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Segregation of IoT Traffic with Machine Learning Techniques

Shilpa P Khedkar, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
In this manuscript, machine learning and deep neural networks-based approaches are proposed for segregating the IoT traffic which eventually enhances the throughput of IoT networks and reduces the congestion  ...  With the advent of cloud computing and evolution of IoT, the classification of traffic over IoT networks has attained significance importance due to rapid growth of users and devices.  ...  An ensemble method in bagging classifier combines the predictions/results from multiple machine learning algorithms altogether to make better and accurate predictions than any individual algorithm.  ... 
doi:10.17762/turcomat.v12i2.1806 fatcat:jbvkavbcwfdwvascjhpvdvb54u

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Shreshth Tuli, Nipam Basumatary, Sukhpal Singh Gill, Mohsen Kahani, Rajesh Chand Arya, Gurpreet Singh Wander, Rajkumar Buyya
2019 Future generations computer systems  
We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis.  ...  Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time.  ...  We would like to thank the editor, area editor and anonymous reviewers for their valuable comments and suggestions to help and improve our research paper.  ... 
doi:10.1016/j.future.2019.10.043 fatcat:eqhosiszbvafzhy7wjkr3poiwe

Deep learning in the fog

Andrzej Sobecki, Julian Szymański, David Gil, Higinio Mora
2019 International Journal of Distributed Sensor Networks  
Processing all the data in the cloud may not be sufficient in cases when we need privacy and low latency, and when we have limited Internet bandwidth, or it is simply too expensive.  ...  In the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized  ...  of Electronics, Telecommunications and Informatics of Gdan´sk University of Technology.  ... 
doi:10.1177/1550147719867072 fatcat:2b6j6gr32nf4lksf24n6z63qzu

Compressing Representations for Embedded Deep Learning [article]

Juliano S. Assine, Alan Godoy, Eduardo Valle
2019 arXiv   pre-print
We explore that trade-off by studying the compressibility of representations at different stages of MobileNetV2, showing those results agree with theoretical intuitions about deep learning, and that an  ...  Despite recent advances in architectures for mobile devices, deep learning computational requirements remains prohibitive for most embedded devices.  ...  Valle is partially funded by CNPq grant PQ-2 311905/2017-0 and FAPESP grant 2019/05018-1. The RECOD Lab receives additional funds from FAPESP, CNPq, and CAPES.  ... 
arXiv:1911.10321v1 fatcat:on62hlkigzf7vfcmcwzzprqnme

Fog Computing Based on Machine Learning: A Review

Fady Esmat Fathel Samann, Adnan Mohsin Abdulazeez, Shavan Askar
2021 International Journal of Interactive Mobile Technologies  
Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage.  ...  Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications.  ...  Hence, an FC optimization scheme that is device and human-driven for energy consumption and latency is proposed and presented as two case studies.  ... 
doi:10.3991/ijim.v15i12.21313 fatcat:ztfuzrshq5eavduujcrhp3unhu

Machine Learning Systems for Intelligent Services in the IoT: A Survey [article]

Wiebke Toussaint, Aaron Yi Ding
2020 arXiv   pre-print
Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services.  ...  It covers the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices.  ...  and bandwidth are testing the limits of complex, large deep learning models in low resource environments [33] [180] [153] .  ... 
arXiv:2006.04950v3 fatcat:xrjcioqkrrhpvgmwmutiajgfbe

Deep Learning and Fog Computing: A Review

Shavan Askar, Zhala Jameel Hamad, Shahab Wahhab Kareem
2021 Zenodo  
use with power for Internet-of-Things (IoT) apps that require speed.  ...  Furthermore, Deep Learning (DL), an important field, has made significant progress in a variety of research areas, including robotics, face recognition, neuromorphic computing, decision-making, computer  ...  The main fog node identified internal and external anomalies of IoT traffic using the learned VCDL model. The Bot-IoT dataset from UNSW was used to test the proposed anomaly detection system.  ... 
doi:10.5281/zenodo.5222646 fatcat:wevh4azi6za2hh74g2c66icaye

Deep Learning in IoT systems: A Review

Shavan Askar, Chnar Mustafa Mohammed, Shahab Wahhab Kareem
2021 Zenodo  
This paper gives an overview of the applications that need to combine deep learning to serve IoT applications in an efficient and automated manner.  ...  To imitate the human intelligence level, the machine or software is made smarter by using advanced deep learning.  ...  DL common methods, 2.4.2 DL Benefits and 2.4.3. DL applications and Platforms). Section 3 is the use cases of using deep learning with IoT.  ... 
doi:10.5281/zenodo.5221645 fatcat:busbazmui5gpfog7kce43muw5a

