Phd Canditate :
Dr. Cleopatra Bardaki
Assoc. Professor K. Pramatari
PhD Thesis Abstract
Internet of Things-enabled Information Systems (IoT-enabled IS) track moving objects between locations and provide object positioning information. This doctoral dissertation explores how the design configuration of IoT-enabled IS affects their information quality (IQ) with respect to information completeness (IC) and accuracy (IA). Design configurations are mathematically expressed as a function of the system architectural and data properties (i.e. location of capture points, labeling level & number of capture points). Quantitative metrics of IQ are devised as functions of the system configuration. Next, an algorithm, which embodies the IQ metrics, is developed to calculate IQ per system configuration. The proposed algorithm is applied to assess IQ of all the alternative configurations of a product RFID-enabled IS in a retail supply chain. Statistical results suggest that both the location of capture points (e.g. RFID readers) and the labeling level (e.g. RFID tagging level) affect the IQ of IoT-enabled systems. Nevertheless, the number of capture points affects only IC, not IA.
This doctoral research contributes to the IS field with an analytical model of the alternative design choices of an IoT-enabled IS as a function of its architectural & contextual characteristics. A proposed methodology ranks the alternative design choices of the IS based on an objective valuation of IQ criteria; and recommends design choices that score high in terms of IQ. Also, it devises an IoT-enabled IS evaluation model that examines the effect of the system’s architectural and contextual characteristics on its IQ. Further, it proposes an IQ assessment methodology of IoT-enabled IS. The metrics of IQ are analytically modeled as a function of the architectural & contextual characteristics of the IoT-enabled systems.
Respectively, this doctoral study offers a two-fold support to designers and organizations that want to develop or update IoT-enabled IS aspiring ‘fit-for-use’ information and, consequently, effective decision making. An IQ assessment tool & an IQ assessment methodology that recommend specific system configurations with high degree of IQ may be applied to perform a priori (or ex ante) and a posteriori (or ex post) IQ evaluation of IoT-enabled IS.