Jitter Buffer Control Algorithm and Simulation Based on Network Traffic Prediction

Yuan, Minglan

doi:10.1007/s10776-019-00429-8

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…Particle filter algorithm [1,2] is an online nonlinear identification algorithm based on Bayesian estimation and Monte Carlo method. Its essence is to approximate the state probability density function by finding a set of random samples propagating in the state space.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

Hui

2020

Complexity

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As one of the most important communication tools for human beings, English pronunciation not only conveys literal information but also conveys emotion through the change of tone. Based on the standard particle filtering algorithm, an improved auxiliary traceless particle filtering algorithm is proposed. In importance sampling, based on the latest observation information, the unscented Kalman filter method is used to calculate each particle estimate to improve the accuracy of particle nonlinear transformation estimation; during the resampling process, auxiliary factors are introduced to modify the particle weights to enrich the diversity of particles and weaken particle degradation. The improved particle filter algorithm was used for online parameter identification and compared with the standard particle filter algorithm, extended Kalman particle filter algorithm, and traceless particle filter algorithm for parameter identification accuracy and calculation efficiency. The topic model is used to extract the semantic space vector representation of English phonetic text and to sequentially predict the emotional information of different scales at the chapter level, paragraph level, and sentence level. The system has reasonable recognition ability for general speech, and the improved particle filter algorithm evaluation method is further used to optimize the defect of the English speech rationality and high recognition error rate Related experiments have verified the effectiveness of the method.

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Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

Hui

2020

Complexity

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“…The second approach introduces an optimization algorithm into the model to improve prediction. These models include particle swarm optimization and Elman neural networks [22]. The third approach introduces an algorithm to decompose the network traffic and then selects the appropriate prediction model for each obtained component.…”

Section: Research Statusmentioning

confidence: 99%

Network traffic prediction model based on linear and nonlinear model combination

Lian

2023

ETRI Journal

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We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

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“…Figure 13 shows the jitter of the E2E delay. Jitter delay refers to rapid variations in the delay at which a packet travels through a network [39]. It shows a deviation indicating how constant the delay is until the packet arrives at the destination.…”

Section: B Efficient and Performance Analysis 1) Group Management Ana...mentioning

confidence: 99%

Hierarchical Blockchain-Based Group and Group Key Management Scheme Exploiting Unmanned Aerial Vehicles for Urban Computing

2022

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In urban computing composed of various Internet of Things (IoT) devices, data collected from IoT as learning data for other IoT devices in a circular structure. Therefore, data are shared resources and crucial in urban computing. In particular, group communication is essential for effective data sharing and to updating software in an IoT system. However, changing the group key is imperative for data security in group communication whenever the group member changes. When the movement of IoTs is high, group key redistribution must be repeated because the group membership keeps changing. In changing and redistributing the group keys, overhead occurs, inevitably degrading system performance. In addition, since much data traffic generates in IoT devices' crowded areas, transmission delay and data loss could degrade system performance. Due to IoT's low power and low capacity characteristics, one-to-one communication is inefficient, requiring efficient group management and group key management. This work proposes a hierarchical blockchain-based group and group key management scheme to establish an efficient communication environment in urban computing. We adopted blockchains to track the movement and density of IoT and secure node authorization. Using the upper layer blockchain, the unmanned aerial vehicle (UAV) determines the movement and density of IoTs. Using the lower-layer blockchain, base stations (BSs) identify the IoT's movement of information in each group. We included only nodes determined to be safe in the group. Through the hierarchical blockchain, while protecting the IoT's privacy, we can record the information in the blockchain to determine the mobility of nodes and the node density of a group. We set several experimental environments, and analyzed the efficiency by simulating the addition of secondary gateways and group integration. As a result, we showed that our proposal establishes an efficient communication environment and reduces computational and communication overhead.INDEX TERMS group management, group key management, urban computing, internet of things, blockchain, unmanned aerial vehicle, data security, privacy

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Jitter Buffer Control Algorithm and Simulation Based on Network Traffic Prediction

Cited by 8 publications

References 20 publications

Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

Network traffic prediction model based on linear and nonlinear model combination

Hierarchical Blockchain-Based Group and Group Key Management Scheme Exploiting Unmanned Aerial Vehicles for Urban Computing

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