Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 42 references
Medicine
TL;DR
Results show that coordinated MEC-side control of AI model selection and computation, under a shared deadline-aware objective, provides a robust latency–energy trade-off for MEC-assisted vehicular networks.
Abstract
Multi-access edge computing (MEC) enables computation-intensive perception and decision-making tasks in vehicular networks to be offloaded to nearby edge servers. Existing approaches usually fix the artificial intelligence (AI) inference model, overlooking how model selection jointly affects latency, energy consumption, and service reliability. We propose a hierarchical model selection and control (HMSC) framework based on deep reinforcement learning (DRL) for MEC-assisted vehicular networks. The framework couples a vehicle-layer MAPPO component that provides a communication interface representation for subchannel assignment and energy accounting with a centralized MEC-layer soft actor-critic (SAC) agent that, under SDN orchestration, adaptively selects lightweight or high-fidelity AI models and allocates computational resources. Accordingly, the core contribution of this paper lies in MEC-side model-aware computation control under an explicitly defined subchannel-contention abstraction, rather than in physical-layer transmit-power optimization. Both layers are guided by a composite objective that integrates normalized end-to-end (E2E) latency, normalized energy consumption, and a deadline-violation penalty. Using a discrete-time simulation framework, HMSC reduces E2E latency compared with static inference and non-hierarchical DRL baselines and sustains a higher deadline satisfaction ratio (DSR) under constrained uplink throughput and varying traffic loads. The learned policy is load-aware, favoring high-fidelity inference under light load and lightweight inference under congestion; a post hoc analysis using YOLOv5-family accuracy reference further quantifies the inference-quality implications of this adaptive selection behavior. These results show that coordinated MEC-side control of AI model selection and computation, under a shared deadline-aware objective, provides a robust latency–energy trade-off for MEC-assisted vehicular networks.
: Peak age of information (PAoI) and energy consumption (EC) are conflicting yet critical metrics in mobile edge computing (MEC)-assisted vehicular networks. Most existing studies overlook the joint effects of sensing, transmission, and computation. The main contributions of this work are threefold. First, we derive novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain. Second, we demonstrate that the partial computation offloading strategy is superior, effectively balancing the low latency of local processing with the high power of edge computing. Third, we formulate a weighted optimization problem to navigate the PAoI-EC tradeoff and identify an optimal offloading ratio that dynamically adapts to specific freshness and efficiency requirements. Numerical results demonstrate that jointly optimizing the offloading ratio, edge computing capability, and transmission power significantly improves performance. Our findings offer practical guidelines for designing timely and energy-efficient vehicular telematics systems.
Hui Zhang, Mangang Xie, Baozhen An et al.· Computers, Materials & C...· 0 citations
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 heterogeneous vehicle-MEC interactions, spatiotemporal traffic dynamics, and continuously varying vehicle populations. To address these challenges, this paper considers a traffic-aware embodied edge intelligence-enabled vehicular network (EEIVN), where edge intelligence is grounded in the physical traffic environment by integrating VLM-based semantic perception with edge decision making. Based on this architecture, we formulate a reliability-constrained delay minimization problem (RDMP) by jointly optimizing task offloading ratio, computing resource allocation, and vehicle association, while constraining the queue reliability to mitigate queue-induced tail delay. To solve the NP-hard RDMP, we propose a VLM-multi-agent proximal policy optimization (VLM-MAPPO) approach that integrates a VLM-based traffic awareness method, a vehicle-adaptive MAPPO algorithm, and a vehicle association scoring and selection mechanism. Extensive simulations based on SUMO and CARLA demonstrate that the proposed VLM-MAPPO approach outperforms benchmarks in terms of task completion delay and tail delay, while maintaining comparable vehicle energy consumption and exhibiting robust scalability under dynamic traffic conditions and varying vehicle densities.
Xulong Qiao, Jian Wang, Zemin Sun et al.· IEEE Transactions on Cogniti...· 0 citations
6G vehicular services, including cooperative perception, augmented reality navigation, and high-definition map updating, need computation support close to moving vehicles. Vehicular Edge Computing (VEC) is a natural solution, but the offloading decision becomes difficult when wireless channel conditions, vehicle density, and edge server loads vary simultaneously. In this paper, we study joint task offloading and resource allocation in 6G VEC with high- and low-frequency cooperation (HL-FC). We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP). Each vehicle decides its offloading ratio, transmission power, server association, and edge CPU request from local observations. To evaluate the proposed policy, we build a lightweight equation-driven Python simulator and compare MAPPO with Local-only, Edge-only, Random, and Greedy policies. Compared with Edge-only, MAPPO reduces the average system cost by 32.15%, 23.51%, and 17.13% under 10, 15, and 20 vehicles, respectively. It also improves the task completion rate by 21.00, 20.49, and 17.65 percentage points. Additional blockage experiments show that HL-FC keeps the policy more robust than high-frequency-only transmission under severe high-frequency blockage. The results reveal that MAPPO delivers better performance when edge resources become congested than in lightly loaded scenarios.
Zi-Heng Gu· 2026 8th International Confe...· 0 citations
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, Jiang-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
Results across heterogeneous urban scenarios show that the proposed framework improves vehicular communication performance in terms of packet delivery reliability and communication delay, while preserving competitive machine learning performance under deployment-oriented constraints.
Juan Pablo Astudillo León, Leticia Lemus Cárdenas, Luis J. de la Cruz Llopis et al.· IEEE Access· 0 citations
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations
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