PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks

González-Landero, Franks; García‐Magariño, Iván; Lacuesta, Raquel; Lloret, Jaime

doi:10.1109/access.2018.2811900

Cited by 11 publications

(11 citation statements)

References 23 publications

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“…The USE questionnaire contains 30 questions, and each question was evaluated by a Likert-type scale from 1 to 7 (from “strongly disagree” to “strongly agree”, respectively). The USE Questionnaire has been used in various studies to evaluate user’s perceived usability, such as in the evaluation of the software developed for radiologists [29], mobile application [30], and M-health care application [31]. Therefore, it enabled us to measure the participants’ user experience.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Evaluation of User Interface for Ventilators Based on Respiratory Therapists’ Performance, Workload, and User Experience

et al. 2018

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BackgroundPoor ergonomic design of ventilators can result in human errors. In this study, we evaluated the ergonomics of ventilators through respiratory therapists’ performance, workload, and user experience.Material/MethodsSixteen respiratory therapists were recruited to this usability study of 3 ventilators. Participants had to perform 7 tasks on each ventilator. Respiratory therapists’ performance was measured by task errors of all tasks for each participant. Workload was measured by objective measurement (blink rate and duration) and by subjective measurement (NASA-TLX). User experience was assessed by the USE Questionnaire.ResultsFor task errors, significant differences were found among ventilators (p<0.05) and the Evital 4 received higher task errors when compared to the Servo I (p<0.05). For blink rate, significant differences were found in tasks of starting the ventilator, ventilator monitoring values recognition, ventilator setting parameters modification, alarm parameter recognition, and resetting among ventilators (p<0.05). Furthermore, blink duration was also found to be significant differently in tasks of starting the ventilator, mode and setting parameters recognition, ventilator monitoring values recognition, ventilator mode modification, and alarm parameter recognition and resetting, as well as in the average of all tasks (p<0.05). For perceived workload, the Evital 4 received higher NASA-TLX scores among ventilators. For user experience, the Servo I received the highest scores on the USE Questionnaire among the ventilators.ConclusionsThe study provides a comprehensive evaluation method of user interface based on respiratory therapists’ performance, workload, and user experience. In addition, this study suggests that the ergonomic design of the Evital 4 is poor. Finally, we found that eye motion (blink rate and duration) may be useful to assess the ergonomics of a user interface.

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Section: Methodsmentioning

confidence: 99%

Comprehensive Evaluation of User Interface for Ventilators Based on Respiratory Therapists’ Performance, Workload, and User Experience

et al. 2018

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“…Appropriate mechanisms for considering the users' priorities in 5G ultra-dense networks [116]. QS/OF.…”

Section: Communication and Informationmentioning

confidence: 99%

Methodological Proposals for the Development of Services in a Smart City: A Literature Review

et al. 2020

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This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as AI application areas. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 AI application areas, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring–smart electric grid, make the greater contributions.

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“…The 5G environment is applied in many fields and utilized for many purposes, such as smart cities [21], e-Health [22], and environmental surveillance [23]. Many approaches have been applied to the 5G environment in order to enhance the network performance [24].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks

Cited by 11 publications

References 23 publications

Comprehensive Evaluation of User Interface for Ventilators Based on Respiratory Therapists’ Performance, Workload, and User Experience

Comprehensive Evaluation of User Interface for Ventilators Based on Respiratory Therapists’ Performance, Workload, and User Experience

Methodological Proposals for the Development of Services in a Smart City: A Literature Review

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

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