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
Dew Computing Architecture for Cyber-Physical Systems and IoT
TL;DR: An architecture of applying dew computing for cyber-physical systems is presented, elaborating the new features and functionalities and comparing it to other similar architectures.
Posted Content
A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook.
TL;DR: In this paper, a review of AI-enabled routing protocols designed primarily for aerial networks, with an emphasis on accommodating highly-dynamic network topology, is presented, including topology-predictive and self-adaptive learning-based routing algorithms.
Journal ArticleDOI
Edge-based compression and classification for smart healthcare systems: Concept, implementation and evaluation
Alaa Awad Abdellatif,Alaa Awad Abdellatif,Ahmed Emam,Carla-Fabiana Chiasserini,Amr Mohamed,Ali Jaoua,Rabab K. Ward +6 more
TL;DR: A reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification, and a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions.
Proceedings ArticleDOI
Deploying Fog applications How much does it cost, by the way?
TL;DR: This paper shows how the inclusion of the cost model in the FogTorchΠ open-source prototype permits to determine eligible deployments of multi-component applications to Fog infrastructures and to rank them according to their QoS-assurance, Fog resource consumption and cost.
Journal ArticleDOI
GASP: Genetic Algorithms for Service Placement in Fog Computing Systems
TL;DR: A scalable heuristic based on genetic algorithms for the problem of mapping data streams over fog nodes is presented, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes.
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.