Microservices-based IoT Monitoring Application with a Domain-driven Design Approach

Rahmatulloh, Alam; Sari, Dewi Wulan; Shofa, Rahmi Nur; Darmawan, Irfan

doi:10.1109/icadeis52521.2021.9701966

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…This includes parameter tuning, feature selection, model selection, and evaluation for machine learning models. Data as a service: Implementing data as a service ( Rahmatulloh et al, 2021 ) fosters distributed data management, enhancing interoperability and exchangeability among different applications. Data representation: Proper data representation is vital for contextualization, enabling insightful comparisons and trend analytics ( Bhavadharini et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

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An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

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Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture’s effectiveness. To further assess the architecture’s practical application, a prototype architecture for predicting pandemic anxiety was developed.

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Section: Resultsmentioning

confidence: 99%

“…Data as a service: Implementing data as a service ( Rahmatulloh et al, 2021 ) fosters distributed data management, enhancing interoperability and exchangeability among different applications.…”

Section: Resultsmentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

View full text Add to dashboard Cite

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“…Concurrently, the Internet of Things (IoT) has emerged as a rapidly advancing concept, revolutionizing connectivity and data exchange across various applications [5,6]. In the realm of water monitoring [7][8][9][10], IoT devices have demonstrated their utility in communication, information sharing, and the coordination of decisions through network-integrated data analysis [11]. Prior studies have introduced IoT-based water monitoring systems incorporating components such as the SEN-HZ21WA water flow sensor, Arduino Uno, and wireless communications [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

IoT-Enabled Water Distribution Monitoring: A Sensor-Based Analytical Model

Rahmatulloh,

Supriatna,

Widiyasono

et al. 2023

ISI

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Water service providers are confronted with multifaceted challenges ranging from ensuring service quality to addressing technical issues such as pipeline leaks. Manifestations of such leaks are often detected through diminished water pressure, yet the monitoring systems employed by these providers remain suboptimal. Traditional methodologies for identifying and inspecting distribution issues rely heavily on manual, analog procedures. This study seeks to harness the Internet of Things (IoT) for the development of an enhanced water distribution monitoring system. Employing a quantitative experimental methodology, the proposed system integrates an Arduino Uno microcontroller with sensors for water pressure, flow, and GPS location. Data captured by these sensors is synchronized with the Thinger.io platform via an ESP-32 module, facilitating real-time monitoring. Upon evaluation, it was observed that IoT implementation via Thinger.io could effectively track fluctuations in water discharge and pressure. Recorded data revealed a maximum water discharge of 303 liters, with water pressure values ranging from a low of 0.30 bar to a high of 4.07 bar. The outcome of this research is a visual monitoring framework that provides a dynamic assessment of water discharge and pressure, enabling water service providers to conduct routine evaluations with enhanced precision. This IoT-based approach not only streamlines the monitoring process but also introduces a level of automation that could significantly improve service reliability for drinking water infrastructures.

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