Task Offloading and Resource Scheduling in a Vehicle-RSU-Cloud Resource Environment

A 5G mmWave-based task offloading approach for vehicular edge computing

Vehicle Edge Computing (VEC) is a computing paradigm specifically designed to support the execution of computationally intensive vehicle applications while ensuring low latency, efficient bandwidth utilization, and reduced energy consumption. In modern vehicular environments, vehicles are required to process large amounts of data generated by diverse applications, and local onboard resources are often insufficient to meet these demands. Therefore, vehicles must efficiently offload computational tasks to external computing systems that offer higher processing capabilities in order to maintain optimal computational performance, especially under dynamic and changing network conditions.This paper investigates a VEC scenario in which vehicles outsource their computational tasks with the objective of optimizing overall computation time and increasing the successful task transfer rate. In such scenarios, several factors affect computational efficiency, including heterogeneous task requirements and the inherent mobility of vehicles. Variations in task size, delay sensitivity, and processing demands, combined with frequent changes in vehicle location, can significantly affect the performance of task offloading mechanisms and resource utilization.To address these challenges, the study incorporates and utilizes a fifth-generation (5G) radio network to effectively manage task offloading decisions and computational resources. A 5G-based task offloading scheme is proposed, aiming to manage computational resources in a mobility-aware manner. By considering vehicle mobility, the proposed scheme adapts task export decisions to changing network conditions, thereby improving system robustness and efficiency. The scheme is further supported by a distributed communication model, which enables near-optimal solutions by coordinating task offloading and resource allocation across the network.Furthermore, the proposed approach integrates a fifth-generation new radio (NR) communication model to enhance system performance. This model includes both cellular connectivity and millimeter wave (mmWave) communication, allowing the system to benefit from the strengths of each communication mode. Simulation results demonstrate that the proposed model significantly improves computational efficiency, particularly in terms of successful task handover and Quality of Experience (QoE). QoE represents the overall service performance as perceived by users and is influenced by key factors such as latency, throughput, and error rates. Overall, the proposed approach achieves improved task offloading efficiency, reduces task failure rates, and enhances QoE through the effective integration of mmWave and 5G NR communication models.

Joint Heterogeneous Communication, Load-Balanced Offloading, and Cloud-Edge-Edge Collaborative Construction for Centralized Control Systems

The intelligent substation centralized control system faces three intertwined challenges: mixing event-driven and periodic traffic with different QoS needs, sharing limited inspection hosts among multiple substations, and placing cloud-edge-edge nodes to balance delay and load. This paper tackles them together. We build a distributed heterogeneous wireless model and design a joint uplink-downlink resource allocation that protects urgent traffic. We then propose a latency-utility offloading scheme that levels the computational load across shared hosts. Finally, we develop a cloud-edge-edge construction method with coverage optimization that accounts for both communication distance and load distribution. Simulations show that our framework not only meets delay and reliability targets but also improves channel utilization by 26%, cuts average interference by 3.2 dB, reduces per-task energy by 27%, and maintains a task completion rate above 99.5% under heavy load.

Joint Task Offloading and Resource Allocation with Data Caching in UAV-Aided Mobile Edge Computing Networks for Latency-Sensitive Applications

Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.

PPO-Based Task Offloading for Deadline-Aware IoV Services in a Real-Data-Driven Environment

Task offloading is a key enabler for delay-sensitive Internet of Vehicles (IoV) services, where vehicular applications must be executed under strict latency constraints. This paper proposes a Proximal Policy Optimization (PPO)-based binary offloading framework that selects between Multi-access Edge Computing (MEC) and Cloud execution. Unlike purely simulation-based approaches, the proposed framework is built on a real-data-driven environment derived from vehicular mobility traces and measured service-delay observations collected from the Modena Automotive Smart Area (MASA) testbed. The RL agent observes mobility and delay-related features and learns a deadline-aware offloading policy through reward-driven interaction with the environment. Experimental results under a 50 ms deadline show that PPO achieves the best overall trade-off between task acceptance and delay control, while providing a more stable service behavior than fixed baselines.

Mobility-aware task scheduling in vehicular fog computing networks: a combined DTMC and MMSP approach

Impacts of Heterogeneous Traffic Environments on Vehicle-Infrastructure Collaborative Computing Latency: A Multi-Layer Agent-Based Simulation Approach

Achieving efficient task offloading with low latency in cooperative vehicle-infrastructure systems represents a critical challenge influenced by three key factors: heterogeneous vehicle computational capabilities, dynamic traffic conditions, and variable communication reliability. To address the fundamental trade-off between real-time processing performance and cost-effective infrastructure investment, this study develops a multi-layer agent-based modeling (ABM) framework that systematically evaluates how roadside unit (RSU) computation capacity, traffic volume, and vehicular computation capability (VCC) heterogeneity collectively impact collaborative computing latency. The ABM framework comprises three interconnected layers: an infrastructure layer that models RSUs with sensing, communication, and computing functions; a traffic layer that simulates diverse traffic environments; and a collaborative computing layer that manages decentralized task generation and offloading. A modified water-filling algorithm is integrated to dynamically allocate computational tasks based on available resources and latency constraints. Simulation results under signalized intersection scenarios reveal nonlinear latency growth with increasing traffic volume due to queuing effects. When the RSU computing capacity is insufficient, most tasks are offloaded to nearby vehicles. Our results show that when the RSU computational capacity is comparable to the total VCC of all vehicles, the RSU processes approximately 40% of the tasks, primarily due to vehicle–RSU mobility effects and stochastic wireless transmission failures. Furthermore, increasing VCC heterogeneity—while keeping the average constant—leads to both higher average latency and greater variability. These findings provide quantitative insights for optimizing computing resource deployment in real-world cooperative systems.