A Worker Selection Scheme for Vehicle Crowdsourcing Blockchain

With the improvement of the computing power and storage capacity of vehicular equipment, as well as the growing privacy concerns over sharing sensitive raw data, federated learning can be a promising solution for realizing distributed vehicular crowdsourcing services. The global learning model can be used as crowdsourcing tasks and assigned to vehicles that utilize their local training models. In such a distributed scenario, it is essential to ensure the completion of high-quality training tasks and the security of sensitive data. We propose a vehicular crowdsourcing blockchain to achieve secure reputation management of vehicles in a distributed manner, providing a safe and trusted solution for vehicle crowdsourcing services. We design a worker selection scheme, which combines the reputation of workers with the amount of data as an indicator of worker reliability. We further propose an effective incentive machine to enable more workers with high reputations to participate in crowdsourcing tasks and to contribute higher quality local data. Simulation results show that the worker selection scheme improves the matching rate by 10% and significantly improves the total utility. The proposed scheme is deployed on the IBM Hyperledger Fabric platform to observe its real-world running time and overall performance.