A revenue-driven approach for enhanced task utilization in vehicular cloud computing

Proximal policy optimization with self adaptive penalty function for vehicular resource allocation

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.

QoS-Driven Resource Allocation and Performance Optimization for Aggregated VLC–RF Vehicular Networks

Visible light communication (VLC) is widely regarded as a key enabler for future vehicular networks, thanks to its extremely large unlicensed bandwidth and non-interference with existing radio frequency (RF) communication networks. With the goal of maximizing the benefits of both RF and VLC technologies, aggregated VLC–RF vehicular networks, in which any vehicle can be served by both RF and VLC access points (APs) concurrently, have recently become a more robust and promising approach for enhancing vehicle-to-everything (V2X) applications and improving the quality-of-service (QoS) of vehicular networks. This paper focuses on the joint spectrum reuse and power allocation problem in aggregated VLC–RF vehicular networks with delayed channel state information (CSI) feedback, where vehicle-to-vehicle (V2V) links opportunistically reuse the RF spectrum allocated to vehicle-to-infrastructure (V2I) links. Specifically, we focus on maximizing the total V2I achievable rate to support high-rate content delivery, and guaranteeing the required reliability of V2V links tasked with exchanging safety-critical information. Furthermore, the sum V2I achievable rate maximization problem is decomposed into four subproblems, which are iteratively solved through an efficient block coordinate descent (BCD)-based alternating optimization algorithm. Moreover, simulation results validate the convergence and efficiency of the proposed algorithm while highlighting the impact of critical parameters on system performance, providing valuable insights for resource allocation in aggregated VLC–RF vehicular networks.

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

This paper proposes a hierarchical computation framework that flexibly supports task execution across local vehicles, neighboring vehicles, RSUs, and cloud resources, and designs an efficient task migration and resource scheduling strategy that improves overall system performance under dynamic network conditions.

MoReSP: A Multiobjective Mobility- and Reliability-Aware Scheduling Model for RSU-Assisted Vehicular IoT Networks

Vehicular Internet of Things (V-IoT) networks require reliable scheduling for safety-critical communication, cooperative awareness, and cooperative perception under dynamic mobility and limited roadside infrastructure. This paper proposes MoReSP, a Mobility- and Reliability-aware Scheduling Policy for roadside unit (RSU)-assisted V-IoT networks. MoReSP uses mobility-regime inference, structured action scoring, safety projection, and episodic parameter adaptation to select among deny, grant, preempt, coexist, and handoff actions. Its multiobjective formulation jointly minimizes average delay, communication energy consumption, admission-adjusted reliability loss, a penalty for cooperative perception message (CPM) delivery/freshness, and RSU-load imbalance. The framework is evaluated under vehicle-load variation, Nagel–Schreckenberg (NaSch) density variation, and RSU-capacity scaling using admission-adjusted metrics that penalize excessive blocking and interruption. MoReSP is compared with five literature-grounded benchmark families: Age of Correlated Information (AoCI)-Heuristic, RSU-Coop, Handoff-Aware, vehicle-to-everything (V2X)-Priority, and Adaptive Learning-based Task Offloading multi-armed bandit (ALTO-MAB). Simulation results show that MoReSP achieves the lowest admission-adjusted system cost across all evaluated scenarios. At nominal RSU capacity, MoReSP reduces the system cost by 43.6% compared with the best baseline. Under high vehicle load, it reduces the cost by 54.4% at arrival scale 2.0 and maintains effective packet and CPM delivery ratios of 0.849 and 0.828, respectively. These results demonstrate that MoReSP provides a reliable and balanced scheduling solution for dynamic V-IoT environments.

Efficient Congestion Mitigation Using Adaptive Load Balancing for Vehicular Ad Hoc Networks

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.

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