Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 13863-13879· 0 citations· 47 references
Abstract
As Satellite Edge Computing (SEC) emerges as a computing backbone of 6 G IoT, its feasibility is threatened by frequent handovers that disrupt task continuity and cause deadline violations in AI-driven workloads. Conventional communication-centric handover strategies overlook the urgency of computational tasks, further exacerbating these issues. To address this gap, we propose the <bold>C</bold>omputation <bold>A</bold>ware <bold>C</bold>onditional <bold>H</bold>andover <bold>O</bold>ptimization (CACHO) framework, a non-intrusive solution fully compatible with 3GPP Conditional Handover (CHO). The framework integrates model partition driven delayed execution to enable seamless migration via inter-satellite tensor transmission, timeout-risk predictive proactive triggering to protect at-risk tasks from overloaded satellites, and task-load-aware target selection to balance computational workloads and service popularity. These modules are orchestrated within standardized CHO workflows without altering the core logic, ensuring deployability in existing satellite network systems. Simulations based on real Starlink constellation data demonstrate that our approach reduces task timeout rates by 29.95–51.02<inline-formula><tex-math notation="LaTeX">$\%$</tex-math><alternatives><mml:math><mml:mo>%</mml:mo></mml:math><inline-graphic xlink:href="xu-ieq1-3674471.gif"/></alternatives></inline-formula> compared to the 3GPP standard CHO and several state-of-the-art baselines, while maintaining handover success rates and minimizing extra handovers needed. Beyond performance gains, this work provides the first blueprint for embedding task life-cycle awareness into handover workflows, bridging mobility management and computation guarantees, and advancing the feasibility of robust SEC for mission-critical 6G IoT applications.
Vehicular edge computing (VEC), a key enabler for the Internet of Things (IoT) in intelligent transportation, addresses onboard processing constraints through collaborative task offloading among vehicles, facilitating latency-sensitive applications such as autonomous driving. However, developing efficient offloading strategies remains particularly challenging in high-density vehicular networks, where intensive computational demands coexist with severely constrained intervehicle communication ranges due to signal blockage. To handle this, we propose M4O, a mobility-aware task offloading framework supporting multihop, multiuser, and multitask offloading optimization. M4O intelligently integrates vehicle mobility patterns and enables relay-assisted offloading to enhance system effectiveness and robustness. The framework employs a dual-algorithm approach: the advantage actor–critic (A2C) for indivisible tasks and the hybrid proximal policy optimization (H-PPO) for divisible tasks, both optimized to minimize the temporally coupled composite cost of time and resources. Extensive experiments demonstrate that the deep reinforcement learning (DRL)-based solutions of M4O deliver stable and efficient offloading strategies, outperforming existing benchmarks by significant margins in cost efficiency. Our code is available at https://github.com/Zhouym1028/M4O
Momiao Zhou, Yimin Zhou, Yanshi Sun et al.· IEEE Internet of Things Jour...· 0 citations
A hierarchical edge-fog anomaly detection framework that integrates lightweight edge-level filtering with a fog-level Temporal Convolutional Network (TCN) detector, which suggests that hierarchical edge intelligence is a practical but calibration-sensitive direction for scalable anomaly monitoring in EV charging infrastructures.
H. Jeong· Computers, Materials & C...· 0 citations
An online Deep Reinforcement Learning (DRL) based adaptive partition method to dynamically determine optimal partitioning decision so as to jointly accelerate DNN inference and mitigate energy consumption is developed.
Shubin Zhang, Junrong Ma, Kaikai Chi et al.· ACM transactions on sensor n...· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
A novel Multi-Armed Bandit (MAB) approach is applied to optimize dynamic bandwidth allocation at the ONU layer, enabling increased user density without inducing latency burdens at the OLT and establishes a fault-resilient infrastructure suitable for next-generation converged optical networks.
K.Tara Phani, K. Kumari· Journal of optical communica...· 0 citations
The emergence of AI-native 6G networks necessitates efficient and proactive resource management mechanisms to support highly dynamic and data-intensive services. Existing approaches typically employ independent prediction models for mobility, handover, and channel quality, leading to redundant data processing and limited exploitation of cross-layer dependencies. In this paper, we propose a shared data intelligence–driven multi-task prediction framework that jointly models mobility, handover, and channel quality indicator (CQI) within a unified learning architecture. By leveraging a common feature space and a single processing pipeline, the proposed framework simultaneously generates multiple correlated predictions, thereby reducing computational overhead. A correlation analysis using real-world datasets demonstrates that mobility, CQI, and handover events exhibit inherent inter-dependencies, justifying the use of a shared representation. Furthermore, a processing time comparison shows that the proposed approach achieves approximately 10.12% reduction compared to conventional independent prediction models by eliminating redundant feature extraction and repeated model execution. These results validate that shared data intelligence is an effective and scalable solution for efficient multi-task prediction in real-time 6G network environments.
K. Hasan, Seong-Ho Jeong· International Conference on...· 0 citations
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