Analysis of architectures implemented for IIoT

Oñate, William; Sanz, Ricardo

doi:10.1016/j.heliyon.2023.e12868

Cited by 6 publications

(6 citation statements)

References 86 publications

(83 reference statements)

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“…The solutions for fog computing from the specialized literature that are analyzed in different review or survey papers [ 13 , 17 , 18 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ] specify that either they are presented at an abstract level or they are solutions for specific applications. An essential advantage of the proposed solution, which distinguishes it from the solutions presented in the specialized literature, is customizability, because it allows the instantiation of objects and their interconnection to implement specific solutions.…”

Section: Discussionmentioning

confidence: 99%

“…The main solutions for fog computing from the specialized literature that are analyzed in different reviews or survey papers [ 13 , 17 , 18 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ] present the high-level parts of the fog nodes, such as security, the task scheduling, or resource scheduling when resources are limited; these papers are not focused on how data are processed within fog computing.…”

Section: Related Workmentioning

confidence: 99%

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A Dynamic IIoT Framework Based on the Publish–Subscribe Paradigm

Ungurean,

Gaitan

2023

Sensors

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The use of the Internet of Things (IoT) technologies and principles in industrial environments is known as the Industrial Internet of Things (IIoT). The IIoT concept aims to integrate various industrial devices, sensors, and actuators for collection, storage, monitoring, and process automation. Due to the complexity of IIoT environments, there is no one-size-fits-all solution. The main challenges in developing an IIoT solution are represented by the diversity of sensors and devices, connectivity, edge/fog computing, and security. This paper proposes a distributed and customized IioT (Industrial Internet of Things) framework for the interaction of things from the industrial environment. This framework is distributed on the fog nodes of the IIoT architecture proposed, and it will have the possibility to interconnect local things (with low latency) or global things (with a latency generated by the Internet network). To demonstrate the functionality of the proposed framework, it is included in the fog nodes presented in other paper. These fog nodes allow the integration of CANOpen networks into an IioT architecture. The most important advantages of the proposed architecture are its customizability and the fact that it allows decision operations to be carried out at the edge of the network to eliminate latency due to the Internet.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Dynamic IIoT Framework Based on the Publish–Subscribe Paradigm

Ungurean,

Gaitan

2023

Sensors

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“…The devices connected to IoT systems are potentially risky in the absence of security elements or algorithms ( Ibrahim and Gebali, 2022 ), reason why the devices performing edge gateway functions have improved designs with the use of application-specific integrated circuits (ASICs), just as security chips are essential in the implementation of Industrial IoT (IIoT) ( Oñate and Sanz, 2023 ) and Ag-IoT systems. Wide evolution and adaptability of ML algorithms lead to their employ in optimizing gateway equipment tasks (ML-Optimized Gateways), making the performance of these tasks efficient even with resource limitations.…”

Section: Emerging Technologies Of Precision Crop Protection In Line W...mentioning

confidence: 99%

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

et al. 2023

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Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.

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“…Likewise, the industrial internet of things (IIOT), presents autonomous work systems operating in real time with diverse architectures and communication protocols that handle large amounts of data with computer security alternatives that allow offering cloud services and that present an alternative for the management of bibliographic resources [22], [23]. In addition, with the current technological advances, it is possible to talk about intelligent power plants that implement automatic learning algorithms with data science to improve the management of machinery and information [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Reading and storage of library resources using UHF RFID technology with IoT

et al. 2023

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Libraries supervisors have the administrative duty of control and keep safe their inventories, such as books, documents, manuals, and theses, facilitating access to their users to this information and helping them loan and return, internal or external, in case it’s permitted. We wanted to design and build a prototype for a device capable of complementing these tasks in a low cost. We go through the processes in the design of a prototype for a simultaneous Radio Frequency Identification Reader using Ultra High Frequencies with Internet of Things technologies for data storage and communication between multiple stations, to do so we use a decision matrix method to select hardware tools, and software programming languages and libraries, needed to build such device, comparing, and weighting the characteristics that fit our needs. The resulting hardware selection was the Raspberry Pi and the RedBoard with a 59,7% and 12,4% respectively, and NodeJS with WebSockets for the software selection with a 53,8%. Finally, we present the designed architecture and a working prototype of the device that was built. Obtained response times from the reader and the database storage were as low as 10 milliseconds and as high as 20 milliseconds.

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Analysis of architectures implemented for IIoT

Cited by 6 publications

References 86 publications

A Dynamic IIoT Framework Based on the Publish–Subscribe Paradigm

A Dynamic IIoT Framework Based on the Publish–Subscribe Paradigm

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Reading and storage of library resources using UHF RFID technology with IoT

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