A Fast Truth Reasoning Method for Distributed Spatiotemporal Data Based on Deep Learning
In response to the lack of effective ground truth benchmarks and participant credibility information in distributed sensing of mobile crowds,this paper introduces a Deep Learning-based Fast Truth Inference method for distributed spatiotemporal data(DLFTI).It establishes a three-tiered ground truth benchmark framework consisting of gold truth data(sourced from drones),silver truth data(sourced from highly credible participants),and bronze truth data(derived from deep matrix factorization),enabling rapid credibility calculation of participants.Additionally,the method is designed to estimate truth data across three levels and has successfully achieved fast and accurate truth inference in mobile crowd sensing.Simulation results indicate that this method significantly outperforms traditional algorithms in participant identification and truth discovery.
Mobile crowd perceptiondistributed spatiotemporal datatruth reasoningdeep learningtrusted computing
万方数据
维普
