Fog data management: A vision, challenges, and future directions

Abstract

Cloud computing with its key facets and its inherent advantages still faces several challenges in the Internet of Things (IoT) ecosystem. The distance among the IoT end devices and cloud computing might be a problem for latency-sensitive applications such as catastrophe management and content transference applications. Fog computing is a novel paradigm to address such issues that playacts a significant role in massive and real-time data management systems in an IoT environment. Particularly IoT data management by fog computing is one important phase for latency reduction in latency-sensitive applications and necessary to generate more skilled knowledge and intelligent decisions. In this study, we used the SLR (systematic literature review) method to survey fog data management to understand the various topics and main contexts in this domain that have been newly offered. The target of this article is classifying and analyzing the researches about the fog data management domain which has been released from 2014 to 2019. A context-based taxonomy is offered for fog data management including data processing, data storage and data security based on the context of papers that are elected with the SLR method in our study. Based on presented technical taxonomy, the grouped papers in any context are compared with each other pursuant to some metrics of fog data management reference model. Then, for any selected research, the new findings, advantages, and weaknesses are debated. Finally, based on studies the open issues in fog data management and their related challenges for future researches are highlighted.