Automated and Reproducible Application Traces Generation for IoT Applications

Santi, Nina; Grünblatt, Rémy; Foubert, Brandon; Hameed, Aroosa; Violos, John; Leivadeas, Aris; Mitton, Nathalie

doi:10.1145/3479242.3487321

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…This architecture is restricted to data generated from sensors like sonar and temperature sensors and is presented from a software perspective only. While [11] generated different types of network traffic data with the FIT IoT-LAB testbed. Their work includes a single technology (IEEE 802.15.4) and focuses on the delay and throughput of links under different message sizes and frequencies.…”

Section: State Of the Artmentioning

confidence: 99%

Dataset Collection of Multi-Communication Technologies Monitored in Different Mobility Contexts

Koteich,

Mitton

2024

2024 International Wireless Communications and Mobile Computing (IWCMC)

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Section: State Of the Artmentioning

confidence: 99%

Dataset Collection of Multi-Communication Technologies Monitored in Different Mobility Contexts

Koteich,

Mitton

2024

2024 International Wireless Communications and Mobile Computing (IWCMC)

Self Cite

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“…This behavior creates another level of QoS uncertainty that can lead to considerable traffic fluctuations, as well as spectrum and resource requirements. More details regarding the testbed and the dataset generation can be found in [25].…”

Section: A Applications and Edge Computing Infrastructurementioning

confidence: 99%

Toward QoS Prediction Based on Temporal Transformers for IoT Applications

Hameed

Violos

Leivadeas

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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Internet 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.

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Automated and Reproducible Application Traces Generation for IoT Applications

Cited by 2 publications

References 30 publications

Dataset Collection of Multi-Communication Technologies Monitored in Different Mobility Contexts

Dataset Collection of Multi-Communication Technologies Monitored in Different Mobility Contexts

Toward QoS Prediction Based on Temporal Transformers for IoT Applications

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