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Federated Learning for Internet of Things: A Comprehensive Survey
[article]
2021
arXiv
pre-print
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In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration ...
Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing ...
In fact, FL is very useful to build proactive data caching schemes in edge computing without the need for direct access to user data [66] . ...
arXiv:2104.07914v1
fatcat:b5wsrfcbynel7jqdxpfw4ftwh4
Application of deep learning algorithms and architectures in the new generation of mobile networks
2021
Serbian Journal of Electrical Engineering
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Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks. ...
Operators of modern mobile networks are faced with significant challenges in providing the requested level of service to an ever increasing number of user entities. ...
DRL alone has seen implementations in applications of network access and adaptive rate control, proactive caching and data offloading, network security and connectivity preservation, traffic routing, resource ...
doi:10.2298/sjee2103397d
fatcat:n3hduljspfbt3mkq2zdzbae72u
Federated Learning for Internet of Things: A Comprehensive Survey
2021
IEEE Communications Surveys and Tutorials
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In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration ...
Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing ...
In fact, FL is very useful to build proactive data caching schemes in edge computing without the need for direct access to user data [66] . ...
doi:10.1109/comst.2021.3075439
fatcat:ycq2zydqrzhibfqyo4vzloeoqy
Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review
[article]
2022
arXiv
pre-print
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The reviewed ML-based approaches are categorized as supervised and reinforcement learning (RL). Moreover, DL-based approaches and their combination with RL are surveyed. ...
This paper presents a comprehensive literature review on the application of artificial intelligence (AI) methods such as deep learning (DL) and machine learning (ML) for resource allocation optimization ...
caching with popularity [106] , distributed caching with least recently used [107] , and no-cache N/A Layered fog radio access network DQN Fang [108] Used LSTM in an integrated resource prediction ...
arXiv:2203.12315v1
fatcat:43mouwxwene6xllnw3gsmdh6hy
Machine Learning Meets Communication Networks: Current Trends and Future Challenges
2020
IEEE Access
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Beginning from the physical layer, the use of ML in MAC and network layers, and in technologies such as SDN, NFV, and MEC is described. ...
INDEX TERMS Communication networks, machine learning, physical layer, MAC layer, network layer, SDN, NFV, MEC, security, artificial intelligence (AI). I. ...
The article focuses on the potential of big data analytics along with methods and technologies for proactive network optimization using machine learning in future networks. ...
doi:10.1109/access.2020.3041765
fatcat:erbcetvcrjabrl4qloow3dqcai
DAEMON Deliverable 4.1: Initial design of intelligent orchestration and management mechanisms
2021
Zenodo
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NI-assisted network functionalities for B5G systems.) and several specific Key Performance Indicators (KPIs) of objective 4 (Demonstrating the viability and performance of NI-native B5G networks). ...
In essence, the activities in WP4 will be based on the architecture defined in WP2 to not only ensure the developed NI solutions are aligned with the specific needs of mobile network systems, but also ...
Finally, the work [168] proposed a mobile proactive caching scheme, using again RL, where the caches are deployed at the mobile users' equipment, not at edge servers as in our model. ...
doi:10.5281/zenodo.5745456
fatcat:isg5vbmabnecblzd7d3536cjya
Deep Learning in Mobile and Wireless Networking: A Survey
[article]
2019
arXiv
pre-print
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We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. ...
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. ...
Wei et al. address user scheduling and content caching simultaneously [370] . ...
arXiv:1803.04311v3
fatcat:awuvyviarvbr5kd5ilqndpfsde
Deep Learning in Mobile and Wireless Networking: A Survey
2019
IEEE Communications Surveys and Tutorials
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We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. ...
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. ...
Wei et al. address user scheduling and content caching simultaneously [373] . ...
doi:10.1109/comst.2019.2904897
fatcat:xmmrndjbsfdetpa5ef5e3v4xda
2020 Index IEEE Transactions on Vehicular Technology Vol. 69
2020
IEEE Transactions on Vehicular Technology
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Revocable Data-Sharing Scheme in VANETs; TVT Dec. 2020 15933-15946 Hoseini, S.A., Ding, M., Hassan, M., and Chen, Y., Analyzing the Impact of Molecular Re-Radiation on the MIMO Capacity in High-Frequency ...
, C.S., see Le, T.H.T., TVT Dec. 2020 15162-15176 Hong, D., Lee, S., Cho, Y.H., Baek, D., Kim, J., and Chang, N Guo, H., Liu, J., and Zhang, Y., Toward Swarm Coordination: Topol-ogy-Aware Inter-UAV ...
., +, TVT Nov. 2020 12897-12911 Cooperative Caching and Transmission in CoMP-Integrated Cellular Networks Using Reinforcement Learning. ...
doi:10.1109/tvt.2021.3055470
fatcat:536l4pgnufhixneoa3a3dibdma
Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing
2019
ACM Computing Surveys
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By exploiting such data, the devices and applications are able to bring context-aware services to users. ...
Besides an extension in the utilization of network infrastructure, this also provides optimizations in replicating information across networks to enhance local context awareness. ...
-88749-R) and the Comunidad de Madrid grant EdgeData-CM (P2018/TSC-4499). ...
doi:10.1145/3341145
fatcat:vkzofhgipfhqtm2z2w5swivksy
Big Data Analytics for Large-scale Wireless Networks
2019
ACM Computing Surveys
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2019 pre-print arXiv:1909.08069v1 fatcat:s2sgrqbxencudm3wcmdpkebbcy
The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. ...
Moreover, we discuss the open research issues and outline the future directions in this promising area. ...
ACKNOWLEDGEMENT The work described in this paper was partially supported by Macao Science and Technology Development Fund under Grant No. 0026/2018/A1. The authors would like to thank Gordon K.-T. ...
doi:10.1145/3337065
fatcat:vjjoymozrzb6pkchdp36dh6lze
D3.1 – State-of-the-Art and Market Analysis Report
2021
Zenodo
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Document with main results of SotA review and stakeholders and market analysis carried out. ...
and flexible IoT resource discovery by using meta-data and resource descriptions in a dynamic data model. ...
self-awareness and network-level self-awareness. ...
doi:10.5281/zenodo.6705158
fatcat:xote6pjzubcvxo4aqxxbraooxi
AI and ML – Enablers for Beyond 5G Networks
2020
Zenodo
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In network diagnostics, attention is given to forecasting network conditions, characteristics and undesired events, such as security incidents. Estimating user location is part of network insights. ...
In the sequel the white paper elaborates on use case and optimisation problems that are being tackled with AI/ML, partitioned in three major areas, namely: network planning, network diagnostics/insights ...
Also in this case, several algorithms can be used, including Seasonal ARIMA and LSTM networks. ...
doi:10.5281/zenodo.4299895
fatcat:ngzbopfm6bb43lnrmep6nz5icm
Deep Neural Mobile Networking
[article]
2020
arXiv
pre-print
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in mobile networks. ...
This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering ...
In order to assess the flexibility of MICROSCOPE in serving heterogeneous edge and core network scenarios, we consider three use cases: (i) fifty MEC facilities deployed at the edge, aggregating traffic ...
arXiv:2011.05267v1
fatcat:yz2zp5hplzfy7h5kptmho7mbhe
Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions
2021
IEEE Open Journal of the Communications Society
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The use of AI in Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. ...
" of the edge using AI, and AI "Applications" in the edge as its main topics. ...
The research in this paper has been funded by Vlaio by means of the FLEXNET research project. ...
doi:10.1109/ojcoms.2021.3116437
fatcat:knvl27fcwrarjhhua7zo475lwy
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