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

Abstract

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.

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

“…Authors in [131] included prior knowledge in information fusion to train neural networks and achieved a 10% improvement over statistical traffic predictions. Similarly, authors in [132] and [133] were able to achieve MAE of 0.3 and 0.002% using LSTMs and regression models respectively. Authors in [140] were able to achieve a Root Mean Square Error (RMSE) of 0.0298 in their prediction accuracy with 500 units in their LSTM model.…”

“…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. LSTMs were able to achieve the MSE and MAE of 0.05 and 0.3 respectively while regression models achieved MSE and MAE of 0.004 and 0.002 respectively for different traffic predictions tasks indicating their potential to perform this task accurately [132], [133].…”

“…[133] are promising tools to understand time dependencies and accurately forecast/estimate user requirements. LSTMs were able to achieve the MSE and MAE of 0.05 and 0.3 respectively while regression models achieved MSE and MAE of 0.004 and 0.002 respectively for different traffic predictions tasks indicating their potential to perform this task accurately [132], [133].…”

Toward QoS Prediction Based on Temporal Transformers for IoT Applications

“…There are also few recent studies that applied regression based approaches [12], [13], to predict throughput and packet delivery ratio (PDR), since regression based techniques tend to be a light weight alternative for the prediction of QoS metrics. However, most of the IoT data used for the QoS prediction consist of time series sequences which are better predicted using deep learning approaches, such as Recurrent Neural Networks (RNN) or Long Short-Term Memory (LSTM) networks, that are specifically designed for handling time series data.…”

“…Nonetheless, various research gaps can be identified in these existing studies. Firstly, in [8]- [13] several QoS prediction mechanisms are presented, however, without considering any time dependencies. Secondly, for the works [14]- [17], only a simple traffic prediction is provided, without predicting typical QoS metrics found in an IoT context.…”