Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

Younus, Muhammad Usman; Khan, Muhammad Khurram; Anjum, Muhammad Rizwan; Afridi, Sharjeel; Arain, Zulfiqar Ali

doi:10.1109/access.2020.3046693

Cited by 31 publications

(22 citation statements)

References 32 publications

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“…In [13], DRL-based adaptive approach has been proposed for multiple tiles to minimize the decision space of rate allocation, enabling the rate adaptive algorithm for maximizing the user's QoE. Some authors [14,15] use SDN and RL for the improving the network performance.…”

Section: Ml-based Techniques For Rate Adaptationmentioning

confidence: 99%

Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video

Younus¹,

Shafi²,

Rafiq³

et al. 2022

Computers, Materials &Amp; Continua

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The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm. However, the users only focus on the part of 360-degree videos, known as a viewport. Despite the immense hype, 360-degree videos convey a loathsome side effect about viewport prediction, making viewers feel uncomfortable because user viewport needs to be pre-fetched in advance. Ideally, we can minimize the bandwidth consumption if we know what the user motion in advance. Looking into the problem definition, we propose an Encoder-Decoder based Long-Short Term Memory (LSTM) model to more accurately capture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct input to predict the future user movement. Then, this prediction model is combined with a rate adaptation approach that assigns the bitrates to various tiles for 360-degree video frames under a given network capacity. Hence, our proposed work aims to facilitate improved system performance when QoE parameters are jointly optimized. Some experiments were carried out and compared with existing work to prove the performance of the proposed model. Last but not least, the experiments implementation of our proposed work provides high user's QoE than its competitors.

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Section: Ml-based Techniques For Rate Adaptationmentioning

confidence: 99%

Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video

Younus¹,

Shafi²,

Rafiq³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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“…The early warning method does not require a strict distribution of data and has the ability to handle information omission. Comprehensive analysis of the above, using the integrated early warning method is more scientific and reasonable than the results derived from a single early warning method and can better ensure the effectiveness of the early warning results, and the government debt risk early warning model currently established in China mainly uses the linear relationship between indicators, while in fact, the early warning system is a multifaceted, nonlinear, and complex determination system [20]. Among the comprehensive early warning methods, the artificial neural network-based early warning method has the characteristics of nonlinearity, fault tolerance, and generalization ability compared with the other two early warning methods, which not only excludes the tendency of subjectivity but also can eliminate the factor of colinearity, and thus has more advantages [21].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Wireless Network Model‐Assisted Abnormal Warning Information in Government Management

Sun

2021

Journal of Sensors

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The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area is critical because anomalies may lead to a reduction in network efficiency. The origin of these anomalies may be a technical problem in a cell or a fraudulent intrusion in the network. Usually, they need to be identified and fixed as soon as possible. Therefore, in order to identify these anomalies, data-driven systems using machine learning algorithms are developed with the aim from the raw data to identify and alert the occurrence of anomalies. Unsupervised learning methods can spontaneously describe the data structure and derive network patterns, which is effective for identifying unintended anomalous behavior and detecting new types of anomalies in a timely manner. In this paper, we use different unsupervised models to analyze traffic data in wireless networks, focusing on models that analyze traffic data combined with timeline information. The factor analysis method is used to derive the results of factor analysis, obtain the three major public factors and comprehensive factor scores, and combine the results with the BP neural network model to conduct a nonlinear simulation study on local governmental debt risk. A potential semantic analysis model based on Gaussian probability is presented and compared with other methods, and experimental results show that this model can provide a robust, over-the-top anomaly detection in a fully automated, data-driven solution.

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“…To prolong the NL of the SDWSN, an RL approach that trains the SDN controller to optimise the routing paths is proposed in [141]. The controller gets the rewards in terms of estimated path lifetime loss.…”

Section: Energy Efficiencymentioning

confidence: 99%

A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

Lasso¹,

Marchegiani²,

Jurado³

et al. 2021

Preprint

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This paper is aimed to present a comprehensive survey of relevant research over the period 2012-2021 of Software-Defined Wireless Sensor Network (SDWSN) proposals and Machine Learning (ML) techniques to perform network management, policy enforcement, and network configuration functions. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSNs, mainly the current state-of-art, machine learning techniques, and open issues.

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Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

Cited by 31 publications

References 32 publications

Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video

Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video

Unsupervised Wireless Network Model‐Assisted Abnormal Warning Information in Government Management

A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

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