Profit-Driven and Delay-Sensitive Task Offloading for Automated Guided Vehicles in Vehicular Fog Networks via Dynamic Fee-Charging Optimization

Automated Guided Vehicles (AGVs) are integral to modern industrial automation, streamlining material handling and logistics operations. However, the increasing computational demands of AGVs, including real-time navigation, decision-making, and task scheduling, necessitate efficient offloading mechanisms. Vehicular Fog Networks (VFNs), a specialized form of fog computing, offer a promising solution by leveraging distributed computing resources at the network edge. Despite their advantages, VFNs introduce challenges such as fluctuating connectivity, variable node availability, and unpredictable computational loads, making traditional offloading approaches inadequate. This paper proposes a novel optimization framework that integrates profit maximization and resource-aware task offloading for AGVs in VFNs. The framework incorporates a dynamic fee-charging model that adjusts service charges based on user-experienced Quality of Service (QoS), incentivizing service providers to enhance performance. Additionally, an Integer Linear Programming (ILP) model is developed to balance profit optimization with delay-aware QoS constraints. To ensure scalability, resource expansion strategies are introduced, dynamically allocating computing resources based on real-time task demands. Extensive performance evaluations demonstrate the effectiveness of the proposed framework in optimizing task offloading for AGVs in VFN-based Industrial Internet of Things (IIoT) environments. Results indicate significant improvements in service profitability, resource utilization, and delay-sensitive task execution. By addressing key IoT challenges in AGV-driven IIoT systems, this study contributes to advancing intelligent resource allocation strategies in fog computing and IoT ecosystems. The proposed approach enhances AGV efficiency and service quality, reinforcing the role of IoT-enabled VFNs in next-generation industrial automation.