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
Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
TL;DR: In this paper , the authors proposed a dynamic task scheduling algorithm based on attribute reduction with an enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection, which can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.
Proceedings ArticleDOI
A Malicious Node Detection Model for Wireless Sensor Networks Security Based on CHSA-MNDA Algorithm
Yongan Guo,Xin Tang,Hongbo Sun +2 more
TL;DR: A detection model of malicious nodes in wireless sensor network based on CHSA-MNDA algorithm can improve the efficiency and accuracy of malicious node detection, thereby improving the security of IoT.
Journal ArticleDOI
Distributed Fog Computing and Federated-Learning-Enabled Secure Aggregation for IoT Devices
TL;DR: This work proposes a secure aggregation protocol based on efficient additive secret sharing in the fog-computing (FC) setting that provides two simple new client selection methods and conducts experiments on high-dimensional inputs.
Journal ArticleDOI
Predictive machine learning (ml) algorithm using iot framework for novel corona virus (covid-19)
TL;DR: The research work focused on faster identification of COVID-19 virus infection cases potentially using Machine Learning (ML) algorithm from the real-time symptom data and illustrated that K-Nearest Neighbour (KNN) algorithm is highly efficient while compared with other ML algorithms in predicting the possible recovery of the infected patients from pandemic CO VID-19 with the accuracy of 96.85%.
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.