A Deep Learning method for effective channel allotment for SDN based IOT

Shilpa P Khedkar, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
Different machine learning algorithm can be used for prediction of network traffic and allocation of the channel is done for better assignment.  ...  Due to advances in the field of internet of things (IoT), the transmission speed become very important and need to be discussed.  ...  SDN Enabled IOT Deep Learning for Networking Deep learning is an ML branch focused on a series of algorithms that create computational models to reflect abstractions of high-level data.  ... 
doi:10.17762/turcomat.v12i2.1508 fatcat:znrywmkkbfbcjab4kdlvybg72i

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction [article]

Ferhat Ozgur Catak, Evren Catak, Murat Kuzlu, Umit Cali, Devrim Unal
2021 arXiv   pre-print
With the rapid developments of machine learning techniques, especially deep learning, it is critical to take the security concern into account when applying the algorithms.  ...  The main idea behind adversarial attacks against machine learning models is to produce faulty results by manipulating trained deep learning models for 6G applications for mmWave beam prediction.  ...  Input Dataset Training Set Test Set Training Set Test Set Deep Learning Model Model: h(x) Loss Function: J(θ) Deployment Base Station Malicious User x0 x1 x2 · · · · · · · · · · · · xn Legitimate  ... 
arXiv:2105.03905v3 fatcat:rtp75vsoure5lfozju53odz7oa

Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems

Bouziane Brik, Adlen Ksentini, Maha Bouaziz
2020 IEEE Access  
INDEX TERMS Deep learning, federated deep learning, UAVs-based wireless networks, wireless communications.  ...  Due to its privacy-preserving and low communication overhead and latency, FDL is much more adequate for many UAVs-enabled wireless applications.  ...  In this case, the deep RNN with Long Short-Term Memory (LSTM) may be applied to predict next energy consumption due to its effectiveness to deal with timevarying data [23] .  ... 
doi:10.1109/access.2020.2981430 fatcat:7jbg7xcav5bifiniyxyubq4s7y

Deep Learning at the Edge [article]

Sahar Voghoei, Navid Hashemi Tonekaboni, Jason G. Wallace, Hamid R. Arabnia
2019 arXiv   pre-print
In this paper, we discuss one of the most widely used machine learning methods, namely, Deep Learning (DL) and offer a short survey on the recent approaches used to map DL onto the edge computing paradigm  ...  An edge device is an electronic device that provides connections to service providers and other edge devices; typically, such devices have limited resources.  ...  There are multiple challenges associated with implementing and running deep learning algorithms on the edge devices.  ... 
arXiv:1910.10231v1 fatcat:sqggugvy4nh2dc6yskm6lvgiee

Machine Learning in the Internet of Things for Industry 4.0 [article]

Tomasz Szydlo, Joanna Sendorek, Robert Brzoza-Woch, Mateusz Windak
2020 arXiv   pre-print
In the paper, we analyse what latency is introduced by communication technologies used in the IoT for cloud connectivity and how they influence the response times of the system.  ...  We propose a flow processing stack for such systems along with the organizational machine learning architectural patterns that enable the possibility to spread the learning and inferencing on the edge  ...  Acknowledgements The research presented in this paper was partially supported by the National Centre for Research and Development (NCBiR) under Grant No. LIDER/15/0144/L-7/15/NCBR/2016.  ... 
arXiv:2005.11146v1 fatcat:3apu3rdxzrc4hjqiunw7c5bjnm

Deep Learning: Edge-Cloud Data Analytics for IoT

Ananda M. Ghosh, Katarina Grolinger
2019 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)  
Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based approach for data reduction on the edge with the machine learning on the  ...  However, this results in increased network traffic and latencies.  ...  This study explores the use of deep learning, specifically autoencoders, for data reduction in the edge-cloud IoT data analytics context.  ... 
doi:10.1109/ccece.2019.8861806 dblp:conf/ccece/GhoshG19 fatcat:q5ybcleaoffzfab2m3gcut3uv4

Federated Learning for Internet of Things: A Comprehensive Survey [article]

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 arXiv   pre-print
The important lessons learned from this review of the FL-IoT services and applications are also highlighted.  ...  The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).  ...  Instead of sharing the raw IoT data, FL offers an alternative of sharing learning results to enable intelligent IoT networks with low latency and privacy preservation.  ... 
arXiv:2104.07914v1 fatcat:b5wsrfcbynel7jqdxpfw4ftwh4
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