Suitability of MQTT and REST Communication Protocols for AIoT or IIoT Devices Based on ESP32 S3

Nemlaha, Eduard; Střelec, Peter; Horák, Tibor; Kováč, Szabolcs; Tanuška, Pavol

doi:10.1007/978-3-031-21435-6_19

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Communication between the used sensors is performed with the help of the standards-based messaging MQTT protocol. MQTT is a fast and lightweight protocol that allows messaging between devices located on unstable networks and ensures secure, reliable and two-way messaging [18]. It is designed as a lightweight publish/subscribe messaging transport that is ideal for connecting remote devices with a small network bandwidth.…”

Section: Figure 1 Iiot System Schemementioning

confidence: 99%

Identification of Inability States of Rotating Machinery Subsystems Using Industrial IoT and Convolutional Neural Network – Initial Research

Kolar

Lisjak

Curman³

et al. 2023

Teh. glas. (Online)

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Rotating parts can be found in almost all operational equipment in the industry and are of great importance for proper operation. However, reliability theory explains that every industrial system can change its state when failure happens. Predictive maintenance as one of the latest maintenance strategy emerged from the Maintenance 4.0 concept. Nowadays, this concept can include Industrial Internet of Things (IIoT) devices to connect industrial assets thus enable data collection and analysis that can help make better decisions about maintenance activity. Robust data acquisition system is a prerequisite for any modern predictive maintenance task as it provides necessary data for further analysis and health assessment of the industry asset. Fault diagnosis is an important task in the maintenance of industrial rotating subsystems, considering that early state change diagnosis and fault identification can prevent system failure. Vibration analysis in theory and practice is considered a correct technique for early detection of state changes and failure diagnostics of rotating subsystems. The identified technical state should be considered in a context of the ability and different inability states. Therefore, early different inability states identification is the next step in the rotary machinery diagnostics procedure. Most of the existing techniques for fault diagnosis of rotating subsystems that use vibrations involve the step of extracting features from the raw signal. Considering that the features that describe the behavior of the rotary subsystem can differ significantly depending on the type of equipment, such an approach usually requires an expert in the field of signal processing and rotary subsystems who can define the necessary features. Recently, the emergence of machine deep learning and its application in maintenance promises to provide highly efficient fault diagnostics while simultaneously reducing the need for expert knowledge and human labour. This paper presents authors aim to use self-developed IIoT system built as an IIoT accelerometer as the edge device, web API and database with convolutional neural network as deep learning-based data-driven fault diagnosis to detect and identify different inability states of rotating subsystems. Large dataset for two different rotational speed is collected using IIOT system and multiple convolutional neural network models are trained and tested to examine possibility of using IIOT for inability state prediction.

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Section: Figure 1 Iiot System Schemementioning

confidence: 99%

Identification of Inability States of Rotating Machinery Subsystems Using Industrial IoT and Convolutional Neural Network – Initial Research

Kolar

Lisjak

Curman³

et al. 2023

Teh. glas. (Online)

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“…MQTT is suitable for healthcare applications that require realtime data exchange and efficient communication between devices, such as remote patient monitoring or sensor data collection. It is simplicity and low overhead makes it ideal for resource-constrained environments [18]. On the other hand, Representational State Transfer (RESTful) is an architectural style used for designing networked applications.…”

Section: Data Interoperability Standardsmentioning

confidence: 99%

Literature Review: Clinical Data Interoperability Models

et al. 2023

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A medical entity (hospital, nursing home, rest home, revalidation center, etc.) usually includes a multitude of information systems that allow for quick decision-making close to the medical sensors. The Internet of Medical Things (IoMT) is an area of IoT that generates a lot of data of different natures (radio, CT scan, medical reports, medical sensor data). However, these systems need to share and exchange medical information in a seamless, timely, and efficient manner with systems that are either within the same entity or other healthcare entities. The lack of inter- and intra-entity interoperability causes major problems in the analysis of patient records and leads to additional financial costs (e.g., redone examinations). To develop a medical data interoperability architecture model that will allow providers and different actors in the medical community to exchange patient summary information with other caregivers and partners to improve the quality of care, the level of data security, and the efficiency of care should take stock of the state of knowledge. This paper discusses the challenges faced by medical entities in sharing and exchanging medical information seamlessly and efficiently. It highlights the need for inter- and intra-entity interoperability to improve the analysis of patient records, reduce financial costs, and enhance the quality of care. The paper reviews existing solutions proposed by various researchers and identifies their limitations. The analysis of the literature has shown that the HL7 FHIR standard is particularly well adapted for exchanging and storing health data, while DICOM, CDA, and JSON can be converted in HL7 FHIR or HL7 FHIR to these formats for interoperability purposes. This approach covers almost all use cases.

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“…Special protocols must also be used, for example, MQTT application layer protocol, and use of special cloud services, e.g. B. Arctic Cloud, IBM Cloud, Intel Cloud [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Air quality monitoring using multi node slave IoT

Rahani,

Fathurrahman

2024

J. Soft Comput. Explor.

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Jakarta is the city with the second poorest air quality in the world. IQAir data show that Jakarta's air quality is 159. In addition, the concentration of air particles in Jakarta is 14.2 times higher than the annual guidelines of the World Health Organization (WHO). According to the WHO, exposure to air pollution causes around 7 million premature deaths and millions of years of lost health time each year. Air pollution also stunts children's growth, impairs lung function, etc. Therefore, we need a system that can be used to combine air quality to determine how dangerous a place is with air quality. Knowing air quality, certain policies or actions being taken to overcome this danger. This research aims to build and test a prototype air quality monitoring system using multi-node slaves with the Internet of Things. The prototype development process was carried out by adapting the architectural framework of the air quality monitoring system with the Internet of Things. The testing of prototype results is carried out to sound sensor values and functional success. The results of the test show that the system can run well according to the design made. The DSM501A sensor device functions to detect particles of a size larger than one micrometer, which usually include cigarette smoke, house dust, ticks, spores, pollen, and mildew, and works well so that the controller can read the surrounding air conditions well.

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Suitability of MQTT and REST Communication Protocols for AIoT or IIoT Devices Based on ESP32 S3

Cited by 6 publications

References 11 publications

Identification of Inability States of Rotating Machinery Subsystems Using Industrial IoT and Convolutional Neural Network – Initial Research

Identification of Inability States of Rotating Machinery Subsystems Using Industrial IoT and Convolutional Neural Network – Initial Research

Literature Review: Clinical Data Interoperability Models

Air quality monitoring using multi node slave IoT

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