Edge Computing for Internet of Things
The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce ener...
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2022
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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700 | 1 | |a Man, Ka Lok |4 edt | |
700 | 1 | |a Lee, Kevin |4 oth | |
700 | 1 | |a Man, Ka Lok |4 oth | |
245 | 1 | 0 | |a Edge Computing for Internet of Things |
260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
300 | |a 1 electronic resource (186 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
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506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Technology: general issues |2 bicssc | |
650 | 7 | |a History of engineering & technology |2 bicssc | |
650 | 7 | |a Energy industries & utilities |2 bicssc | |
653 | |a hierarchical edge computing | ||
653 | |a WSN | ||
653 | |a rapid response strategy | ||
653 | |a edge node | ||
653 | |a fog access points | ||
653 | |a cache memory | ||
653 | |a convolutional neural network | ||
653 | |a proactive caching | ||
653 | |a fog computing | ||
653 | |a Internet of Things | ||
653 | |a service placement | ||
653 | |a fog service orchestration | ||
653 | |a spectral classification | ||
653 | |a portable optical fiber spectrometers | ||
653 | |a dew computing | ||
653 | |a edge computing | ||
653 | |a smartphone | ||
653 | |a job scheduling | ||
653 | |a scheduling heuristics | ||
653 | |a mobile edge computing | ||
653 | |a mobile edge server placement | ||
653 | |a multiagent RL | ||
653 | |a edge security | ||
653 | |a offloading computation | ||
653 | |a distributed collaboration | ||
653 | |a data processing | ||
653 | |a dynamic offloading | ||
653 | |a IoT | ||
653 | |a gateways | ||
653 | |a Internet of Things (IoT) | ||
653 | |a failure recovery | ||
653 | |a FP-Growth algorithm | ||
653 | |a association rules | ||
653 | |a frequency pattern analysis | ||
653 | |a computational offloading | ||
653 | |a orchestration | ||
653 | |a functional programming | ||
653 | |a n/a | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/5479 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/84496 |7 0 |z DOAB: description of the publication |