Evaluation of IoT Data Visualization Tools and Techniques

Peddoju, Suresh K.; Upadhyay, Himanshu

doi:10.1007/978-981-15-2282-6_7

Cited by 18 publications

(11 citation statements)

References 15 publications

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“…A starting point to integrate the user in the information visualization process is to analyze how the user will obtain information from the system. There are several ways to represent and classify the information, Protopsaltis [19], and Peddoju [27] made a description of the most used charts in information visualization based on the analysis and pattern of data of interest. Their work concentrates on the main charts used when making decisions with a dashboard, as defined in Table 1, with univariate, bivariate, or multivariate variables.…”

Section: Data Visualization For City Dashboardmentioning

confidence: 99%

Design of an adaptable dashboard for smart cities

Contreras-Figueroa¹,

Montané-Jiménez²,

García³

et al. 2023

Proceedings of ISP RAS

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Today there are smart cities that, through the use of information technologies, sensors, and specialized infrastructure, focus their efforts on improving the quality of life of their inhabitants. From these efforts arose the need to analyze and represent data within a system to make it useful and understandable to people, for which dashboards emerge. The objective of these systems is to provide users with information to support decision-making, so it is essential to adapt the visualization of the information provided to their needs and preferences. However, the analysis of adaptability through user interaction and its benefits is a topic still under exploration. This paper analyzes the literature on information visualization in adaptable dashboards for smart cities. Based on the elements of adaptable dashboards identified in the literature review, we propose an adaptable dashboard architecture, identify the main characteristics of the users of a smart city dashboard, and build an adaptable dashboard prototype using user-centered techniques.

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Section: Data Visualization For City Dashboardmentioning

confidence: 99%

Design of an adaptable dashboard for smart cities

Contreras-Figueroa¹,

Montané-Jiménez²,

García³

et al. 2023

Proceedings of ISP RAS

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“…IoT requires platforms, tools, and libraries to visualize the data. IBM’s BlueMix is a platform that connects innumerable devices and sensors and provides API‘s for data visualization in formats specific to the device [ 266 ]. ThingSpeak supports sensor data along with MATLAB for visualization.…”

Section: Iot Architecture and It's Operating Systemsmentioning

confidence: 99%

Decades of Internet of Things Towards Twenty-first Century: A Research-Based Introspective

Nagajayanthi

2021

Wireless Pers Commun

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“…For all possible threshold values in the range, e.g., [1,50]-in terms of standard deviations-we calculate the F 2 -score. That is, if given a certain threshold, those periods of time that we know that are anomalous are detected as so-i.e., being over/below the threshold.…”

Section: ) Setting Thresholdsmentioning

confidence: 99%

“…Modern monitoring systems provide network managers with thousands of time series of diverse measurements on beautiful dashboard environments such as Grafana, Kibana, or Tableau [1]. Such time series may represent internetwork Key Performance Indicators (KPI) such as the bandwidth, round trip times (RTT), TCP-window sizes, or the number of retransmissions [2].…”

Section: Introductionmentioning

confidence: 99%

Towards the Automatic and Schedule-Aware Alerting of Internetwork Time Series

et al. 2021

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A common factor of every network monitoring system is an alerting module for time series. This module aims at triggering a warning when any type of abnormal behavior is detected in the patterns of a time series. Such a search for anomalies can be carried out by network managers as a supervised task such that the thresholds for considering a measurement as an anomaly are set following a manual process. Alternatively, we focus on how to translate such a task to an unsupervised one, thus alleviating network managers' dedication. To this end, we have developed, based on the experience of monitoring dozens of networks, a player of real anomalies. Thus, by recreating real issues, the alerting systems' parametrization can be carried out without supervision. Additionally, as a novelty, we propose to consider the network managers' workforce as a significant parameter to configure the thresholds of the alerting moduleessentially, avoiding triggering alarms that will hardly receive attention. Then, we propose to measure and rank alarms by relevance, and relate them to the time to be solved for constructing, eventually, automatic schedules for the members of the staff-according to their time availability. Finally, all these proposals have been put into practice in various deployments of monitoring systems on networks in operation, which gives us evidence of its usefulness and low demand for resources.

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Evaluation of IoT Data Visualization Tools and Techniques

Cited by 18 publications

References 15 publications

Design of an adaptable dashboard for smart cities

Design of an adaptable dashboard for smart cities

Decades of Internet of Things Towards Twenty-first Century: A Research-Based Introspective

Towards the Automatic and Schedule-Aware Alerting of Internetwork Time Series

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