Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

Wu, Shujian; Mao, Wei; Liu, Cong; Tang, Tao

doi:10.1155/2019/4687272

Cited by 14 publications

(10 citation statements)

References 26 publications

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“…The rapid growth of network traffic leads to enormous research contributions based on prediction of the network traffic, but the Internet of Things requires improved and efficient approaches to handle huge and dynamic data as shown in some recent study [2,7]. We have surveyed the different techniques used in network traffic prediction in this section to provide insights into the research contributions made by others in the area of our research work.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

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Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Khedkar

Ramalingam

Najafi

2021

Wireless Communications and Mobile Computing

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An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

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Section: Related Work and Research Gapmentioning

confidence: 99%

“…A precise traffic prediction is needed to speed up these nodes and to maximize energy savings. With rising requirements for demands in traffic and computational needs, power consumption is increased [7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Khedkar

Ramalingam

Najafi

2021

Wireless Communications and Mobile Computing

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“…The solutions to these issues were presented in several papers. For instance, in [22], the authors improved the call session control function server by adding traffic prediction, bandwidth negotiation and connection admission control to improve traffic management and increase the accuracy of traffic forecasting without sacrificing the prediction accuracy of the system. For ideal large-data streaming in 5G HetNets, an improved version of traditional traffic prediction was proposed by [23].…”

Section: A Heterogeneous Networkmentioning

confidence: 99%

5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

et al. 2020

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The explosive popularity of small-cell and Internet of Everything devices has tremendously increased traffic loads. This increase has revolutionised the current network into 5G technology, which demands increased capacity, high data rate and ultra-low latency. Two of the research focus areas for meeting these demands are exploring the spectrum resource and maximising the utilisation of its bands. However, the scarcity of the spectrum resource creates a serious challenge in achieving an efficient management scheme. This work aims to conduct an in-depth survey on recent spectrum sharing (SS) technologies towards 5G development and recent 5G-enabling technologies. SS techniques are classified, and SS surveys and related studies on SS techniques relevant to 5G networks are reviewed. The surveys and studies are categorised into one of the main SS techniques on the basis of network architecture, spectrum allocation behaviour and spectrum access method. Moreover, a detailed survey on cognitive radio (CR) technology in SS related to 5G implementation is performed. For a complete survey, discussions are conducted on the issues and challenges in the current implementation of SS and CR, and the means to support efficient 5G advancement are provided.

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“…One of the steps, directed to М2М traffic maintenance methods upgrading, is constructing the model, which would allow fulfill traffic prediction in short-time period and match the majority of modern mass applications of М2М devices. There are known models, making use of neuron network technologies apparatus [9], theory of Markoff processes [8], statistical modeling methods [8], etc. Universal model, which could be used for М2М any type forecasting has not been designed by the time being.…”

Section: Thematic Scope: Publication Of Papers On Geology and Technical Sciencesmentioning

confidence: 99%

Statistical Analysis of Real Traffic of Machine-to-Machine Communication (M2m)

Mukhamejanova¹,

Tumanbayeva²,

Lechshinskaya³

et al. 2021

News of NAN RK. SGTS

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The development of digital technology has spawned the concept of the Internet of Things (IoT). The concept basis is the machine-to-machine interaction (M2M) technology, which allows devices to exchange information. The most effective data transmission medium for M2M devices is mobile communications. Rapid growth of machine-to-machine М2М traffic in mobile communication network defines the actuality of the research problem, its features and characteristics. Research outcomes are indispensable at the network modeling, planning, analyzing the М2М traffic impact at quality of service (QoS) of mobile network communication. The article analyzes the real traffic in the LoraWan network. Aggregated traffic coming to the network server from all devices is considered. To model the М2М batch traffic, apart from specifying the statistic characteristics it is necessary to assess its self-similarity. In order to define the traffic self-similarity there has been computed Hurst parameter. On the basis of STATISTICA programs batch we have conducted statistical analysis and short-term forecasting of real М2М traffic by method of exponential smoothing.

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Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

Cited by 14 publications

References 26 publications

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

Statistical Analysis of Real Traffic of Machine-to-Machine Communication (M2m)

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