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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
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

Dejan Dasic, Miljan Vucetic, Nemanja Ilic, Milos Stankovic, Marko Beko
2021 Serbian Journal of Electrical Engineering  
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

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 IEEE Communications Surveys and Tutorials  
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]

Javad Hassannataj Joloudari, Roohallah Alizadehsani, Issa Nodehi, Sanaz Mojrian, Fatemeh Fazl, Sahar Khanjani Shirkharkolaie, H M Dipu Kabir, Ru-San Tan, U Rajendra Acharya
2022 arXiv   pre-print
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

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  
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

Georgios Iosifidis, Danny De Vleeschauwer, Chia-Yu Chang, Marco Fiore, Sergi Alcalá, Andres Garcia-Saavedra, Gines Garcia, Ivan Paez, Gabriele Baldoni, Andra Lutu, Miguel Camelo, Nina Slamnik-Krijestorac (+10 others)
2021 Zenodo  
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]

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

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

Thang Le Duc, Rafael García Leiva, Paolo Casari, Per-Olov Östberg
2019 ACM Computing Surveys  
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

Hong-Ning Dai, Raymond Chi-Wing Wong, Hao Wang, Zibin Zheng, Athanasios V. Vasilakos
2019 ACM Computing Surveys  
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

ASSIST-IoT Consortium
2021 Zenodo  
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

Alexandros Kaloxylos, Anastasius Gavras, Daniel Camps Mur, Mir Ghoraishi, Halid Hrasnica
2020 Zenodo  
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]

Chaoyun Zhang
2020 arXiv   pre-print
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

Tom Goethals, Bruno Volckaert, Filip De Turck
2021 IEEE Open Journal of the Communications Society  
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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