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Exploring the potential of AI-driven optimization in enhancing network performance and efficiency
2024
Magna Scientia Advanced Research and Reviews
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Leveraging artificial intelligence (AI) algorithms such as machine learning and deep learning, the study investigates how AI can revolutionize network management and operation to achieve higher levels ...
It examines how AI algorithms can analyze vast amounts of network data, identify patterns, and make data-driven decisions to optimize network configurations, routing protocols, and resource allocation ...
One application of reinforcement learning in network optimization is dynamic routing, where reinforcement learning agents learn to dynamically adjust routing protocols based on network traffic conditions ...
doi:10.30574/msarr.2024.10.1.0028
fatcat:6pmf66qcwbhvpfxsdvdzi55cka
A Machine Learning Approach to Routing
[article]
2017
arXiv
pre-print
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Can ideas and techniques from machine learning be leveraged to automatically generate "good" routing configurations? We investigate the power of data-driven routing protocols. ...
Our results suggest that applying ideas and techniques from deep reinforcement learning to this context yields high performance, motivating further research along these lines. ...
Acknowledgements We thank Marco Chiesa for providing us with the code needed to evaluate the oblivious routing scheme. We also thank the anonymous HotNets reviewers for valuable feedback. ...
arXiv:1708.03074v2
fatcat:653jvjwgxbdibohizvwrvtczdi
Comparative Analysis of Routing Schemes Based on Machine Learning
2022
Mobile Information Systems
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Machine learning-based distributed routing algorithms, in contrast to traditional mathematical model-driven distributed routing algorithms, are typically data-driven, allowing them to adapt to dynamically ...
Finally, the future development of machine learning-based intelligent routing systems is examined. ...
According to the types of machine learning methods used, the data-driven intelligent routing algorithms are largely separated into intelligent routing algorithms based on supervised learning and reinforcement ...
doi:10.1155/2022/4560072
fatcat:tqkpguszzbgnhk2tlpds3paqli
Congestion-Control Throwdown
2017
Proceedings of the 16th ACM Workshop on Hot Topics in Networks - HotNets-XVI
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by a data-driven (i.e., machine learning) approach. ...
Can ideas and techniques from machine learning (ML) be leveraged to automatically generate "good" routing configurations? We focus on the classical setting of intradomain traffic engineering. ...
ACKNOWLEDGEMENTS We thank Marco Chiesa for providing us with the code needed to evaluate the oblivious routing scheme. We also thank the anonymous HotNets reviewers for valuable feedback. ...
doi:10.1145/3152434.3152446
dblp:conf/hotnets/SchapiraW17
fatcat:n2y463xyxfgzxirwnesz6kldru
The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks
2021
Asian Business Review
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It is possible to define machine learning (ML) as a way of dealing with heterogeneous data in order to get the most out of a network without involving humans in the process or teaching it anything. ...
Machine learning (ML) is a preferred method for dealing with this kind of dynamicity. ...
MACHINE LEARNING APPLICATIONS FOR WSN AND VEHICULAR NETWORKS The vast amount of data generated and accumulated in the vehicular network makes data-driven solutions suitable for decision making and network ...
doi:10.18034/abr.v11i3.603
fatcat:zbfrwhpoafdebasnoqn4zgj46q
Keyword Index
2020
2020 2nd International Conference on Computer and Information Sciences (ICCIS)
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Datapath
DCT
DDoS
Decision Tree
Decision Tree (DT)
DEENR
Deep Convolutional Neural Network
deep learning
Deep Learning
Deep learning
Deep machine learning
Deep Neural Networks
delay
demand ...
learning
Machine learning
Machine Learning
Machine Learning (ML)
Machine learning methods
Malware
MATLAB
maximum power point tracking
Mel Frequency Cepstral
Melanoma detection
MEMS
Metaheuristics ...
doi:10.1109/iccis49240.2020.9257716
fatcat:4eljcohqozfozar3awajqvqzn4
Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis
[article]
2017
arXiv
pre-print
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Tight collaboration between experts of machine learning and manycore system design is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. ...
Such a framework will be necessary to address the rising complexity of designing large-scale manycore systems and machine learning techniques. ...
By dynamically learning which routing decisions effectively load-balances the network, we can create an efficient routing mechanism. ...
arXiv:1712.00076v1
fatcat:rqju4xmpmjetzpbwlnr6x3gbmu
Role of Machine Learning in WSN and VANETs
2021
International Journal of Electrical and Computer Engineering Research
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For such dynamicity, Machine learning (ML) approaches are considered favourable. ...
