Open Access

ARTICLE

Proactive Mobility-Aware Fog Service Continuity Using Digital Twins and GRU–EWMA-Based Association Forecasting

Navjeet Kaur¹, Ayush Mittal², Saad Alahmari^3,*

1 Apex Institute of Technology (CSE), Chandigarh University, Mohali, Punjab, India
2 Strategic Technology Group (STG), Infosys Ltd., Chandigarh, India
3 Department of Computer Science, Applied College, Northern Border University, Arar, Saudi Arabia

* Corresponding Author: Saad Alahmari. Email:

(This article belongs to the Special Issue: Integrating Computing Technology of Cloud-Fog-Edge Environments and its Application)

Computers, Materials & Continua 2026, 88(1), 65 https://doi.org/10.32604/cmc.2026.079991

Received 01 February 2026; Accepted 24 March 2026; Issue published 08 May 2026

Abstract

Mobile fog computing must support latency-sensitive applications under dynamic user mobility and time-varying network conditions. Existing mobility-aware scheduling approaches are largely reactive and often ignore prediction uncertainty, resulting in service disruptions and inefficient task migration. This paper proposes an uncertainty-aware digital twin-based orchestration framework for proactive mobility-aware fog computing. The framework maintains real-time synchronized digital twins of users and fog nodes and integrates a hybrid Gated Recurrent Unit-Exponentially Weighted Moving Average (GRU-EWMA) mobility prediction model with fog-load forecasting to enable joint mobility- and load-aware decision-making. An entropy-based confidence mechanism is introduced to regulate proactive handover and task migration, thereby reducing unnecessary task migrations when predictions are uncertain. The proposed framework is implemented in the MobFogSim simulator and evaluated against state-of-the-art baselines. Experimental results demonstrate that the proposed approach reduces the average task delay by up to 28.1%, decreases energy consumption by up to 9.5%, and improves the task success rate to 99.1%, while incurring only a modest digital-twin computational overhead. These results confirm that integrating uncertainty-aware mobility prediction with digital twin–driven orchestration significantly enhances reliability and efficiency in mobile fog computing environments.

---

Keywords

---