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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey [article]

Xiaofei Wang and Yiwen Han and Victor C.M. Leung and Dusit Niyato and Xueqiang Yan and Xu Chen
2019 arXiv   pre-print
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]

Mehdi Mohammadi, Ala Al-Fuqaha, Sameh Sorour, Mohsen Guizani
2018 arXiv   pre-print
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

Tarun Jaiswal, Sushma Jaiswal
2019 International Journal of Trend in Scientific Research and Development  
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

Mihai Andronie, George Lăzăroiu, Mariana Iatagan, Cristian Uță, Roxana Ștefănescu, Mădălina Cocoșatu
2021 Electronics  
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

Johanna Andrea Hurtado Sánchez, Katherine Casilimas, Oscar Mauricio Caicedo Rendon
2022 Sensors  
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

Muhammad Ali, Ashiq Anjum, M. Usman Yaseen, A. Reza Zamani, Daniel Balouek-Thomert, Omer Rana, Manish Parashar
2018 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC)  
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

Walter Cerroni, Alex Galis, Kohei Shiomoto, Mohamed Faten Zhani
2021 IEEE Communications Magazine  
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]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
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

Jonas Queiroz, Paulo Leitão, José Barbosa, Eugénio Oliveira
2019 Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics  
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

Omer Ali, Mohamad Khairi Ishak, Muhammad Kamran Liaquat Bhatti, Imran Khan, Ki-Il Kim
2022 Sensors  
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

Paula Fraga-Lamas, Lucía Ramos, Víctor Mondéjar-Guerra, Tiago M. Fernández-Caramés
2019 Remote Sensing  
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

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
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  
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

Daniele Ravi, Charence Wong, Benny Lo, Guang-Zhong Yang
2016 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)  
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

Abdu Gumaei, Mabrook Al-Rakhami, Hussain AlSalman, Sk. Md. Mizanur Rahman, Atif Alamri
2020 Computers Materials & Continua  
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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