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A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks [article]

Bo Yang, Omobayode Fagbohungbe, Xuelin Cao, Chau Yuen, Lijun Qian, Dusit Niyato, Yan Zhang
2021 arXiv   pre-print
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.  ...  Due to the energy budget of the devices and the limited communication bandwidth, a joint energy and latency problem is formulated, which is solved by decomposing the original problem into an uploading  ...  CONCLUDING REMARKS In this article, we developed a TL-empowered edge-CNN framework enabling the image classification with privacy-preserving over 5G IIoT edge networks.  ... 
arXiv:2104.09382v1 fatcat:j44g5rufi5dvtapr3bhr45o4vy

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

Lilian C. Mutalemwa, Seokjoo Shin
2020 IEEE Access  
Subsequently, an optimal coded caching solution for joint optimization of delivery latency and energy consumption was proposed for ICN-based 5G D2D networks.  ...  The work in [79] focused on the use of a FL framework to design a low-latency multi-access scheme for edge learning.  ... 
doi:10.1109/access.2020.3037357 fatcat:khkeuvo6ujanhp42y2k372n5u4

Six Key Enablers for Machine Type Communication in 6G [article]

Nurul Huda Mahmood, Hirley Alves, Onel Alcaraz López, Mohammad Shehab, Diana P. Moya Osorio, Matti Latva-aho
2019 arXiv   pre-print
This article presents an over-arching vision for machine type communication in 6G.  ...  Driven by impetus to provide vertical-specific wireless network solutions, machine type communication encompassing both its mission critical and massive connectivity aspects is foreseen to be an important  ...  In this sense, introducing new metrics, such as effective capacity (EC) and effective energy efficiency (EEE) provides a robust framework for joint WIT and WET optimization.  ... 
arXiv:1903.05406v1 fatcat:wsrguijsfjbe7p5lf6a3ibr5lm

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Quoc-Viet Pham, Fang Fang, Vu Nguyen Ha, Md. Jalil Piran, Mai Le, Long Bao Le, Won-Joo Hwang, Zhiguo Ding
2020 IEEE Access  
MEC WITH ENERGY HARVESTING AND WIRELESS POWER TRANSFER A.  ...  For example, the work in [56] showed that cognitive radio edge computing can well support low-latency and computeintensive industrial applications.  ... 
doi:10.1109/access.2020.3001277 fatcat:isme7xwfejf4pfnowosljxnzja

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art [article]

Quoc-Viet Pham, Fang Fang, Vu Nguyen Ha, Md. Jalil Piran, Mai Le, Long Bao Le, Won-Joo Hwang, Zhiguo Ding
2020 arXiv   pre-print
The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities.  ...  We also summarize testbeds and experimental evaluations, and open source activities, for edge computing.  ...  MEC WITH ENERGY HARVESTING AND WIRELESS POWER TRANSFER A.  ... 
arXiv:1906.08452v2 fatcat:krti5kagxzeqte7osdpxs4lxoe

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

Afra Domeke, Bruno Cimoli, Idelfonso Tafur Monroy
2022 Applied Sciences  
Furthermore, to reveal the benefits of these systems further, a novel framework based on reinforcement learning for controller synchronization in distributed edge sliced networks is proposed.  ...  As the integration of two technologies delivers network capabilities more efficiently and effectively, this paper provides a comprehensive study on edge-enabled network slicing frameworks and potential  ...  [27] introduced a 5G enabled platform for vertical automotive industry.  ... 
doi:10.3390/app12136617 fatcat:3beb3r3fbnc27n56dc43ezh244

5G support for Industrial IoT Applications – Challenges, Solutions, and Research gaps

Pal Varga, Jozsef Peto, Attila Franko, David Balla, David Haja, Ferenc Janky, Gabor Soos, Daniel Ficzere, Markosz Maliosz, Laszlo Toka
2020 Sensors  
support for 5G, and private campus networks.  ...  These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios.  ...  Acknowledgments: The authors would like to thank the PhD course entitled "5G Networks, Services and their Synergies with Industrial IoT" that has conceived many ideas and collaboration related to this  ... 
doi:10.3390/s20030828 pmid:32033076 pmcid:PMC7038716 fatcat:cbsr2pao5bffxooaod3l3rlbfe

Industrial Digital Twins at the Nexus of NextG Wireless Networks and Computational Intelligence: A Survey [article]

Shah Zeb, Aamir Mahmood, Syed Ali Hassan, MD. Jalil Piran, Mikael Gidlund, Mohsen Guizani
2021 arXiv   pre-print
, age of information, federated learning, data analytics), and other promising trends in networked computing (e.g., edge and cloud computing).  ...  DT is rapidly diffusing in the industries with recent advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics  ...  Main ideas: a) a part of CDT is shifted to the edge layer to make local learning and make decisions quickly (federated learning), b) EDTs are developed at the edge layer, i.e., at 5G gNodeB (gNB) or edge  ... 
arXiv:2108.04465v1 fatcat:gbf365ldgbdcnfdbiecirm454q

