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

International audienceThe 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

Towards QoS Prediction based on Temporal Transformers for IoT Applications

International audienceInternet of Things (IoT) devices generate a tremendous amount of time series data that is extremely dynamic, heterogeneous and time dependent. Such types of data introduce significant challenges for the real-time prediction of QoS metrics of IoT applications with different traffic characteristics. To this end, in this paper, we propose a temporal transformer model and a unified system to predict several QoS metrics of heterogeneous IoT applications when they communicate with the Edge of the network. The transformer model also leverages an attention module to provide a solution for both short-term and long-term sequence prediction of QoS metrics that allows to better extract any time dependencies. In particular, in our framework, we firstly generate a set of datasets containing real-time traffic information of five different IoT applications such as Heating, Ventilation, and Air Conditioning (HVAC), lighting, Voice over Internet Protocol (VoIP), surveillance and emergency response using the 802.15.4 access technology and the RPL routing protocol. Following, we perform the data cleaning, downsampling and pre-processing of the datasets and we construct the QoS datasets, which include four QoS metrics, namely throughput, packet delivery ratio, packet loss ratio and latency. Finally, we evaluate the transformer model through extensive experimentation using both short-term and long-term dependencies and we show that our model can guarantee a robust performance and accurate QoS prediction

Automated and Reproducible Application Traces Generation for IoT Applications

International audienceIn this paper, we investigate and present how to generate application traces of IoT (Internet of Things) Applications in an automated, repeatable and reproducible manner. By using the FIT IoT-Lab large scale testbed and relying on state-of-the-art software engineering techniques, we are able to produce, collect and share artifacts and datasets in an automated way. This makes it easy to track the impact of software updates or changes in the radio environment both on a small scale, e.g. during a single day, and on a large scale, e.g. during several weeks. By providing both the source code for the trace generation as well as the resulting datasets, we hope to reduce the learning curve to develop such applications and encourage reusability as well as pave the way for the replication of our results. While we focus in this work on IoT networks, we believe such an approach could be of used in many other networking domains