Journal ArticleDOI
Fog Computing: Helping the Internet of Things Realize Its Potential
TLDR
Fog computing is designed to overcome limitations in traditional systems, the cloud, and even edge computing to handle the growing amount of data that is generated by the Internet of Things.Abstract:
The Internet of Things (IoT) could enable innovations that enhance the quality of life, but it generates unprecedented amounts of data that are difficult for traditional systems, the cloud, and even edge computing to handle. Fog computing is designed to overcome these limitations.read more
Citations
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Journal ArticleDOI
Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues
TL;DR: In this article, the authors reviewed fog computing technology conceptually and defined it based on the existing studies in the literature, together with its architecture, applications, advantages, and open issues with optimization methods being performed to obtain the optimal services.
Journal ArticleDOI
Modeling and Simulation Tools for Fog Computing—A Comprehensive Survey from a Cost Perspective
TL;DR: This work provides a comprehensive literature review along two axes—modeling with an emphasis in the proposed fog computing architectures and simulation which investigates the simulation tools which can be used to develop and evaluate novel fog-related ideas.
Journal ArticleDOI
Fog-based energy-efficient routing protocol for wireless sensor networks
TL;DR: A new method based on P-SEP which uses FECR and FEAR algorithms in implementation which improves the performance of fog-supported WSNs and prolong the lifetime of networks.
Journal ArticleDOI
Opportunistic Fog for IoT: Challenges and Opportunities
Niroshinie Fernando,Seng Wai Loke,Iman Avazpour,Feifei Chen,Amin B. Abkenar,Amani S. Ibrahim +5 more
TL;DR: This paper discusses key issues in opportunistic fog computing, investigates potential solutions from existing work, and proposes an opportunistic architecture for future work.
Journal ArticleDOI
Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay.
TL;DR: A delay-aware online workload allocation and scheduling (DAOWA) algorithm is proposed to achieve the goal of reducing long-term average task serve delay by satisfying as many as possible delay-sensitive IoT applications’ quality of service (QoS) requirements.
References
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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.
Book ChapterDOI
Fog Computing: A Platform for Internet of Things and Analytics
TL;DR: This chapter proposes a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing, and pays attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge.
Journal ArticleDOI
The Promise of Edge Computing
Weisong Shi,Schahram Dustdar +1 more
TL;DR: The success of the Internet of Things and rich cloud services have helped create the need for edge computing, in which data processing occurs in part at the network edge, rather than completely in the cloud.
Proceedings ArticleDOI
The Fog computing paradigm: Scenarios and security issues
Ivan Stojmenovic,Sheng Wen +1 more
TL;DR: The motivation and advantages of Fog computing are elaborated, and its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks are analysed.
Proceedings ArticleDOI
Towards wearable cognitive assistance
TL;DR: The architecture and prototype implementation of an assistive system based on Google Glass devices for users in cognitive decline that combines the first-person image capture and sensing capabilities of Glass with remote processing to perform real-time scene interpretation is described.