Guest Editorial: Special Issue on Blockchain and Edge Computing Techniques for Emerging IoT Applications

Victor C. M. Leung, Xiaofei Wang, F. Richard Yu, Dusit Niyato, Tarik Taleb, Sangheon Pack
2021 IEEE Internet of Things Journal  
Focusing on the research of social-aware cloud computing, cooperative cell caching, and mobile traffic offloading, he has authored over 100 technical papers in  ...  intelligent ultra-dense edge computing (I-UDEC) framework in 5G ultra-dense network environments and a novel two-timescale deep reinforcement learning (2Ts-DRL) algorithm to achieve real-time and low overhead  ...  Hassija et al., in "A blockchain and edge-computing-based secure framework for government tender allocation," exploit a secure and transparent framework for government tenders.  ... 
doi:10.1109/jiot.2021.3050050 fatcat:rux57gjppjdqla556myxnvp4ve

Guest Editorial: IEEE TC Special Issue On Smart Edge Computing and IoT

Luca Benini, Simone Benatti, Taekwang Jang, Abbas Rahimi
2021 IEEE transactions on computers  
Deep learning inference requires a massive amount of MAC operations and high-bandwidth data transfers, which are critical in digital architectures, especially in those that are used in edge devices.  ...  For this reason, both architectural optimizations and neural network performance tuning and analysis tools are important tools for the evolution of next generation edge devices.  ...  The paper entitled "Design and Simulation of a Hybrid Architecture for Edge Computing in 5G and Beyond" by Rahimi et al. explores computing techniques and architectural frameworks for Edge Computing in  ... 
doi:10.1109/tc.2021.3082675 fatcat:ffx3cnnozbbivf5zokiil62irq

Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

Chamitha de Alwis, Anshuman Kalla, Quoc-Viet Pham, Pardeep Kumar, Kapal Dev, Won-Joo Hwang, Madhusanka Liyanage
2021 IEEE Open Journal of the Communications Society  
industry verticals, Over-The-Top (OTT) providers, and edge providers [238] , [243] .  ...  [216] proposed a FL framework for IoT networks with the aim to simultaneously maximize the utilization efficiency of edge resources and minimize the cost for IoT networks. Kang et al.  ... 
doi:10.1109/ojcoms.2021.3071496 fatcat:lpu2cwh6xfebjpbt56svuwxoti

Table of Contents

2020 IEEE Transactions on Vehicular Technology  
Huang, and H. Jiang 11728 A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback . . . . . . . . . . . . . . A. M.  ...  Xie, and Y. Zhang 12229 Reducing Offloading Latency for Digital Twin Edge Networks in 6G . . . . . . . . .W. Sun, H. Zhang, R. Wang, and Y.  ... 
doi:10.1109/tvt.2020.3027076 fatcat:xns72myvtfhgpjy622lpgmii3i

Survey on Multi-Access Edge Computing for Internet of Things Realization

Pawani Porambage, Jude Okwuibe, Madhusanka Liyanage, Mika Ylianttila, Tarik Taleb
2018 IEEE Communications Surveys and Tutorials  
IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge.  ...  The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency  ...  The EU H2020 funded 5G-PPP Phase 2 project, MATILDA [204] , aims to design and implement a holistic 5G framework for the design, development and orchestration of 5G-ready applications and 5G network services  ... 
doi:10.1109/comst.2018.2849509 fatcat:fnksr3dbqfakzfvnopyyxjdlfm

Toward 6G Network: Requirements, Key Technologies, and Challenges in Future Research

Ahmed Shamil Mustafa, Adib Habbala
2021 Zenodo  
The 5G network technology has recently become widely available, and there are numerous 5G operators in many countries. It's past time for academics and businesses to focus on the 6G mobile network.  ...  Future wireless networking occurs over long distances and across large areas of the globe.  ...  namely wireless power transfer and energy harvesting.  ... 
doi:10.5281/zenodo.4943210 fatcat:cckx75rti5dfrpohcuxii3rjgy

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [article]

Khaled B. Letaief, Yuanming Shi, Jianmin Lu, Jianhua Lu
2021 arXiv   pre-print
However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion  ...  By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence  ...  For instance, over-the-air federated learning (FL) provides a collaborative ML framework to train a global statistical model over wireless networks without accessing edge devices' private raw data [31  ... 
arXiv:2111.12444v1 fatcat:crrbtfylvjeihogumggdnxcbpq
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