Cost-Efficient Vehicular Crowdsensing Based on Implicit Relation Aware Graph Attention Networks

The development of vehicular intelligence and networking has led to the emergence of vehicular crowdsensing as a new perception paradigm. By integrating edge computing, vehicular intelligence, and Internet of Vehicles technologies, vehicular crowdsensing is poised to have far-reaching implications in the domains of intelligent transportation, industrial sensing, and smart cities. In urban sensing scenarios, recruiting a large number of users can provide a lot of useful data, yet is costly due to expected financial incentive plans. As a solution, sparse mobile crowdsensing techniques have been proposed to collect data at a subset of sensing grids for data inference. However, the majority of these methods rely solely on explicit connections between sensing grids and do not consider implicit relations, which are crucial for accurate data inference. To achieve both high-quality data inference and cost reduction, we propose a cost-efficient vehicular crowdsensing scheme based on implicit relation-aware graph attention networks (CVC-IRGAT), which combines missing data inference with active grid selection. First, we design the IRGAT model to capture implicit and explicit relations between grids through a dual-channel mechanism of relation-aware and graph attention. Then, we design a method to assess the inferred data based on the Gaussian mixture model. Given the assessment values, a deviation information score function is proposed to measure the importance of the inferred values and model errors. Finally, we introduce active learning iterations to select the grids in accordance with this function. Extensive experiments have been conducted on real-world datasets, which demonstrate the superiority of the proposed CVC-IRGAT.