Multi-dimensional parametric assessment with IoT in acquaintance of digital pipeline

Kumar, N. Suresh; K, Mallikharjuna Rao; Kothuru, Mahesh; Rao, Y. Narasimha

doi:10.11591/ijece.v9i6.pp4649-4656

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…Moreover, simulations consist of the ability to replicate various activities within the virtual manufacturing cell and to explore and optimize the actions of the manufacturing process [ 19 ]. In addition, development of secured and rapid digital pipelines [ 20 ] enables constant back-and-forth sharing of data, which is essentially required for this desired cyber-physical data fusion [ 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding

Xia

Saidy

Kirkpatrick

et al. 2021

Sensors

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A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation; (2) fast and secured industrial communication networks; (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; (4) interoperability between machine and human. Semantic integration of process indicators is fundamental to the success of such implementation. This work proposes an automated semantic integration of data-intensive process signals that is deployable to industrial signal-based control loops. The proposed system rapidly infers manufacturing events from image-based data feeds, and hence triggers process control signals. Two image inference approaches are implemented: cloud-based ML model query and edge-end object shape detection. Depending on use cases and task requirements, these two approaches can be designated with different event detection tasks to provide a comprehensive system self-awareness. Coupled with conventional industrial sensor signals, machine vision system can rapidly understand manufacturing scenes, and feed extracted semantic information to a manufacturing ontology developed by either expert or ML-enabled cyber systems. Moreover, extracted signals are interpreted by Programmable Logical Controllers (PLCs) and field devices for cognitive automation towards fully autonomous industrial systems.

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Section: Literature Reviewmentioning

confidence: 99%

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding

Xia

Saidy

Kirkpatrick

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, by making easy interaction with a wide range of devices such as, home application, CCTV, surveillance sensors, engines, monitors, vehicles, etc. [7,8]. The IoT model is truly applicable in different areas (home, industrial automation, medical assistance, mobile health care, elderly assistance, smart power management, intelligent networks, cars and more) [9].…”

Section: Internet Of Things (Iot)mentioning

confidence: 99%

Water monitoring and analytic based thingspeak

Miry¹,

Aramice²

2020

IJECE

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Diseases associated with bad water have largely reported cases annually leading to deaths, therefore the water quality monitoring become necessary to provide safe water. Traditional monitoring includes manual gathering of samples from different points on the distributed site, and then testing in laboratory. This procedure has proven that it is ineffective because it is laborious, lag time and lacks online results to enhance proactive response to water pollution. Emergence of the Internet of Things (IoT) and step towards the smart life poses the successful using of IoT. This paper presents a water quality monitoring using IoT based ThingSpeak platform that provides analytic tools and visualization using MATLAB programming. The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze. The system notifies authorities when there are water quality parameters out of a predefined set of normal values. A warning will be notified to user by IFTTT protocol.

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“…The average accuracy will be improved with proper data integrity [21,22]. The throughput and accuracy can be further improved by implementing advanced clock synchronization techniques [13,23]. In the present work all the sensory data is arranged in matrix format for easy understanding, processing, and data retrieving.…”

Section: Integrity Of Datamentioning

confidence: 99%

Providing enhanced security in IoT based smart weather system

Rao¹,

Chandra

Revathi

et al. 2020

IJEECS

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Weather reporting system consist multiple devices which are playing dynamic role in producing dynamic environmental parameters such as temperature, humidity, and air pollution. It was a tedious task to bring all the devices together and make them interleaving to produce relevant measurement. Recent trends have proven that the IoT has brought all the devices and peripherals in one place to make more flexible and smart measurement. In the traditional weather monitoring system measurement method is not real time, not continuous and also tedious job to take continuous measurement. But, the IoT has completely changed the measurement scenario and improved the consistency of the measurement. In the present work the environmental parameters such rainfall, temperature, humidity, and density of carbon dioxide in the air are measured with sensors. The Arduino Uno card gathers all information from the devices which are associated with its port pins. The information is sent to cloud server for record and future retrieving. At the same time the data security in cloud been assured with encryption and decryption data while retrieving the information from cloud. This enhances the security, ease of accessing the cloud data from mobile applications, provides wise predictions and minimizes the communication overhead.

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Multi-dimensional parametric assessment with IoT in acquaintance of digital pipeline

Cited by 6 publications

References 16 publications

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding

Water monitoring and analytic based thingspeak

Providing enhanced security in IoT based smart weather system

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