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Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

Mahzabeen Emu, Salimur Choudhury
2021 IEEE Open Journal of the Computer Society  
, and intelligently for next-generation networks.  ...  In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions.  ...  To the best of our knowledge, none of the existing works suggested the use of ensemble deep learning 59 assisted strategies for VNF orchestration in order to function 60 within reasonable running time  ... 
doi:10.1109/ojcs.2021.3098462 fatcat:mycl3xng55hnjkhczkb46axa5u

2020 Index IEEE Internet of Things Journal Vol. 7

2020 IEEE Internet of Things Journal  
., Rateless-Code-Based Secure Cooperative Transmission Scheme for Industrial IoT; JIoT July 2020 6550-6565 Jamalipour, A., see Murali, S., JIoT Jan. 2020 379-388 James, L.A., see Wanasinghe, T.R.,  ...  ., +, JIoT Jan. 2020 99-115 Joint DNN Partition Deployment and Resource Allocation for Delay-Sensitive Deep Learning Inference in IoT.  ...  ., +, JIoT Sept. 2020 8287-8295 JIoT Oct. 2020 9372-9382 Interaction of Edge-Cloud Computing Based on SDN and NFV for Next Generation IoT.  ... 
doi:10.1109/jiot.2020.3046055 fatcat:wpyblbhkrbcyxpnajhiz5pj74a

Network Function Virtualization: State-of-the-Art and Research Challenges

Rashid Mijumbi, Joan Serrat, Juan-Luis Gorricho, Niels Bouten, Filip De Turck, Raouf Boutaba
2016 IEEE Communications Surveys and Tutorials  
trade-offs in developing technologies for its successful deployment.  ...  of new services with increased agility and faster time-to-value.  ...  ACKNOWLEDGMENT The authors are indebted to the Editor-in-Chief for coordinating the review process, and to the anonymous reviewers for their insightful comments and suggestions.  ... 
doi:10.1109/comst.2015.2477041 fatcat:w4s54gwp45e7lmg5j2k674ztie

A Survey on Edge Computing Systems and Tools

Fang Liu, Guoming Tang, Youhuizi Li, Zhiping Cai, Xingzhou Zhang, Tongqing Zhou
2019 Proceedings of the IEEE  
Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey.  ...  To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing  ...  CoreML supports not only deep learning models, but also some standard models such as tree ensembles, SVMs, and generalized linear models.  ... 
doi:10.1109/jproc.2019.2920341 fatcat:rocspx5ziffblfzaye2xhebe3e

Internet of Things 2.0: Concepts, Applications, and Future Directions

Ian Zhou, Imran Makhdoom, Negin Shariati, Muhammad Ahmad Raza, Rasool Keshavarz, Justin Lipman, Mehran Abolhasan, Abbas Jamalipour
2021 IEEE Access  
However, machine learning and deep learning based IoT systems are susceptible to "Butterfly Effect."  ...  Thus, this architecture reduces the time of service development, service deployment, and service configuration. iTaaS only reduces complexity and interoperability issues for software deployment.  ... 
doi:10.1109/access.2021.3078549 fatcat:g5jkc5p6tngpfonbhtsbcjipai

Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

Martins O. Osifeko, Gerhard P. Hancke, Adnan M. Abu-Mahfouz
2020 Journal of Sensor and Actuator Networks  
Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage.  ...  We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions.  ...  IoT Al-Garadi, Mohamed [11] ML and Deep Learning (DL) methods solutions for IoT security Mohammadi, Al-Fuqaha [12] DL in data analytics and learning in the IoT domain The acceptance of artificial  ... 
doi:10.3390/jsan9020021 fatcat:jhhenqau2fgvdjmoqkovs73rle

AI and ML – Enablers for Beyond 5G Networks

Alexandros Kaloxylos, Anastasius Gavras, Daniel Camps Mur, Mir Ghoraishi, Halid Hrasnica
2020 Zenodo  
Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data.  ...  Finally other specific methods are presented, such as generative adversarial networks and unsupervised learning and clustering.  ...  Applying a reinforcement learning strategy, such as Q-learning, can generate more profitable deployment decisions in a federated ecosystem.  ... 
doi:10.5281/zenodo.4299895 fatcat:ngzbopfm6bb43lnrmep6nz5icm

