Data Quality Based Intelligent Instrument Selection with Security Integration

Abstract

We propose a novel Data Quality with Security (DQS) integrated instrumentation selection approach that facilitates aggregation of multi-modal data from heterogeneous sources. As our major contribution, we develop a framework that incorporates multiple levels of integration in finding the best DQS-based instrument selection: data fusion from multi-modal sensors embedded into heterogeneous platforms, using multiple quality and security metrics and knowledge integration. Our design addresses the security aspect in the instrumentation design, which is commonly overlooked in real applications, by aggregating it with other metrics into an integral DQS calculus. We develop DQS calculus that formalizes the problem of finding the optimal DQS value. We then propose a Genetic Algorithm–based solution to find an optimal set of sensors in terms of the DQS they provide, while maintaining the level of platform security desirable by the user. We show that our proposed algorithm demonstrates optimal real-time performance in multi-platform instrument selection. To facilitate the framework application by the instrumentation designers and users, we develop and make available multiple Android applications.