Spatio-temporal Mamba for User Mobility Prediction in Mobile Edge Computing
DOI:
https://doi.org/10.13052/jwe1540-9589.2536Keywords:
Mobile edge computing (MEC), mobility prediction, Mamba, GRU, quality of service (QoS), sequence modelingAbstract
In mobile edge computing (MEC), frequent server handovers due to user mobility increase latency and degrade quality of service (QoS). This study enhances MEC service stability by predicting user mobility for efficient server transitions. The proposed spacio-temporal (ST)-Mamba model combines Mamba (state-space encoder) and a gated recurrent unit (GRU) in parallel to capture both long-term and short-term dependencies, while Fourier feature embedding enriches spatial-temporal representation. Experiments show that ST-Mamba achieves about 9–10% lower root mean square error (RMSE) and mean absolute error (MAE) than long short-term memory (LSTM), GRU, and Transformer baselines, with statistically significant improvements confirmed by Welch’s t-test. These results demonstrate that hybrid state space model (SSM)–RNN architectures are promising for mobility-aware QoS optimization in MEC, with future work extending to real-world and multi-user settings.
Downloads
References
Elbamby, M. S., C. Perfecto, M. Bennis, and K. Doppler. 2018. Toward low-latency and ultra-reliable virtual reality. IEEE Network. 32(2): 78–84.
Zhou, P. Y., Y. Zhang, X. Wang, et al. 2024. 5G MEC computation handoff for mobile augmented reality. In Proceedings of the IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom). IEEE. pp. 1–6.
Ranaweera, P., A. Jurcut, and M. Liyanage. 2021. MEC-enabled 5G use cases: A survey on security vulnerabilities and countermeasures. ACM Computing Surveys (CSUR). 54(9): 1–37.
Bozkaya, E. 2023. Digital twin-assisted and mobility-aware service migration in mobile edge computing. Computer Networks. 231: 109798.
Alsahfi, T., et al. 2025. Optimizing healthcare big data performance through regional computing. Scientific Reports. 15(1): 3129.
Tran, T. P., and M. Yoo. 2024. Mobility-aware Service Migration in MEC System. In Proceedings of the 2024 International Conference on Information Networking (ICOIN). IEEE. pp. 653–656.
Yang, R., H. He, and W. Zhang. 2021. Multitier service migration framework based on mobility prediction in mobile edge computing. Wireless Communications and Mobile Computing. 2021(1): 6638730.
Jahandar, S., et al. 2025. Handover decision with multi-access edge computing in 6G networks: A survey. Results in Engineering. 103934.
Mehrabi, M., H. Salah, and F. H. P. Fitzek. 2019. A survey on mobility management for MEC-enabled systems. In Proceedings of the IEEE 2nd 5G World Forum (5GWF). IEEE. pp. 259–263.
Singha, N. R., N. Sarma, and D. K. Saikia. 2025. TMLpSA-MEC: Transformer-based Mobility Aware Periodic Service Assignment in Mobile edge computing. Computer Networks. 111329.
Loutfi, S. I., et al. 2024. An overview of mobility awareness with mobile edge computing over 6G network: Challenges and future research directions. Results in Engineering. 23: 102601.
Xu, G., et al. 2016. A survey for mobility big data analytics for geolocation prediction. IEEE Wireless Communications. 24(1): 111–119.
Bhadani, A. K., and D. Jothimani. 2016. Big data: challenges, opportunities, and realities. In Effective Big Data Management and Opportunities for Implementation. pp. 1–24.
Fattore, U., et al. 2020. AutoMEC: LSTM-based user mobility prediction for service management in distributed MEC resources. In Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM.
Kulkarni, V., et al. 2019. Examining the limits of predictability of human mobility. Entropy. 21(4): 432.
Lu, X., et al. 2013. Approaching the limit of predictability in human mobility. Scientific Reports. 3(1): 2923.
Qiao, Y., et al. 2018. A hybrid Markov-based model for human mobility prediction. Neurocomputing. 278: 99–109.
Ma, Z., and P. Zhang. 2022. Individual mobility prediction review: Data, problem, method and application. Multimodal Transportation. 1(1): 100002.
Brik, B., and A. Ksentini. 2021. Toward optimal MEC resource dimensioning for a vehicle collision avoidance system: A deep learning approach. IEEE Network. 35(3): 74–80.
Zeng, Q., et al. 2025. Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction. Sensors. 25(10): 3025.
Marmat, A., and D. Thankachan. 2025. ML-driven latency optimization for mobile edge computing in fiber-wireless access networks. MethodsX. 103594.
Zhang, X., et al. 2025. Pretraining-improved Spatiotemporal graph network for the generalization performance enhancement of traffic forecasting. Scientific Reports. 15(1): 27668.
The 5th International Workshop on Big data driven Edge Cloud Services (BECS 2025) Co-located with the 25th International Conference on Web Engineering (ICWE 2025), June 30–July 3, 2025, Delft, Netherlands
Liu, F., et al. 2025. Enhanced Mamba model with multi-head attention mechanism and learnable scaling parameters for remaining useful life prediction. Scientific Reports. 15(1): 7178.
Singh, R., R. Sukapuram, and S. Chakraborty. 2023. A survey of mobility-aware multi-access edge computing: Challenges, use cases and future directions. Ad Hoc Networks. 140: 103044.
Qin, Y., et al. 2025. Task offloading optimization in mobile edge computing based on a deep reinforcement learning algorithm using density clustering and ensemble learning. Scientific Reports. 15(1): 211.
Bernad, C., et al. 2025. Towards Smart 6G: Mobility Prediction for Dynamic Edge Services Migration. Expert Systems with Applications. 129348.
Tran, T. P., and M. Yoo. 2024. Mobility-aware Service Migration in MEC System. In Proceedings of the 2024 International Conference on Information Networking (ICOIN). IEEE. pp. 653–656.
Xiao, Jue, Tingting Deng, and Shuochen Bi. 2024. “Comparative analysis of LSTM, GRU, and transformer models for stock price prediction.” proceedings of the international conference on digital economy, blockchain and artificial intelligence.
Gunkel, Jonas, Andrea Tundis, and Max Mühlhäuser. 2024 “The Story of Mobility: Combining State Space Models and Transformers for Multi-Step Trajectory Prediction.” Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge.
Qin, Zhenlin, et al. 2025 “TransGTE: a transformer-based model with geographical trajectory embedding for the individual trip destination prediction.” Expert Systems with Applications: 128159.
Liu, Fugang, et al. 2025. “Enhanced Mamba model with multi-head attention mechanism and learnable scaling parameters for remaining useful life prediction.” Scientific Reports 15.1: 7178.
Patro, Badri Narayana, and Vijay Srinivas Agneeswaran. 2025. “Mamba-360: Survey of state space models as transformer alternative for long sequence modelling: Methods, applications, and challenges.” Engineering Applications of Artificial Intelligence 159: 111279.
