Resource-Aware Approaches for Truth Analysis in Crowdsourcing

Although crowdsourcing can provide a large amount of information through mobile devices and mobile users, the information provided by them may be inaccurate. Various truth analysis techniques have been proposed to identify truth from the noisy data either in a heuristic manner or using statistical models. However, if the available data are limited or have large conflicts, it is difficult to identify the truth or ensure the data credibility (quality). In this paper, we address this problem by utilizing the communication networks to adaptively collect data from mobile users, especially when the existing data are not enough to ensure data credibility. Considering the requirement on data credibility and the constraint of network resources, we quantify the tradeoff between the enhanced data credibility and the increased network overhead, and propose resource-aware approaches for truth analysis. Specifically, we formalize two problems in resource-constrained mobile opportunistic networks: max-credibility which aims to maximize data credibility with some network overhead, and min-overhead which aims to achieve a specified data credibility while minimizing the network overhead. Simulation and experimental results demonstrate the effectiveness of the proposed solutions in terms of data credibility and network overhead.