Stabilizing Sensor Data Collection for Control of Environment-Friendly Clean Technologies Using Internet of Things

Bhadoria, Robin Singh; Bajpai, Dhananjai

doi:10.1007/s11277-019-06414-x

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…Uma das formas de se obter ganhos operacionais com um sistema IoT é o monitoramento em tempo real e predição realizados por meio de tecnologias de computação em nuvem (GEORGAKOPOULOS, 2016;BHADORIA;BAJPAI, 2019). O relato do Entrevistado E14, no Quadro 2, evidencia como os dados coletados por medidores de água IoT podem trazer valor para uma empresa da área de saneamento: esses dados são capturados pelos medidores e transformados em informações entregues de forma estruturada para gestores, que podem tomar decisões respaldados pelo sistema IoT.…”

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“…Esse exemplo mostra como dispositivos IoT podem monitorar e identificar problemas, além de auxiliar na resolução de forma autônoma. Dessa forma, os dispositivos agregam capacidades de sentir e atuar no mundo físico, aspectos associados ao sensing e seizing das capacidades dinâmicas (ROSSI et al, 2021;BHADORIA;BAJPAI, 2019). Todos os relatos utilizam os dispositivos IoT, conforme demonstrado pela incidência estatística apresentada pelo MAXQDA.…”

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A Vantagem Competitiva Das Empresas Cleantechs E O Desenvolvimento De Capacidades Dinâmicas Utilizando Internet Das Coisas

Noronha

Hayashi

Silva

et al. 2022

REAd. Rev. eletrôn. adm. (Porto Alegre)

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RESUMO As empresas de tecnologia limpa (Cleantechs) utilizam diferentes tecnologias para o desenvolvimento de soluções e inovações no mercado. Entre essas tecnologias, o IoT (Internet of Things) se destaca por viabilizar a automação de processos e a criação de capacidades e soluções que diferenciam os modelos de negócios dessas empresas e podem gerar vantagens de mercado. A pergunta de pesquisa deste trabalho é: Como o IoT impulsiona o desenvolvimento de capacidades dinâmicas para obtenção de vantagem competitiva nas empresas Cleantechs? O objetivo é apresentar um modelo de desenvolvimento de capacidades dinâmicas por meio de IoT para obtenção de vantagem competitiva nas empresas Cleantechs. O percurso metodológico contou com a realização de uma pesquisa qualitativa de caráter descritivo. Foram realizadas 22 entrevistas, representadas por 17 empresas Cleantech. As lentes teóricas utilizadas foram: “IoT”, “Capacidades Dinâmicas” e “Vantagem Competitiva”. Os resultados indicam que o IoT pode minimizar os custos de transação no processo de adaptação de produtos e serviços, aumentar a previsibilidade de incertezas e riscos, além de fomentar a agilidade nas organizações. O trabalho apresenta como contribuição científica um modelo que demonstra como ocorre o desenvolvimento de capacidades dinâmicas via IoT para vantagem competitiva de mercado.

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A Vantagem Competitiva Das Empresas Cleantechs E O Desenvolvimento De Capacidades Dinâmicas Utilizando Internet Das Coisas

Noronha

Hayashi

Silva

et al. 2022

REAd. Rev. eletrôn. adm. (Porto Alegre)

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“…Moreover, suitable communication and computation technologies devoted to measurement activities are needed, to guarantee specific accuracy levels. In this context, reasonably, the industrial network must handle not only an increased amount of measurement traffic in a deterministic way, but also the coexistence of time-critical and normal data exchange [9,[18][19][20][21]. Indeed, in time-critical applications, the concept of time assumes a greater importance and a more refined meaning, and the best effort behavior is no longer sufficient.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

Fedullo

Morato

Tramarin

et al. 2022

Sensors

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The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems within the factory systems of the future. The aforementioned technological revolution strongly affects industrial smart and distributed measurement systems as well, pointing to ever more integrated and intelligent equipment devoted to derive accurate measurements. Moreover, as factory automation uses ever wider and complex smart distributed measurement systems, the well-known Internet of Things (IoT) paradigm finds its viability also in the industrial context, namely Industrial IoT (IIoT). In this context, communication networks and protocols play a key role, directly impacting on the measurement accuracy, causality, reliability and safety. The requirements coming both from Industry 4.0 and the IIoT, such as the coexistence of time-sensitive and best effort traffic, the need for enhanced horizontal and vertical integration, and interoperability between Information Technology (IT) and Operational Technology (OT), fostered the development of enhanced communication subsystems. Indeed, established technologies, such as Ethernet and Wi-Fi, widespread in the consumer and office fields, are intrinsically non-deterministic and unable to support critical traffic. In the last years, the IEEE 802.1 Working Group defined an extensive set of standards, comprehensively known as Time Sensitive Networking (TSN), aiming at reshaping the Ethernet standard to support for time-, mission- and safety-critical traffic. In this paper, a comprehensive overview of the TSN Working Group standardization activity is provided, while contextualizing TSN within the complex existing industrial technological panorama, particularly focusing on industrial distributed measurement systems. In particular, this paper has to be considered a technical review of the most important features of TSN, while underlining its applicability to the measurement field. Furthermore, the adoption of TSN within the Wi-Fi technology is addressed in the last part of the survey, since wireless communication represents an appealing opportunity in the industrial measurement context. In this respect, a test case is presented, to point out the need for wirelessly connected sensors networks. In particular, by reviewing some literature contributions it has been possible to show how wireless technologies offer the flexibility necessary to support advanced mobile IIoT applications.

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An optimized deep learning model using Mutation-based Atom Search Optimization algorithm for cervical cancer detection

Chitra

Kumar

2021

Soft Comput

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Stabilizing Sensor Data Collection for Control of Environment-Friendly Clean Technologies Using Internet of Things

Cited by 12 publications

References 34 publications

A Vantagem Competitiva Das Empresas Cleantechs E O Desenvolvimento De Capacidades Dinâmicas Utilizando Internet Das Coisas

A Vantagem Competitiva Das Empresas Cleantechs E O Desenvolvimento De Capacidades Dinâmicas Utilizando Internet Das Coisas

A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

An optimized deep learning model using Mutation-based Atom Search Optimization algorithm for cervical cancer detection

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