Fog Computing: A Taxonomy, Survey and Future Directions
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TLDR
In this paper, the challenges in fog computing acting as an intermediate layer between IoT devices/sensors and cloud datacentres and review the current developments in this field are discussed.Abstract:
In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.read more
Citations
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Journal ArticleDOI
Security challenges in fog-computing environment: a systematic appraisal of current developments
Jimoh Yakubu,Shafi’i Muhammad Abdulhamid,Haruna Atabo Christopher,Haruna Chiroma,Mohammed Abdullahi +4 more
TL;DR: This review is intended to guide experts and novice researchers to identify certain areas of security challenges in fog computing for future improvements and to create a taxonomy based on the different security techniques used.
Journal ArticleDOI
Dynamic Energy Efficient Resource Allocation Strategy for Load Balancing in Fog Environment
Anees Ur Rehman,Zulfiqar Ahmad,Ali Imran Jehangiri,Mohammed Alaa Ala’anzy,Mohamed Othman,Arif Iqbal Umar,Jamil Ahmad +6 more
TL;DR: Simulation results reveal that the presented strategy is an efficient resource allocation scheme for balancing load in fog environments to minimize the energy consumption and computation cost by 8.67 % and 16.77 % as compared with existing DRAM scheme.
Journal ArticleDOI
Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment
TL;DR: This work proposes a prototype-based clustering algorithm to divide enterprise users into several categories based on their total electricity consumption, and proposes a model selection approach by analyzing users’ historical records of electricity consumption and identifying the most important features.
Posted Content
A Survey of Fog Computing and Communication: Current Researches and Future Directions.
TL;DR: This survey discusses the evolution of distributed computing from the utility computing to the fog computing, various research challenges for the development of fog computing environments, the current status on fog computing research along with a taxonomy of various existing works in this direction.
Journal ArticleDOI
Demand-Driven Deep Reinforcement Learning for Scalable Fog and Service Placement
TL;DR: A Deep Reinforcement Learning (DRL) solution, named Intelligent Fog and Service Placement (IFSP), to perform instantaneous placement decisions proactively and a novel end-to-end architecture utilizing a service scheduler and a bootstrapper are proposed.
References
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Edge Computing: Vision and Challenges
TL;DR: The definition of edge computing is introduced, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge Computing.
Proceedings ArticleDOI
Fog computing and its role in the internet of things
TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
Book ChapterDOI
Fog Computing and Its Role in the Internet of Things
TL;DR: This chapter argues that the above characteristics make the Fog the appropriate platform for a number of critical internet of things services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).
Journal ArticleDOI
iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments
TL;DR: In this paper, the authors propose a simulator, called iFogSim, to model IoT and fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost.
Journal ArticleDOI
Edge-centric Computing: Vision and Challenges
Pedro García López,Alberto Montresor,Dick Epema,Anwitaman Datta,Teruo Higashino,Adriana Iamnitchi,Marinho P. Barcellos,Pascal Felber,Etienne Rivière +8 more
TL;DR: This position paper position that a new shift is necessary in computing, taking the control of computing applications, data, and services away from some central nodes to the other logical extreme of the Internet, and refers to this vision of human-centered edge-device based computing as Edge-centric Computing.