Fu, X. Li, H. Ji, and H. Zhang, “Optimal access points selection based on mobility prediction in heterogeneous small cell networks,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC), 2018, pp. 421–425.
23. S. Gambs, M.-O. Killijian, and M. N. del Prado Cortez, “Next place prediction using mobility Markov chains,” in Proc. 1st Workshop Meas. Privacy Mobility (MPM), New York, NY, USA: ACM, 2012, doi: 10.1145/2181196.2181199
24. A. Ajinu and C. P. Maheswaran, “A novel prediction model for mobility tracing of users with hybrid metaheuristic concept,” Wireless Netw., vol. 28, pp. 107–123, Jan. 2022, doi: 10.1007/s11276-021-02806-9
25. C. Bhadane and K. Shah, “Context-aware next location prediction using data mining and metaheuristics,” Evol. Intell., vol. 14, pp. 871–880, Jun. 2021, doi: 10.1007/s12065-020-00469-7
26. D. Parambanchary and V. Malleswara Rao, “WOA-NN: A decision algorithm for vertical handover in heterogeneous networks,” Wireless Netw., vol. 26, pp. 165–180, Jan. 2020, doi: 10.1007/s11276-018-1787-z
27. C. H. Liu, Y. Wang, C. Piao, Z. Dai, Y. Yuan, G. Wang, and D. Wu, “Time-aware location prediction by convolutional area-of-interest modeling and memory-augmented attentive LSTM,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 5, pp. 2472–2484, 2022, doi: 10.1109/TKDE.2020.3005735
28. A. Jain, R. Duin, and J. Mao, “Statistical pattern recognition: A review,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 4–37, 2000, doi: 10.1109/34.824819
29. K. S. Fu, “Structural pattern recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-3, no. 3, p. 352, May 1981, doi: 10.1109/TPAMI.1981.4767111
30. M. R. Kumar, S. S. S. Kamisetty, S. Ayan, N. Reddy, Raghunanda, and Vediyappan, “A robust approach for road traffic prediction using Markov chain model,” in Proc. 7th Int. Conf. Image Inf. Process. (ICIIP), 2023, pp. 663–669, doi: 10.1109/ICIIP61524.2023.10537659
31. S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016, doi: 10.1016/j.advengsoft.2016.01.008
32. M. M. Abualhaj, S. Al-Khatib, A. Alalousi, M. O. Hiari, and M. Al Zyoud, “Phishing attack detection using whale optimization algorithm-based feature selection,” in Proc. 5th Int. Conf. Emerg. Smart Technol. Appl. (eSmarTA), 2025, pp. 1–6, doi: 10.1109/eSmarTA66764.2025.11132149
33. R. Zangmo, S. K. Sudabattula, S. Mishra, and N. Dharavat, “Optimal placement of distributed generators and electric vehicles using whale optimization algorithm (WOA),” in Proc. 1st Int. Conf. Innov. Sustain. Technol. Energy, Mechatronics, Smart Syst. (ISTEMS), 2024, pp. 1–6, doi: 10.1109/ISTEMS60181.2024.10560189
34. U. Pasupathi, A. G. Saminathan, A. Manikandan, M. Subhashini, C. Gowrishankar, and T. S. Poornappriya, “An efficient data collection using whale optimization algorithm (WOA) in wireless sensor network,” in Proc. Global Conf. Commun. Inf. Technol. (GCCIT), 2024, pp. 1–6, doi: 10.1109/GCCIT63234.2024.10862476
35. H. M. Mohammed, S. U. Umar, and T. A. Rashid, “A systematic and meta-analysis survey of whale optimization algorithm,” Comput. Intell. Neurosci., vol. 2019, Art. no. 8718571, 2019, doi: 10.1155/2019/8718571?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle
36. B. Rehman et al., “Joint user grouping and power control using whale optimization algorithm for NOMA uplink systems,” PeerJ Comput. Sci., vol. 8, Art. no. e882, 2022, https://dx.doi.org/10.7717/peerj-cs.882
37. 3GPP, “5G system; Architecture enhancements for 5G system (5GS) to support network data analytics services,” 3GPP, Sophia Antipolis, France, Tech. Rep. TS 23.288 V19.6.0, Dec. 2025.
38. I. Shayea, M. Ergen, A. Azizan, M. Ismail, and Y. I. Daradkeh, “Individualistic dynamic handover parameter self-optimization algorithm for 5G networks based on automatic weight function,” IEEE Access, vol. 8, pp. 214392–214412, 2020, doi: 10.1109/ACCESS.2020.3037048