Delay prediction in IoT using Machine Learning Approach

Abdellah, Ali R.; Mahmood, Omar Abdulkareem; Koucheryavy, Andrey

doi:10.1109/icumt51630.2020.9222245

Cited by 20 publications

(8 citation statements)

References 12 publications

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“…e predictive accuracy is measured with the RMSE score and the mean absolute percentage of error (MAPE) as the MAE score. In [6], NARX time-series recurrent neural networks have been utilized to anticipate IoT communication. ree neural network training techniques have been used to test the predictability, trainlm, traincgf, and trainrp, with MSE, RMSE, and MAPE performance evaluators.…”

Section: Related Workmentioning

confidence: 99%

Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model

Patil

Raj

Singh

2021

Security and Communication Networks

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Prediction of IoT traffic in the current era has attracted noteworthy attention to utilize the bandwidth and channel capacity optimally. In this paper, the problem of IoT traffic prediction has been studied, and solutions have been proposed by using machine learning method ARIMA and learning time series algorithms such as LSTM and gated recurrent unit (GRU-NN) based on neural networks. The proposed GRU-NN predicts the traffic on the basis of transfer learning. The advantage of the GRU-NN over LSTM is also highlighted by solving the problem of gradient disappearance. The proposed GRU-NN memorizes the traffic characteristics of the IoT environment for a long time which eventually helps the system to forecast the upcoming traffic from the existing traces of the traffic. The proposed GRU-NN makes use of the transfer learning technique to handle the problem of insufficient IoT traffic data along with the gradient boosting training method for achieving better accuracy in predicting the network traffic in the IoT environment. The results reveal that the proposed GRU-NN model outperforms the other traffic predictors in terms of statistical performance evaluation parameters such as MAE, RMSE, MRE, and MSE. The results show that the GRU-NN provides the most accurate predictions followed by the LSTM predictor and then ARIMA and other approaches taken up for the comparative study.

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Section: Related Workmentioning

confidence: 99%

Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model

Patil

Raj

Singh

2021

Security and Communication Networks

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“…The dataset included only two features (obfuscated mobile identification (SIM) and the time stamp of traffic records). The authors in [8] proposed a single step ahead and a multistep prediction method for delay prediction in IoT based on NARX recurrent neural network. They simulated an IoT environment and used a simulated dataset.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Regression Approach for Throughput Estimation in an IoT Environment

Hameed

Violos

Santi

et al. 2021

2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing &Amp; Communications (GreenCom) An

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The success of Internet of Things (IoT) has significantly increased the volume of data generated by various smart applications. However, as many of these applications are characterized by strict Quality of Service (QoS) requirements, there is a growing need for accurately predicting typical performance parameters such as throughput. This prediction should be based on the applications' traffic profiles and at the same time reflect the network uncertainty that IoT access networks add to the overall communication. In this work, we deployed 6 different smart building applications in a real testbed while creating a considerable traffic contention in an IEEE 802.15.4 access network. After preprocessing the raw data and following a feature engineering mechanism, we apply five different regression learning approaches to each application and predict its throughput. By resorting to several prediction error metrics and time metrics such as training and inference time, we show that the multiple linear regression achieves high accuracy while outperforming other well known machine learning methods.

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“…These challenges affect delayed operations and applications involving IoT sensing devices. One primary concern is to avoid long delays during the execution of IoT systems due to the problem of information congestion in IoT-enabled smart cities [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

et al. 2022

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Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

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Delay prediction in IoT using Machine Learning Approach

Cited by 20 publications

References 12 publications

Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model

Prediction of IoT Traffic Using the Gated Recurrent Unit Neural Network- (GRU-NN-) Based Predictive Model

A Machine Learning Regression Approach for Throughput Estimation in an IoT Environment

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

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