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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
[article]
2019
arXiv
pre-print
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In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management ...
promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL. ...
Edge intelligence and intelligent edge are not independent of each other. Edge intelligence is the goal, and the DL services in intelligent edge are also a part of edge intelligence. ...
arXiv:1907.08349v2
fatcat:4hfqgdto4fhvlguwfjxuz3ik5q
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
[article]
2018
arXiv
pre-print
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In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely Deep Learning (DL), to facilitate the analytics and learning in the IoT domain. ...
DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. ...
In the following subsection, we review several state-of-theart enabling technologies that facilitate deep learning on the fog and cloud platforms.
A. ...
arXiv:1712.04301v2
fatcat:kr64lst37rhlfcpaxckgzlozvu
Deep Learning Based Pain Treatment
2019
International Journal of Trend in Scientific Research and Development
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Among machine learning methods, a subset has so far been applied to pain research-related problems, SVMs, regression models, deep learning and several kinds of neural networks so far most often revealed ...
This discipline uses Computational processing of difficult pain-associated records and relies on "intelligent" Machine learning algorithms. ...
This paper represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. ...
doi:10.31142/ijtsrd23639
fatcat:tqg4u3tkgjhmjpya67g3lnewwu
Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems
2021
Electronics
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upon the progression of operations advancing a system to the intended state in CPPSs. ...
With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity ...
The convergence of standard automation systems within CPPSs, together with service-oriented designs and fog, edge, and cloud computing technologies, are developing sustainable manufacturing Internet of ...
doi:10.3390/electronics10202497
fatcat:rryhw72fhvalloix23qkxwh4ca
Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
2022
Sensors
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of deep neural networks, the exploration–exploitation method, and the use cases (or vertical applications). ...
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. ...
Acknowledgments: The authors thank the University of Cauca and the Ministry of Information and Communication Technologies, Colombia, for supporting this investigation. ...
doi:10.3390/s22083031
pmid:35459015
pmcid:PMC9032530
fatcat:ibv767jl3nh27iu27vfn2t7z7m
Edge Enhanced Deep Learning System for Large-Scale Video Stream Analytics
2018
2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC)
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We address this problem by distributing the deep learning pipeline across edge and cloudlet/fog resources. ...
However, the network connecting the data capture source and the cloud platform can become a bottleneck. ...
Deep Model Training The deep learning training for object recognition was performed on the cloud platform. ...
doi:10.1109/cfec.2018.8358733
dblp:conf/icfec/AliAYZBRP18
fatcat:3g5mvxvwkfdwhob6dmersotd7u
Series Editorial: Network Softwarization and Management
2021
IEEE Communications Magazine
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capabilities, empowering the network with inbuilt cognition and intelligence. ...
"Network Softwarization" advocates for network architectures that separate the software implementing network functions, protocols and services from the hardware running them. ...
Edge Cloud-native Networking The third article, "DEEP: A Vertical-Oriented Intelligent and Automated Platform for the Edge and Fog" by Guimarães et al., focuses on a novel integration of the cloud-tothings ...
doi:10.1109/mcom.2021.9475153
fatcat:k22dxqyadrh53hwscmxg6oh4ku
Deep Learning in Mobile and Wireless Networking: A Survey
[article]
2019
arXiv
pre-print
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In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. ...
We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. ...
Bang et al. introduce a low-power and programmable deep learning processor to deploy mobile intelligence on edge devices [154] . ...
arXiv:1803.04311v3
fatcat:awuvyviarvbr5kd5ilqndpfsde
Distributing Intelligence among Cloud, Fog and Edge in Industrial Cyber-physical Systems
2019
Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
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The solution lies in taking advantage of Edge and Fog computing to create a decentralized multi-level data analysis computing infrastructure that supports the development of industrial CPS. ...
In this context, this work discusses the distribution of intelligence along Cloud, Fog and Edge computing layers in industrial CPS, leveraging some research challenges and future directions. ...
ACKNOWLEDGEMENTS This work is part of the GO0D MAN project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N o 723764. ...
doi:10.5220/0007979404470454
dblp:conf/icinco/QueirozLBO19
fatcat:xryk5kmlwba5jebcdfsxgqesai
A Comprehensive Review of Internet of Things: Technology Stack, Middlewares, and Fog/Edge Computing Interface
2022
Sensors
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Finally, the interfacing of Fog/Edge Networks to IoT technology stack is thoroughly investigated by discussing the current research and open challenges in this domain. ...
The main scope of this study is to provide a comprehensive review into IoT technology (the horizontal fabric), the associated middleware and networks required to build future proof applications (the vertical ...
The authors would also like to thank Hayat Dino Bedru, and Maham Hussain for the their time and support during the critical review of this article. ...
doi:10.3390/s22030995
pmid:35161740
pmcid:PMC8840251
fatcat:sfaylca5lzam7kqq3p6kujtld4
A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance
2019
Remote Sensing
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In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. ...
Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing ...
This approach is based on a Deep Neural Network (DNN) architecture for trail detection that uses transfer learning to estimate the view orientation and the lateral offset of the MAV with respect to the ...
doi:10.3390/rs11182144
fatcat:54xs26xnvzf7rfa5b64tuzkz44
Deep Learning in Mobile and Wireless Networking: A Survey
2019
IEEE Communications Surveys and Tutorials
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In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. ...
We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. ...
Bang et al. introduce a low-power and programmable deep learning processor to deploy mobile intelligence on edge devices [156] . ...
doi:10.1109/comst.2019.2904897
fatcat:xmmrndjbsfdetpa5ef5e3v4xda
Table of Contents
2021
IEEE Communications Magazine
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automated Platform for the edGe and foG Carlos Guimarães, Milan Groshev, Luca Cominardi, Aitor Zabala, Luis M. ...
consistent comPosition and modular data Plane ProGramminG Ricardo Parizotto, Lucas Castanheira, Fernanda Bonetti, Anderson Santos, and Alberto Schaeffer-Filho deeP: a vertical-oriented intelliGent and ...
doi:10.1109/mcom.2021.9475428
fatcat:ygejndq4hffgrpl7bwgrf3xo4y
Deep learning for human activity recognition: A resource efficient implementation on low-power devices
2016
2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
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In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. ...
To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial ...
deep learning approach are compared on two Android smartphones and on the Intel Edison Development Platform. ...
doi:10.1109/bsn.2016.7516235
dblp:conf/bsn/RaviWLY16
fatcat:cczyz2r3grdatknepcn7ymu3u4
DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing
2020
Computers Materials & Continua
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The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful edge devices for recognition. ...
The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. ...
Funding Statement: The authors received no specific funding for this study.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study. ...
doi:10.32604/cmc.2020.011740
fatcat:hdxcvuzq3bf2tfyzecmqf22djm
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