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Multiobjective Optimization-driven Task Scheduling in Vehicular Cloud Environments

Simulation results show that MOMUS outperforms state-of-the-art VEC scheduling approaches, particularly under high-demand scenarios, achieving higher task completion rates while reducing monetary cost and maintaining acceptable latency.

Joahannes Costa, A. M. Souza, D. Rosário et al. · 0 citations
2026

SkySched: A Hierarchical and Scalable Reinforcement Learning Framework for Multi-UAV Vehicular Edge Computing Network

Unmanned Aerial Vehicles (UAVs) are increasingly deployed as embodied aerial agents in low-altitude economies, forming mobile aerial edge networks that enable flexible computation offloading for vehicles. However, their limited endurance and frequent join/leave behaviours result in highly dynamic topologies, underminin...

Meng Yi, V. Lee, Miao Du et al. · 0 citations
2026

Traffic-Aware Embodied Edge Intelligence for Vehicular Networks: An Integrated VLM and MAPPO Approach

Vehicular edge computing (VEC) has emerged as a key paradigm to support computation-intensive and delay-sensitive vehicular applications by offloading tasks from vehicles to nearby multi-access edge computing (MEC) servers. However, in realistic urban environments, task processing performance is heavily affected by het...

Xulong Qiao, Jian Wang, Zemin Sun et al. · 0 citations
Open access Aug 2026

Drift-Plus-Penalty-Based Joint Optimization of Computational Resource Scheduling, Power Control, and UAV Flight Decisions in UAV-Enabled Mobile Edge Computing

A Lyapunov-based joint optimization framework for UAV-enabled MEC systems achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation.

Lei Li, Xue Gao, Quan-Sheng Guan · 0 citations
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Advanced Adaptive Scheduling for Autonomous Driving in Beyond-5G/6G Networks

SOVANET+ is presented, an extended scheduling technique that jointly accounts for service criticality, network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services, supporting its viability for next-generation intelligent transportation systems.

Athanasios Kanavos, Gerasimos Papanikolaou-Ntais, A. Kaloxylos · 0 citations
#edge computing Open access Aug 2026

Collaborative resource allocation in UAV-assisted MEC networks: A heterogeneous MAPPO scheme

This paper proposes a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices and UAVs act as heterogeneous agents and utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers.

Ming Cheng, Canlin Zhu, Jian-Hang Tang et al. · 0 citations

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