Enhancing Urban Sensing: A Bus-Aided Two-Stage Framework for Vehicular Edge Computing

The rapid development of communication technology has enabled intelligent vehicles and edge networks, making vehicular crowdsensing an emerging paradigm for data collection and dissemination. Vehicles can be mandated to collaboratively perform large-scale data sensing. However, due to high mobility, availability, and resource constraints, it is difficult to design effective mechanisms to encourage suitable vehicles to complete sensing tasks. To overcome these challenges, this work proposed a two-stage bus-aided vehicular crowdsensing framework, which involves collaboration between the bus system and normal vehicles in the vehicular network. In the first stage, the buses perform sensing, and a modified stable matching is applied to effectively allocate tasks to vehicles while optimizing sensing cost. In the second stage, normal vehicles are selected through contract to perform sensing in areas inaccessible to the buses. The vehicles are offered optimal contracts based on the sensing quality index (SQI). The SQI of a vehicle is obtained by considering some vital metrics, including promptness, reputation, willingness, and commitment. To achieve fairness in contract design, information asymmetry is also considered. Simulations were conducted to validate the effectiveness of the proposed schemes against baseline methods. The proposed schemes achieve remarkable results in various experiments.