Search

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. [ABSTRACT FROM AUTHOR]

The incorporation of Artificial Intelligence algorithms in Intelligent Transportation Systems gives rise to new opportunities for a more sustainable urban mobility. However, one of the main challenges is to decide when and where these techniques should be applied; several options appear, such as cloud computing, fog computing, edge computing, or even edge devices. In this paper, an Internet of Things‐based solution for smart traffic management is presented. Using the lightweight Random Early Detection for Vehicles Dynamic mechanism as a basis, we optimize using evolutionary algorithms. Random Early Detection for Vehicles Dynamic can be applied in signaled intersections to proactively detect incipient congestion and set the best cycle and phases of traffic lights. Then, the authors demonstrate that once Random Early Detection for Vehicles Dynamic has been appropriately optimised offline, it can be later used in unknown traffic scenarios without the burden of applying Artificial Intelligence in constrained Internet of Things devices. The performance of this mechanism is widely tested with the SUMO simulation tool. Results show that this improved version, called iREDVD, notably reduces the vehicles' waiting time, average trip time, fuel consumption, and emission of particles and gaseous pollutants compared with other proposals. [ABSTRACT FROM AUTHOR]

Summary: The deployment of Internet of Things (IoT) solutions in smart cities or industrial environments (Industrial IoT, IIoT) demands careful consideration in terms of user‐centric or system‐centric target metrics. A better monitoring system able to transform performance outputs into decision‐making and intelligent actions requires less restrictive performance evaluation methods. Classic approaches to performance evaluation in telecommunication networks rely on Quality of Service (QoS) and/or Quality of user Experience (QoE) assessment models. However, the new IoT paradigm establishes a completely different scenario, where, for instance, consumers might no longer be users but machines. In this paper, we propose the evaluation of the performance of IoT services and applications comprising the combination of four quality measures, namely, Quality of Data (QoD), Quality of Information (QoI), Quality of user Experience (QoE), and Quality Cost (QC). The proposal is analyzed using computing simulations. Specifically, we improve the simulation tool FLoRa (Framework for LoRa) incorporating additional LoRa Wide Area Network (LoRaWAN) features. LoRaWAN is a Low‐Power Wide Area Network (LPWAN) technology that operates on the LoRa modulation scheme. Long coverage ranges, low power consumption, and support for a massive number of IoT devices using a limited infrastructure are the main assets of LoRaWAN, establishing it as one of the top IoT technologies. Results show that it is easier and more efficient to disassociate metrics into different dimensions in order to provide a clear vision of the performance of IoT services. This performance evaluation method can be customized and applied to different IoT markets. [ABSTRACT FROM AUTHOR]

A successful deployment of multimedia applications over wireless environments entails improving the quality of service (QoS), not only from a technical point of view, but also considering the quality of experience (QoE) from the final user's perception. Although objective QoE measure models avoid the difficulties of subjective surveys, subjective QoE assessments are essential to understand the way users evaluate the QoS. In this work, we study the effect of a wide range of parameters on the QoE of VVoIP applications in a real wireless scenario. Through a complete statistical analysis of users' ratings, we identify the following facts. Although the use of VVoIP in wireless networks does not yet represent an advantage for users, there are great expectations for all applications under study, and with greater popularity comes higher expectations. It is easier for respondents to identify good behavior than poor behavior. Whereas the respondents' frequency of Internet use does not impact on the scores, respondents' gender does. Finally, the most determining parameters of quality from a user's perspective were instability, video quality, voice distortion, usefulness, and graphical interface. [ABSTRACT FROM AUTHOR]