D2.3 Network Architecture Definition, Design Methods and Performance Evaluation

António Eira, João Pedro, Marc Ruiz, Luis Velasco, Jaume Comellas, Gabriel Junyent, Ramon Casellas, Raül Muñoz, Laia Nadal, Michela Svaluto Moreolo, Marco Quagliotti, Anna Chiadò Piat (+25 others)
2019 Zenodo  
The overall Metro-Haul objective is to architect and design cost-effective, energy-efficient, agile and programmable metro networks that are scalable for 5G access and future requirements, encompassing  ...  Figure 28 shows the generic hardware architecture: Ensemble Connector is a high-performance switching and virtualization platform for hosting multivendor VNFs.  ...  Network Scenarios for Architecture Evaluation Geotype definitions The evaluation of new technologies for the deployment of next generation optical metro transport must take into account the abundance  ... 
doi:10.5281/zenodo.3496938 fatcat:3duf4xxqdfg5xgri57krrlmz5u

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

Ijaz Ahmad, Shariar Shahabuddin, Hassan Malik, Erkki Harjula, Teemu Leppanen, Lauri Loven, Antti Anttonen, Ali Hassan Sodhro, Muhammad Mahtab Alam, Markku Juntti, Antti Yla-Jaaski, Thilo Sauter (+3 others)
2020 IEEE Access  
Future research directions are drawn to help the research community to circumvent the challenges of future services (e.g. for massive IoT) and technologies (e.g.  ...  Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services.  ...  As the next generation of communication systems are expected to support new communication paradigms such as IoT and Machine-to-Machine (M2M) services besides the traditional voice and data services, the  ... 
doi:10.1109/access.2020.3041765 fatcat:erbcetvcrjabrl4qloow3dqcai

Empowering Vertical Industries through 5G Networks - Current Status and Future Trends

Alexandros Kaloxylos, Anastasius Gavras, Raffaele De Peppe
2020 Zenodo  
This white paper summarizes the progress and key findings produced by 5G PPP projects to provide 5G network services for vertical industries.  ...  This 5G eHealth Ambulance use case leverages network slicing with video optimizer VNFs and machine learning based Telestroke VNFs as a service for the on-board real-time video streaming and in-network  ...  targets for the deployment of the service.  ... 
doi:10.5281/zenodo.3698113 fatcat:mkjzdvjnqfehxply655s7mdqam

Program

2020 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)  
(5G) networks are ready for deployment, discussions over sixth generation (6G) networks are down the road.  ...  In this work, we will be presenting a simple and fast technic for generating a large amount of data stability on Matlab/Simscape Power System (SPS) for deep learning.  ...  for a variety of energy services.  ... 
doi:10.1109/ccece47787.2020.9255763 fatcat:mpf7smikpfc77bu73ciqstdagm

Softwarization of 5G Networks – Implications to Open Platforms and Standardizations

David Lake, Ning Wang, Rahim Tafazolli, Louis Samuel
2021 IEEE Access  
ACKNOWLEDGMENT The authors would like to acknowledge the support from the University of Surrey's 5G Innovation Centre 940 (5GIC) (http://www.surrey.ac.uk/5gic) members and EPSRC NG-CDI (EP/R004935/1) project for  ...  example a for-purpose IoT protocol with a non-IP packet and addressing format.  ...  Whilst such a strategy has been adequate for supporting a relatively limited range of applications in 4G, system rigidity apparently has become a bottleneck feature for emerging 5G-oriented services with  ... 
doi:10.1109/access.2021.3071649 fatcat:2nmuasyqdfb4xc4wpeqeodqvom

The Roadmap to 6G Security and Privacy

Pawani Porambage, Gurkan Gur, Diana Pamela Moya Osorio, Madhusanka Liyanage, Andrei Gurtov, Mika Ylianttila
2021 IEEE Open Journal of the Communications Society  
Although the fifth generation (5G) wireless networks are yet to be fully investigated, the visionaries of the 6th generation (6G) echo systems have already come into the discussion.  ...  All in all, this work intends to provide enlightening guidance for the subsequent research of 6G security and privacy at this initial phase of vision towards reality.  ...  Different ML types (e.g., neural network, deep learning, supervised learning) can be applied for privacy protection in terms of data, image, location, and communication (e.g.  ... 
doi:10.1109/ojcoms.2021.3078081 fatcat:r5g662rcxjcgvjfc2el3lesdzy

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

Yanling Zhao, Ye Li, Xinchang Zhang, Guanggang Geng, Wei Zhang, Yanjie Sun
2019 IEEE Access  
INDEX TERMS Artificial intelligence, machine learning, network management, software-defined networking.  ...  This paper is necessary and helpful for researchers from different fields to accurately master the key issues.  ...  First, a set of individual learners are generated and then combined by a specific strategy. The main ensemble learning algorithms include bagging and boosting.  ... 
doi:10.1109/access.2019.2928564 fatcat:r4ds5ot6u5a3tl4wzdvahwb7xa

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse  ...  In particular, deep learning based solutions can automatically extract features from raw data, without human expertise.  ...  Coninck et al. consider distributing deep learning over IoT for classification ap- plications [170].  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe
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