Intelligent Medical Case Based e-Learning System

Saleem Ameen, Soyeon Caren Han, Yingru Lin, Minjae Lah, Byeong Ho Kang

Research output: Chapter in Book/Conference paperConference paperpeer-review

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.
Original languageEnglish
Title of host publicationACIS 2017 Proceedings
PublisherAssociation for Information Systems
Publication statusPublished - 2017
Externally publishedYes
Event28th Australasian Conference on Information Systems - University of Tasmania, Hobart, Australia
Duration: 4 Dec 20176 Dec 2017

Conference

Conference28th Australasian Conference on Information Systems
Abbreviated titleACIS 2017
Country/TerritoryAustralia
CityHobart
Period4/12/176/12/17

Fingerprint

Dive into the research topics of 'Intelligent Medical Case Based e-Learning System'. Together they form a unique fingerprint.

Cite this