Identifying and Ranking the Application of Big Internet of Things Data Analyses

Abstract

Objective: As more and more data are generated day by day, the applicability of internet of things (IoT) devices becomes inevitable. The analysis of such data can have many benefits for the organizations and societies. Since previous research has not addressed the identification and prioritization of these applications, the purpose of this study is to identify and rank big IoT data analyses. Methodology: This article was divided into two sections. In part 1, the researchers used the meta-synthesis method to identify the applications; and in part 2, the multivariable method was used to prioritize the applications. Moreover, TELOS feasibility (Technical, Economic, Legal, Operational, & Scheduling) and AHP were used to weight and rank the criteria. Then, the applications were ranked based on the experts’ opinions through Vikor’s method. Findings: In this research, the meta-synthesis method has been used to identify the applications of big IoT data analyses. In this meta-synthesis method, 490 articles were initially identified and after eliminating conference papers, 257 articles were selected to initiate the meta-synthesis process. Finally, 51 articles were selected and as a result, 256 sub-applications were identified which were categorized into 114 main categories, 16 industries, and 7 analytic applications. It is also noteworthy that the diagnostic application within the health and transportation industries (with 102 & 100 applications, respectively), as well as the monitoring application within the health, transportation, and agriculture industries were reported to have the highest functioning. The most identified applications in industry-analysis belong to transport-diagnostic (32 applications), health-diagnostic (29 applications), health-monitoring (26 applications), agriculture-monitoring (25 applications), and transport-monitoring (20 applications). In the prioritization step, after calculating the weights based on the experts’ opinions and hierarchical analysis, the applications of transportation and health industries were ranked using TELOS feasibility as well as the experts’ ratings and the Vikor’s method. According to the experts’ opinions and TELOS feasibility criteria, the predictive applications in the transportation industry and the automation applications in the health industry have received the highest priorities. Conclusion: According to the research findings, big IoT data analysis is mostly used in the transportation and health industrieswhere the predictive applications in the transportation industry and the automation applications in the health industry have been regarded as a priority. Based on these results, the two health and transportation industries and their priority applications are proposed for the companies that want to work in this area. Due to differences in prioritization of the applications in the two transportation and health industries, the justifications for the two industries are different as well.