Bootstrap Based Uncertainty Propagation for Data Quality estimaton in crowdsensing systems
The diffusion of mobile devices equipped with sensing, computation, and communication capabilities is opening unprecedented possibilities for high-resolution, spatiotemporal mapping of several phenomena. This novel data generation, collection, and processing paradigm termed crowdsensing lays upon complex, distributed cyber-physical systems. Collective data gathering from heterogeneous, spatially distributed devices inherently raises the question of how to manage different quality levels of contributed data. In order to extract meaningful information, it is, therefore, desirable to the introduction of effective methods for evaluating the quality of data. In this paper, we propose an approach aimed at systematic accuracy estimation of quantities provided by end-user devices of a crowd-based sensing system. This is obtained thanks to the combination of statistical bootstrap with uncertainty propagation techniques, leading to a consistent and technically sound methodology. Uncertainty propagation provides a formal framework for combining uncertainties, resulting from different quantities inuencing a given measurement activity. Statistical Bootstrap enables the characterization of the sampling distribution of a given statistics without any prior assumption on the type of statistical distributions behind the data generation process. The proposed approach is evaluated on synthetic benchmarks and on a real-world case study. Cross-validation experiments show that condence intervals computed by means of the presented technique show a maximum 1.5Percent variation with respect to interval widths computed by means of controlled standard Monte Carlo methods, under a wide range of operating conditions. In general, experimental results conrm the suitability and validity of the introduced methodology.
Distributed information systems, measurement uncertainty, statistical analysis, sensor systems and applications.