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
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Posted Content
CURIE: A Cellular Automaton for Concept Drift Detection
TL;DR: In CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can be utilized to detect possible distribution changes over the stream and is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.
Journal ArticleDOI
Fork and Join Queueing Networks with Heavy Tails: Scaling Dimension and Throughput Limit
Yun Zeng,Jian Tan,Cathy H. Xia +2 more
TL;DR: The results provide new insights on the scalability of a rich class of FJQN/Bs with various structures, including tandem, lattice, hexagon, pyramid, tree, and fractals.
Journal ArticleDOI
Time to forge ahead: The Internet of Things for healthcare
TL;DR: In this paper , the authors explore the key enabling technologies that underpin the fog architecture, from the sensing layer all the way up to the cloud, and propose that fog computing, a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source, offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.
Book ChapterDOI
Genetic Search Wrapper-Based Naïve Bayes Anomaly Detection Model for Fog Computing Environment
John Oche Onah,Shafi’i Muhammad Abdulhamid,Sanjay Misra,Mayank Mohan Sharma,Nadim Rana,Jonathan Oluranti +5 more
TL;DR: In this paper, a Genetic Search Wrapper-based Naive Bayes anomaly detection model (GSWNB) was proposed for fog computing environment that eliminates extraneous features to minimize time complexity as well as building an improved model that predict result with a higher accuracy using NSL-KDD dataset as benchmark dataset.
Proceedings ArticleDOI
Develop or Dissipate Fogs? Evaluating an IoT Application in Fog and Cloud Simulations.
TL;DR: This paper briefly compares fog modelling approaches of simulators, and presents detailed evaluations in two of them to show the effects of utilizing fog resources over cloud ones to execute IoT applications.
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.