Abstract
Internet of Things (IoT) applications continue to grow at a rapid scale. However, current cloud centric IoT architectures are not feasible to support the mobility needs as well as latency requirements of time critical IoT applications. This has restricted the growth of IoT in certain sectors. Through this paper, we explore fog-computing paradigm as an alternative IoT enabling technology. There is a need to systematically review and synthesize the fog computing concerns or challenges for IoT applications. This paper aims to address this important research need using a well-known systematic literature review (SLR) approach. Using the SLR approach and applying customized search criteria derived from the research question, 17 relevant studies were identified and reviewed in this regard from an initial set of 439 papers. In addition, 4 papers were manually identified based on their relevance. The data was organized into four major challenge categories. The findings of this research paper can help practitioners and researchers to understand the fog computing related concerns, and provide useful insights for future work. This paper is restricted to the number of reviewed studies from chosen database.
Recommended Citation
Ameen, Saleem; Han, Soyeon; Lin, Yingru; Lah, Minjae; and Kang, Byeong-Ho, "Intelligent Medical Case Based e-Learning System" (2017). ACIS 2017 Proceedings. 78.
https://aisel.aisnet.org/acis2017/78