Publication:
A review of fog computing and machine learning: Concepts, applications, challenges, and open issues

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Date
2019
Authors
Abdulkareem K.H.
Mohammed M.A.
Gunasekaran S.S.
Al-Mhiqani M.N.
Mutlag A.A.
Mostafa S.A.
Ali N.S.
Ibrahim D.A.
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Institute of Electrical and Electronics Engineers Inc.
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Abstract
Systems based on fog computing produce massive amounts of data; accordingly, an increasing number of fog computing apps and services are emerging. In addition, machine learning (ML), which is an essential area, has gained considerable progress in various research domains, including robotics, neuromorphic computing, computer graphics, natural language processing (NLP), decision-making, and speech recognition. Several researches have been proposed that study how to employ ML to settle fog computing problems. In recent years, an increasing trend has been observed in adopting ML to enhance fog computing applications and provide fog services, like efficient resource management, security, mitigating latency and energy consumption, and traffic modeling. Based on our understanding and knowledge, there is no study has yet investigated the role of ML in the fog computing paradigm. Accordingly, the current research shed light on presenting an overview of the ML functions in fog computing area. The ML application for fog computing become strong end-user and high layers services to gain profound analytics and more smart responses for needed tasks. We present a comprehensive review to underline the latest improvements in ML techniques that are associated with three aspects of fog computing: management of resource, accuracy, and security. The role of ML in edge computing is also highlighted. Moreover, other perspectives related to the ML domain, such as types of application support, technique, and dataset are provided. Lastly, research challenges and open issues are discussed. � 2013 IEEE.
Description
Applications; Computer graphics; Decision making; Energy utilization; Fog; Green computing; Internet of things; Learning algorithms; Learning systems; Machine learning; Natural language processing systems; Speech recognition; Computing applications; Computing paradigm; Internet of Things (IOT); NAtural language processing; Neuromorphic computing; Research challenges; Research domains; Resource management; Fog computing
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