Framework for Integral Data Quality and Security Evaluation in Smartphones

Abstract

Data quality (DQ) concept should play an important role in decision-making and engineering systems. Underestimation of DQ may lead to resource waste, wrong conclusions, or inefficient decisions. Unfortunately, current approaches to DQ incorporating into data management systems are limited to particular applications. This problem is aggravated by the DQ inequality of data sources. This is especially critical in mobile crowd-sensing applications where data may come from unverified data contributors using the smartphones and other mobile devices. To facilitate the expansion of DQ evaluation to a wider spectrum of applications, this article presents a framework for integral DQ and security evaluation in Android-based smartphones. The developed framework provides support for selecting the DQ metrics and implementing their calculus by integrating diverse sensor DQ and security metrics. We present multiple calculi for DQ and security evaluation such as hierarchical fuzzy rules expert system, neural networks, and algebraic functions. Case studies that demonstrate the framework's performance in addressing real-life tasks are presented and the achieved results are analyzed. The implementation results validate the framework's capability of performing comprehensive DQ evaluations.