A novel holistic approach for performance evaluation in Internet of Things

Martinez‐Caro, Jose‐Manuel; Cano, Maria‐Dolores

doi:10.1002/dac.4454

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…For instance, it is observable that QoE is showing an average value because, in this simulation, it is impacted by delay and packet delivery ratio. More information about this quality model and its four quality dimensions can be found in [10].…”

Section: Methodology and Proposalmentioning

confidence: 99%

“…The proposed warning system can be seen as a classification problem that takes the value 1 when action is needed and 0 otherwise, allowing us to compare the results with other models from the related literature. A similar and interesting classification model was found in [16] for quality assessment, but that uses Mean Opinion Score (MOS) as the quality metric instead of using a more comprehensive quality model as QoX [10,27]. The MOS scale ranges from 1-5, where 1 is the minimum value for quality (lowest quality), and 5 is the maximum (highest quality).…”

Section: Proposing a Warning System For Performance Controlmentioning

confidence: 99%

“…Such is also the case of the quality model proposed by the authors in ref. [10], where we introduced the concept of Quality of X (QoX). QoX considers data, information, network behaviour, and user experience when evaluating or monitoring the quality of a particular system giving rise to four quality dimensions.…”

Section: Introductionmentioning

confidence: 99%

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A novel system to control and forecast QoX performance in IoT‐based monitoring platforms

Martinez‐Caro,

Tasic,

Cano

2023

IET Wireless Sensor Systems

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Communication architectures based on the Internet of Things (IoT) are increasingly frequent. Commonly, these solutions are used to carry out control and monitoring activities. It is easy to find cases for manufacturing, prediction maintenance, Smart Cities, etc., where sensors are deployed to capture data that is sent to the cloud through edge devices or gateways. Then that data is processed to provide useful information and perform additional actions if required. As crucial as deploying these monitoring solutions is to verify their operation. In this article, we propose a novel warning method to monitor the performance of IoT‐based systems. The proposal is based on a holistic quality model called Quality of X (QoX). QoX refers to the use of a variety of metrics to measure system performance at different quality dimensions. These quality dimensions are data (Quality of Data, QoD), information (Quality of Information, QoI), users' experience (Quality of user Experience, QoE), and cost (Quality Cost, QC). In addition to showing the IoT system performance in terms of QoX in real‐time, our proposal includes (i) a forecasting model for independent estimation of QoX applying Deep Learning (DL), specifically using a Long Short‐Term Memory (LSTM) and time series, and (ii) the warning system. In light of our results, our proposal shows a better capacity to forecast quality drops in the IoT‐based monitoring system than other solutions from the related literature.

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Section: Methodology and Proposalmentioning

confidence: 99%