Holmes: Tackling Data Sparsity for Truth Discovery in Location-Aware Mobile Crowdsensing

Mobile crowdsensing has become a novel and effective way to collect sensing data of people's surrounding environment. Among the data collected from multiple contributors, inconsistency often occurs due to noise, different sensor precision, or contributors' heterogeneous sensing behaviors. To tackle the data inconsistency, the problem of truth discovery has been widely studied to jointly infer the underlying ground truths and the contributors' data qualities. Existing truth discovery algorithms are based on the aggregation of large amounts of data so as to generate accurate estimations. However, in mobile crowdsensing, the collected data are usually sparsely distributed among a large sensing area, where each point of interest (PoI) may receive only a few sensing reports. In this case, traditional truth discovery algorithms may not provide an accurate truth estimation for each PoI. To tackle this challenge, in this paper, we propose an effective truth discovery method, namely Holmes, which takes advantage of the spatial correlations of the monitored phenomena by reusing each contributor's data for multiple nearby PoIs. We also take the issue of long-tail data phenomenon into the estimation of contributors' data quality levels, and proposed Holmes-LT. We further propose Holmes-OL to address the online streaming data scenarios. We evaluate the performance of our proposed algorithms on both real and synthetic datasets. The evaluation results demonstrate that our algorithms achieve significant performance improvements in terms of estimation accuracy over the existing truth discovery algorithms.