Simplifying IoT data stream enrichment and analytics in the edge. (June 2021)

• Record Type: Journal Article
• Authors: Gomes, Márcio Miguel; da Rosa Righi, Rodrigo; André da Costa, Cristiano; Griebler, Dalvan
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Edge computing -- IoT -- Stream processing -- Data analysis -- Framework; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107110
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Edge devices are usually limited in resources. They often send data to the cloud, where techniques such as filtering, aggregation, classification, pattern detection, and prediction are performed. This process results in critical issues such as data loss, high response time, and overhead. On the other hand, processing data in the edge is not a simple task due to devices' heterogeneity, resource limitations, a variety of programming languages and standards. In this context, this work proposes STEAM, a framework for developing data stream processing applications in the edge targeting hardware-limited devices. As the main contribution, STEAM enables the development of applications for different platforms, with standardized functions and class structures that use consolidated IoT data formats and communication protocols. Moreover, the experiments revealed the viability of stream processing in the edge resulting in the reduction of response time without compromising the quality of results. Graphical abstract: Highlights: We developed STEAM: a framework for developing data stream processing applications in the edge. STEAM enables the development of applications for different platforms, with standardized functions and class structures. A programming framework that simplifies the development of IoT data stream enrichment and analytics in the edge. The experiments revealed the viability of stream processing in the edge resulting in the reduction of response time.