Aug 2026· Bulletin of Electrical Engineering and Informatics· 0 citations· 24 references
TL;DR
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.
Abstract
The vehicle-to-everything (V2X) is a significant technology that improves road safety, travel experience and entertainment services. Resource allocation (RA) in vehicular networks defines the strategic distribution of communication resources like power, time slots and bandwidth among vehicles and infrastructures to provide effective data transmission. However, RA faces challenges in managing limited transmission resources because of network delays and high reception time by frequent changes in network topology and varying user demands through high mobility of vehicles. Therefore, this research proposes a proximal policy optimization with self adaptive penalty function (PPO-SAPF) based RA for vehicular communications. The PPO-SAPF optimizes the RA in dynamic vehicular networks by adjusting the coefficient matrices, which ensures better adaptability to network topology and user demands. The SAPF provides fine-tuning of policy updates, maintaining a better balance between exploration and exploitation, thereby enhancing performance under different network conditions. The PPO-SAPF achieves a less inter-packet reception time of 119 ms for 16 vehicle-to-vehicle (V2V) links in case 3 compared to context-aware RA (CARA). These 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.
Federated learning (FL) has emerged as a promising paradigm for enabling distributed model training in vehicular networks while keeping raw data local. However, the dynamic mobility of vehicles and the limited spectrum resources create critical challenges for efficient FL execution. In particular, due to the high mobility of vehicles, the candidate set of participating vehicles keeps changing, which makes fixed-threshold selection strategies difficult to be applied effectively. Moreover, vehicle mobility also causes time-varying channel status, and if resource allocation is performed only once at the beginning of each training round, it might not match the varying channels, resulting in imprecise resource allocation and accordingly low resource utilization. To address these issues, we propose a dynamic vehicular FL framework where the long time duration is discretized into fine-grained time slots. A long time sequence two-timescale optimization problem is then formulated to jointly conduct vehicle selection and slot-level communication bandwidth allocation. To solve it, we design a hierarchical Markov Decision Process (H-MDP) framework, and then develop a hierarchical Proximal Policy Optimization-based vehicle selection and bandwidth allocation (HPPO-VSBAFL) strategy, consisting of two cooperative agents: a vehicle selection agent (VSA) for round-level participant selection, and a bandwidth allocation agent (BAA) for slot-level spectrum allocation. Extensive experimental results based on CIFAR-10 with ResNet-18 demonstrate that the proposed HPPO-VSBAFL framework significantly improves FL accuracy compared to baseline schemes, and can effectively adapt to the highly dynamic vehicular environments.
Zichao Zhao, Haixia Zhang, Wenjie Liu et al.· IEEE Transactions on Cogniti...· 0 citations
It is vitally important for intelligent transportation systems (ITS) to make use of vehicular ad hoc networks (VANETs) to improve road safety, traffic management, and communication. The mobility of vehicles and dynamic traffic conditions continue to pose challenges to network congestion. In this paper, we suggest a load‐balancing strategy for reducing network congestion through the optimization of control packet overhead and the enhancement of data dissemination between roadside units (RSUs) and vehicles. As part of the proposed methodology, real‐time traffic conditions, predictive modeling, and intelligent routing algorithms are incorporated to achieve an efficient load distribution among vehicular nodes. Urban VANETs can use the framework to measure performance metrics like packet delivery ratios (PDRs) and energy consumption (EC). Simulations indicate the proposed approach improves overall performance over existing load‐balancing approaches by reducing processing delays, minimizing network congestion, and minimizing resource utilization. Network operations need to optimize control packet overhead to achieve a balance between communication efficiency and network stability.
Kusum Yadav· Internet Technology Letters· 0 citations
Vehicular networks support intelligent transportation through vehicle-to-roadside Units (V2R) and vehicle-to-vehicle (V2V) communication but face challenges from dynamic topologies, limited RSU coverage, and bandwidth scarcity, which impact service delivery and revenue. RDA-ITU addresses these challenges by integrating V2R and V2V paradigms to maximize RSU revenue, enhance service availability, and improve system efficiency. It dynamically allocates services based on real-time network conditions and vehicle mobility, leveraging V2V relays to optimize both RSU-direct and cooperative communication. Through extensive simulations, RDA-ITU significantly outperforms four baselines: RBSM, VVMM-U, VVMM-LW, and VVMM-MA. It achieves 81.1% higher total revenue, 154.8% more completed requests, and 103.6% higher average data delivery. Specifically, versus RBSM, gains reach 77.6% in revenue, 228.0% in TCR, and 242.4% in TDD; against VVMM-U: 32.6%, 43.9%, and 47.9%; versus VVMM-LW: 153.7%, 74.5%, and 284.1%; and versus VVMM-MA: 25.7%, 30.2%, and 53.7%, respectively. These improvements stem from RDA-ITU’s core mechanisms: revenue-optimized candidate sorting, dynamic V2V relay pairing, and adaptive bandwidth allocation. Prioritizing high-revenue services and facilitating efficient cooperative offloading, RDA-ITU ensures strong performance in dense mobile environments, thus promoting revenue-aware vehicular edge computing.
A. Saluja, Satyabrata Das, S. K. Nayak et al.· Turkish Journal of Electrica...· 0 citations
With the rapid growth of Vehicular Edge Computing (VEC) and Mobile Edge Computing, efficient task offloading is essential for enhancing the computing and communication capabilities in vehicular networks. However, many existing methods suffer from slow convergence, load imbalance, and instability in dynamic, latency-sensitive environments. To address these challenges, we propose MAPPO-Lyapunov (MAPPO-L), a multi-agent offloading framework that integrates Multi-Agent Proximal Policy Optimization (MAPPO) with Lyapunov optimization. MAPPO-L enables distributed coordination among vehicles, roadside units (RSUs), and cloud servers, minimizing delay, improving resource utilization, and ensuring long-term stability. Lyapunov theory transforms long-term stability into per-slot optimizations, while MAPPO ensures efficient policy learning. An adaptive exploration mechanism dynamically adjusts exploration rates based on network dynamics, accelerating convergence and stabilizing training. Extensive simulations with real-world data show that MAPPO-L maintains task completion rates above 80%, converges 25%–37.5% faster than baselines, and reduces training fluctuations to 2.3%. Ablation studies confirm the critical roles of location, channel, and queue information, validating the robustness of MAPPO-L in practical VEC environments.
Lu Wei, Yong Yu, Jie Cui et al.· IEEE Transactions on Network...· 0 citations
The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.
Yongbin Wang, Peng Lin, Yan Liu et al.· IEEE Wireless Communications...· 0 citations
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· Electronics· 0 citations