Eminent Data Visualization Tools for Integration of Big Data with IoT

Poonam,; Mittal, Aditi

doi:10.48175/ijarsct-1109

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Therefore, it is important to analyze this data and integrate big data with the Internet of Things. This paper by Mrs. Poonam (2021) defines key concepts related to Big Data and the Internet of Things, explores the role that Big Data plays in the IoT, and looks at several data visualization techniques for making sense of all that information [17].…”

Section: Related Workmentioning

confidence: 99%

Research on the Deployment Strategy of Big Data Visualization Platform by the Internet of Things Technology

Zhang

2023

EAI Endorsed Scal Inf Syst

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INTRODUCTION: To improve the big data visualization platform's performance and task scheduling capability, a big data visualization platform is constructed based on Field Programmable Gate Array (FPGA) chip application power equipment.OBJECTIVES: This study proposes to combine a genetic algorithm and an ant colony scheduling (ACOS) algorithm to design a big data visualization platform deployment strategy based on an improved ACOS algorithm.METHODS: Firstly, big data technology is analyzed. Then, the basic theory of the ant colony algorithm is studied. According to the basic theory of ACOS and genetic algorithm, an improved ACOS algorithm model is constructed. The improved ACOS algorithm scheduler is compared with the other three schedulers. Under the same environment, the completion time of scheduling the same job and different task amounts are analyzed. The Central Processing Unit (CPU) utilization is analyzed when different schedulers have entirely different workloads. RESULTS: The results show that the constructed big data visualization platform based on the improved ACOS algorithm model has higher task scheduling efficiency than other schedulers and can greatly shorten the data processing time. The experimental results show that under the homogeneous cluster, the completion time of the improved ACOS algorithm generally lags the capacity scheduler and the fair scheduler. Under the heterogeneous cluster, the improved ACOS algorithm scheduler can reasonably allocate tasks to nodes with different performances, reducing the task completion time. When the number of completed tasks increases from 50 to 200, the time increases by 45s, and the completion time is shorter than other schedulers. The CPU utilization of different task volumes is the highest, and the utilization rate increases from 81% to 95%. CONCLUSION: The improved ACOS algorithm scheduler has the shortest data processing time and the highest efficiency. This work provides a specific reference value for optimizing the big data visualization platform's deployment strategy and improving the platform's performance.

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Section: Related Workmentioning

confidence: 99%

Research on the Deployment Strategy of Big Data Visualization Platform by the Internet of Things Technology

Zhang

2023

EAI Endorsed Scal Inf Syst

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Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming

Dineva

Atanasova

2022

Sensors

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Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide opportunities for extracting meaningful knowledge for the farmers. This often leads to a sense of missed transparency, fairness, and accountability, and a lack of motivation for the majority of farmers to invest in sensor-based intelligent systems to support and improve the technological development of their farm and the decision-making process. In this paper, a data-driven intelligent monitoring system in a cloud environment is proposed. The designed architecture enables a comprehensive solution for interaction between data extraction from IoT devices, preprocessing, storage, feature engineering, modelling, and visualization. Streaming data from IoT devices to interactive live reports along with built machine learning (ML) models are included. As a result of the proposed intelligent monitoring system, the collected data and ML modelling outcomes are visualized using a powerful dynamic dashboard. The dashboard allows users to monitor various parameters across the farm and provides an accessible way to view trends, deviations, and patterns in the data. ML models are trained on the collected data and are updated periodically. The data-driven visualization enables farmers to examine, organize, and represent collected farm’s data with the goal of better serving their needs. Performance and durability tests of the system are provided. The proposed solution is a technological bridge with which farmers can easily, affordably, and understandably monitor and track the progress of their farms with easy integration into an existing IoT system.

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Eminent Data Visualization Tools for Integration of Big Data with IoT

Cited by 2 publications

References 3 publications

Research on the Deployment Strategy of Big Data Visualization Platform by the Internet of Things Technology

Research on the Deployment Strategy of Big Data Visualization Platform by the Internet of Things Technology

Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming

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