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-Driven Context-Aware Architecture for Node Discovery and Energy Saving Strategy for Internet of Things Environments
TL;DR: This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments that exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process.
Journal ArticleDOI
Energy optimised IoT assisted multiple fuzzy aggravated energy scheduling approach for smart scheduling systems
TL;DR: The results show that Multi-Fuzzy Algorithms Energy Scheming outperforms conventional system design which improves accuracy and reduces the execution time.
Journal ArticleDOI
In-Network Caching for the Green Internet of Things
TL;DR: In this article, a dynamic in-network caching scheme is proposed to decide whether the sensed data should be cached in brokers or not, where the location and delay requirement of the clients, traffic load of the brokers as well as the energy level of the sensor nodes are encapsulated in the proposed scheme.
Journal ArticleDOI
DOT: Decentralized Offloading of Tasks in OFDMA-Based Heterogeneous Computing Networks
TL;DR: DOT, a novel Decentralized Offloading of Tasks scheme in orthogonal frequency division multiple access (OFDMA)-based heterogeneous MEC, is proposed to minimize the sum cost in terms of energy consumption and delay and an offloading algorithm to achieve the NE is developed by exploiting the finite improvement property.
Journal ArticleDOI
Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner
TL;DR: The necessary design and implementation details are highlighted for an experimental water usage reporting system that exhibits Edge Artificial Intelligence (Edge AI) functionality and a first set of corresponding evaluation results is presented.
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.