Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence

Ai-peng, Guo; Yuan, Chunhui

doi:10.3390/electronics10060700

Cited by 32 publications

(18 citation statements)

References 30 publications

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“…Model predictive control with other optimization control algorithm: model predictive control for the cost function is to optimize the real time updated in each moment will be predicted, which is often said that it will roll forward in time domain of a time step, so the model predictive control is also known as rolling time domain control. Compared with traditional control methods, the special characteristics of model predictive control can be summarized as four points [ 16 ]. Finally, model predictive control has the ability to explicitly deal with all kinds of constraints of the system.…”

Section: Related Workmentioning

confidence: 99%

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

Zhou

Zhang

2022

Computational Intelligence and Neuroscience

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By the method of documentation and logical analysis, based on the data, based on logic and based on the knowledge of three kinds of artificial intelligence in the sports education, the intelligent learning system feedback delay are studied, combined with mobile communication which led to the artificial intelligence online sports games teaching, pattern recognition, and virtual technology combined with innovative teaching interaction and experience. Promoting the development of green PE teaching machine learning can identify the types of PE activities and realize efficient PE learning diagnosis. Intelligent decision support system can identify sports talents and improve the effect of personalized PE teaching evaluation. From the perspective of psychological development and education, the key problems to be solved in the integration of artificial intelligence and physical education are examined. Then, the consistent model predictive control for feedback delay of nonlinear sports learning multiagent system with network induced delay and random communication protocol is studied. Under the communication waiting mechanism designed, each agent has a certain tolerance of delay, and this tolerance can be determined by ensuring the stability of the system. At the same time, a random communication protocol is designed to ensure the ordered communication of the multiagent system. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation. To solve the channel competition access problem of the sports intelligent learning system with special structure feedback delay model predictive control, a dual channel awareness scheduling strategy under the model predictive control framework was proposed, and the distributed threshold strategy of sensors and the priority threshold strategy of controllers were designed. It is proved that the sensor will eventually work at Nash equilibrium point under the policy updating mechanism, and the priority threshold strategy of the controller is better than the traditional independent and identically distributed access strategy. By avoiding the data transmission when the channel status is poor, the channel access of the system is efficient and saves energy.

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Section: Related Workmentioning

confidence: 99%

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

Zhou

Zhang

2022

Computational Intelligence and Neuroscience

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“…Method Year [51] Online intelligent English teaching [52] An English network teaching [53] Perceptions on the impact of AI on English language learning [54] Chatbots in education [55] Network intelligent control and traffic optimization [4] Educational technology in English major college teaching [2] Acceptance determinants of 5G services [13] Chatbots applications in education [19] Task-oriented chatbot system [17] Chatbots in higher education [31] Human-computer interaction on IoT-based college physical education [43] College English teaching innovation under AI [33] Machine learning and neural network in English learning [39] Educational robot teaching resources using AI [40] College English teaching mode based on AI [47] Vocabulary intelligent tutoring system for second-grade latinx English learners [56] Understanding students' perceptions of AI teaching [57] AI-enabled routing in software-defined networking [1] Innovation in university education [5] Mobile-assisted language learning [7] Adult learners in distance education [12] Chatbots [20] Conversational agents for learning foreign languages [21] AI-based pronunciation checker [26] Prediction of learning styles in adaptive e-learning [30] Eco-environment construction of English teaching using AI [3] e strategy of college English mode based on 5G and AI [35] Chatbot using dialogflow [36] Preservice primary English teachers' AI chatbots [58] AI chatbot as a language learning medium [28] College English culture intelligence-aided teaching [49] AI in EFL context [50] AI in education [59] AI writing evaluation system in China CET…”

Section: Reference Nomentioning

confidence: 99%

Role of 5G and Artificial Intelligence for Research and Transformation of English Situational Teaching in Higher Studies

Yu¹,

Nazir

2021

Mobile Information Systems

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We live in a modern and technological society run by intelligent and human-like machines and systems. This is due to the advancements in the field of artificial intelligence. The machines are directly or indirectly used in different sectors like healthcare, automatic vehicles, and complex decision-making and at the same used in educational institutes. The usage of AI-based systems and the Internet has brought numerous educational innovations for both teachers and students. With the online learning platforms grounded on AI techniques, 5G has revolutionized the teaching and learning methods by smooth and faster access to educational content. Students of foreign languages, especially English learners, can now use chatbots and intelligent tutoring systems to learn and practice their speaking and listening skills offline and online. With Computer-Assisted Language Learning (CALL), the English learning process can now be interactive and productive. The students can now improve their language skills by conversing with AI-based agents instead of native speakers to avoid any fear and anxiety. The intelligent platforms can understand the consuming power of the student and hence can create and give content according to their level to create an individualized learning environment. With the help of digital assistants, people can also find it very easy and productive to improve English proficiency. To accomplish the goal of English teaching very easily and ideally, the teachers should use AI-based techniques in the classrooms. With the help of intelligent assistants for the daily workload of a teacher, we will be able to concentrate fully on the language learning and skills of the students. The current study has presented a detailed overview of 5G and AI’s role in research and transformation of English situational teaching in higher studies. The search results are compiled and presented with different details of the area.

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“…Fifth-generation (5G) [1] networking is becoming one of the most active research topics and attracting interest. The broad adoption of 5G [2] technology, the continuous growth of cellular connections, and the convergence of new network techniques such as big data, the Internet of Things (IoT) [3], and cloud computing have supported the overall economic and social development. 5G [4] data centers network have emerged as a critical component of modern computing environments.…”

Section: Introductionmentioning

confidence: 99%

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

Aqdus¹,

Amin²,

Ramzan³

et al. 2023

Computers, Materials &Amp; Continua

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The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane's forwarding capabilities. So, the SDN must handle traffic from across the whole network. The flow table depends on Ternary Content Addressable Memorable Memory (TCAM) for storing and a quick search of regulations; it is restricted in capacity owing to its elevated cost and energy consumption. Whenever the flow table is abused and overflowing, the usual regulations cannot be executed quickly. In this case, we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don't fit the flow table at a low rate. This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN, using Feed Forward Neural Network (FFNN), K-Means, and Decision Tree (DT). We generate two network topologies, Fat Tree and Simple Tree Topologies, with the Mininet simulator and coupled to the OpenDayLight (ODL) controller. The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query, propagation delay, overall dropped packets, energy consumption, bandwidth usage, latency rate, and throughput. The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network. By putting the proposed flow method and checking whether a packet may move 1414

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Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence

Cited by 32 publications

References 30 publications

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

Role of 5G and Artificial Intelligence for Research and Transformation of English Situational Teaching in Higher Studies

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

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