Augmented Multiagent DRL for Multi-Incentive Task Prioritization in Vehicular Crowdsensing

Vehicular crowdsensing (VCS) within the social Internet of Vehicles (IoV) significantly advances urban transportation management by enhancing road safety, traffic efficiency, and the overall driving experience. This article presents an intelligent multiagent deep reinforcement learning (DRL) framework for augmented dynamic task prioritization in a multi-incentive VCS system. Our framework, named intelligent multiagent reinforcement learning (IMARL), leverages augmented intelligence to integrate human-like decision-making processes with autonomous vehicle operations, ensuring more adaptive and robust task management. The proposed IMARL framework offers several key advantages: it dynamically adjusts the sensing levels of each vehicle, ensuring efficient energy usage and minimized processing times, and employs a data-quality aware multi-incentive utility model to capture both functional and social incentives. Additionally, our framework incorporates a layered server architecture, enhancing system resilience and scalability. Simulation results demonstrate the superiority of our approach. IMARL achieves significant improvements in task completion rates, energy consumption, and processing delays compared to other DRL and non-DRL benchmark methods. Furthermore, our approach exhibits strong adaptability to changing environmental conditions, maintaining high performance even in high-density traffic scenarios. These quantified results validate the effectiveness of the proposed framework, highlighting its potential to significantly enhance VCS systems in real-world applications.