Forecasting-Driven Stable Successor Matching for UAV-Assisted Continuous Edge Services

2026-05-03

AI Summary

This work addresses the challenge of service disruption for delay-sensitive tasks in UAV-assisted edge networks, where dynamic UAV mobility, energy constraints, and time-varying channels often lead to unexpected interruptions. To mitigate this, the authors propose Fresco, a novel framework that introduces an LSTM-based short-term interruption risk prediction mechanism and an online risk-aware backup UAV matching algorithm. Fresco proactively reserves minimal communication and computational resources while synchronizing lightweight task context to ensure seamless service continuity. Experimental results demonstrate that Fresco significantly reduces service interruption rates and enhances task continuity compared to existing reactive or non-predictive approaches, all while incurring only marginal resource overhead.

Abstract

Continuous and reliable service support is crucial for emerging latency-sensitive and computation-intensive applications in UAV-assisted edge networks (UENs) due to operational dynamics and environmental uncertainty. Although conventional designs can improve coverage and computing efficiency, they often rely on instantaneous resource optimization or reactive handover, rendering ongoing services vulnerable to non-negligible interruptions when the serving UAV degrades due to mobility, energy depletion, or channel dynamics. To avoid such post-failure recovery, a promising approach is to prepare a successor UAV in advance, i.e., a standby UAV that reserves minimal resources and synchronizes service context for possible takeover. Thus, we consider a dynamic UEN architecture where each mobile user carries an ongoing computing mission requiring persistent service support, while UAVs provide wireless access and computing services under time-varying network dynamics and stringent onboard energy constraints. To facilitate proactive and continuous service provisioning, we propose a forecasting-driven proactive reservation-based continuous service scheduling framework, termed Fresco. In Fresco, an LSTM-based module is first used to predict short-term disruption risks of ongoing missions from historical network observations. Guided by these predictions, an online risk-aware successor matching scheme selects suitable standby UAVs for high-risk missions under delay, resource, and energy constraints, while incorporating minimal communication/computation reservation and lightweight service-context synchronization for efficient takeover preparation. Experiments show that Fresco significantly reduces service interruptions and improves mission continuity over reactive and non-predictive baselines, with only modest reservation overhead.

Problem

• UAV-assisted edge networks
• service continuity
• proactive handover
• successor matching
• service interruption

Innovation

• forecasting-driven
• successor matching
• proactive reservation
• UAV-assisted edge computing
• service continuity