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

Khedkar, Shilpa; Ramalingam, A.; Najafi, Moslem Lari

doi:10.1155/2021/5366222

Cited by 22 publications

(13 citation statements)

References 27 publications

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“…Therefore, continuous forecasting of traffic is required. AI & ML techniques such as Long Short Term Memory (LSTM) [132] and regression models (Decision Tree Regression, Gradient Boosted Regression Tree, K-Nearest Neighbour Regression, Support Vector Regression etc.) [133] are promising tools to understand time dependencies and accurately forecast/estimate user requirements.…”

Section: Traffic Prediction At the Edgementioning

confidence: 99%

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

Zia

Chiumento

Havinga

2022

IEEE Open J. Commun. Soc.

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Wireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is a complex and challenging task. On top of including new technologies and wireless standards, one solution is to deploy cross-layer Design (CLD) and multiple Radio Access Technologies (Multi-RAT) to develop scalable QoS-aware IoT networks. However, the complexity of such solutions is high as it involves complex inter-layer interactions and dependencies and inter-application dependencies in multi-RAT networks. Moreover, the wireless environment is very dynamic, so having an optimal constellation of parameters is a challenging task. In this paper, we address the possibilities of using Artificial Intelligence (AI) and Machine Learning (ML) to address these high dimensional and dynamic problems. Based on our findings, we have proposed a distributed network management framework employing AI & ML for studying inter-layer dependencies and developing cross-layer design, traffic classification and traffic prediction at the edge devices for reliable QoS in multi-RAT IoT networks. A thorough discussion on future directions and emerging challenges related to our proposed framework has also been provided for further research in this field.INDEX TERMS QoS in IoT networks, AI & ML for cross-layer design, cross-layer optimization, reliable QoS, multi-RAT networks, edge intelligence.

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Section: Traffic Prediction At the Edgementioning

confidence: 99%

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

Zia

Chiumento

Havinga

2022

IEEE Open J. Commun. Soc.

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“…The work [8] provides a complete overview of the IoT traffic forecasting model using classical time series and an artificial neural network. Real network traces are used to predict IoT traffic.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Application of the NARX neural network for predicting a one-dimensional time series

Serikov

Zhetpisbayeva

Mirzakulova

et al. 2021

EEJET

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Time series data analysis and forecasting tool for studying the data on the use of network traffic is very important to provide acceptable and good quality network services, including network monitoring, resource management, and threat detection. More and more, the behavior of network traffic is described by the theory of deterministic chaos. The traffic of a modern network has a complex structure, an uneven rate of packet arrival for service by network devices. Predicting network traffic is still an important task, as forecast data provide the necessary information to solve the problem of managing network flows. Numerous studies of actually measured data confirm that they are nonstationary and their structure is multicomponent. This paper presents modeling using Nonlinear Autoregression Exogenous (NARX) algorithm for predicting network traffic datasets. NARX is one of the models that can be used to demonstrate non-linear systems, especially in modeling time series datasets. In other words, they called the categories of dynamic feedback networks covering several layers of the network. An artificial neural network (ANN) was developed, trained and tested using the LM learning algorithm (Levenberg-Macwardt). The initial data for the prediction is the actual measured network traffic of the packet rate. As a result of the study of the initial data, the best value of the smallest mean-square error MSE (Mean Squared Error) was obtained with the epoch value equal to 18. As for the regression R, its output ANN values in relation to the target for training, validation and testing were 0.97743. 0.9638 and 0.94907, respectively, with an overall regression value of 0.97134, which ensures that all datasets match exactly. Experimental results (MSE, R) have proven the method's ability to accurately estimate and predict network traffic

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“…The extremely large number of network nodes inevitably leads to the appearance of typical structures of various scales [11]. For example, user service structures in the area of operation of the base station, base station service structures, micro, mini, and macro cloud services organization structures, transport network organization structures, and others [12].…”

Section: Introductionmentioning

confidence: 99%

Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

Nahli¹,

Paramonov²,

Soliman³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Next-generation networks, including the Internet of Things (IoT), fifthgeneration cellular systems (5G), and sixth-generation cellular systems (6G), suffer from the dramatic increase of the number of deployed devices. This puts high constraints and challenges on the design of such networks. Structural changing of the network is one of such challenges that affect the network performance, including the required quality of service (QoS). The fractal dimension (FD) is considered one of the main indicators used to represent the structure of the communication network. To this end, this work analyzes the FD of the network and its use for telecommunication networks investigation and planning. The cluster growing method for assessing the FD is introduced and analyzed. The article proposes a novel method for estimating the FD of a communication network, based on assessing the network's connectivity, by searching for the shortest routes. Unlike the cluster growing method, the proposed method does not require multiple iterations, which reduces the number of calculations, and increases the stability of the results obtained. Thus, the proposed method requires less computational cost than the cluster growing method and achieves higher stability. The method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication networks. The developed method is evaluated for two different network structures and compared with the cluster growing method. Results validate the developed method.

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

Cited by 22 publications

References 27 publications

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

Application of the NARX neural network for predicting a one-dimensional time series

Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

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