ML can be described as the process or method of self-learning without human intervention that can assist through various tools to deal with heterogeneous data to attain maximum benefits from the network ...
MACHINE LEARNING APPLICATIONS FOR WSN AND VEHICULAR NETWORKS Huge Bulk of data produced and accumulated in the vehicular network makes the data-driven methods favourable for decision making that is also ...
doi:10.53375/ijecer.2021.24
fatcat:72mtbdb3uvaepj6efg7lfz6x6q
Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies
2020
Science China Information Sciences
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, coding schemes, routing selection and resource allocation [7] . ...
With the aid of AI techniques, the optical networks can perform in a self-learning manner, with the "self-aware" of network status, the "self-adaptive" of network actions and control policies, and the ...
The traffic demands are collected with REST APIs for machine learning based routing computation. Then the traffic matrices are classified to select optimal route assignment. ...
doi:10.1007/s11432-020-2871-2
fatcat:vrxcbkn5zzcshh5a4an5hyayvm
Computational Intelligence for Internet of Things in the Big Data Era (Part I) [Guest Editorial]
2019
IEEE Computational Intelligence Magazine
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Yao et al. propose a hybrid machine learning architecture for packet routing in their paper entitled "AI Router & Network Mind: A Hybrid Machine Learning Paradigm for Packet Routing", where a distributed ...
In the first paper entitled "QoE-driven Content-Centric Caching With Deep Reinforcement Learning in Edge-Enabled IoT", X. ...
Yao et al. propose a hybrid machine learning architecture for packet routing in their paper entitled "AI Router & Network Mind: A Hybrid Machine Learning Paradigm for Packet Routing", where a distributed ...
doi:10.1109/mci.2019.2937607
fatcat:sfrdmjegnrbe3goabh3xgvqavi
Analytics and Machine Learning in Vehicle Routing Research
[article]
2021
arXiv
pre-print
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To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem ...
The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. ...
Machine learning methodologies could also be used to tackle the PSP problem as data-driven approaches. ...
arXiv:2102.10012v1
fatcat:ihs27x2qu5c55fljxuy4fhawgq
Editorial: Machine Learning and Intelligent Wireless Communications (MLICOM 2017)
2017
Journal on spesial topics in mobile networks and applications
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Driven by this demand, machine learning which offers computers the ability to learn without being explicitly programmed is widely focused. ...
In the next article with the title "Routing Algorithm with Virtual Topology Towards to Huge Numbers of LEO Mobile Satellite Network Based on SDN", the routing issues in SDN are investigated. ...
Driven by this demand, machine learning which offers computers the ability to learn without being explicitly programmed is widely focused. ...
doi:10.1007/s11036-017-0953-3
fatcat:oih534r4j5a35pglpcz75ka43y
Author Index
2021
2021 Annual Reliability and Maintainability Symposium (RAMS)
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, and Fault trees For Functional Safety" -166 Single Event Effects Analysis to improve the system safety and fault tolerance -80 Defining Operator Driven Reliability Inspection Routes through Reliability ...
breakdowns using deep reinforcement No Problem Found Framework based on Analytics and Machine Learning -20 Interstellar Mapping and Acceleration Probe (IMAP) Software FMEA -144 Integrating FMEAs, FMEDAs ...
doi:10.1109/rams48097.2021.9605726
fatcat:p2fbwldn5fa5dmsx7e5z6cfgzm
4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing
[chapter]
2022
Applications
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Comprehensive simulations have shown that the utilization of cross-layer knowledge and the prediction of future network states enable reliable and robust reinforcement learning-based routing algorithms ...
As Mobile Ad-hoc NETworks (MANETs) are not managed centrally, data needs to be routed efficiently from the sender to the receiver, whereas link losses and unnecessary hops need to be avoided. ...
An empirical analysis of used protocols in vehicular networks is provided in [119] . Recent developments in the machine learning field have also had an impact on routing algorithms. ...
doi:10.1515/9783110785982-023
fatcat:maflwf2fcncgjmm4do2rjypbja
A bibliometric and social network analysis of data-driven heuristic methods for logistics problems
2022
Journal of Industrial and Management Optimization
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Subsequently, machine learning and deep learning methods are considered to be among the most promising data-driven methodologies. ...
Before the categorization and content analysis; descriptive, bibliometric and social network analysis are carried out to identify the current state of the literature. ...
As a superset of machine learning, [38] used artificial intelligence. Beside, as a subset of machine learning, there are also some papers related with deep learning. ...
doi:10.3934/jimo.2022190
fatcat:qcf2h5bvnzcdlac6mq76ylr5ny
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