IIoT Gateway for Edge Computing Applications

Craciunescu, Mihai; Chenaru, Oana; Dobrescu, Radu; Florea, Gheorghe; Mocanu, Ştefan

doi:10.1007/978-3-030-27477-1_17

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…Edge technologies must be embraced to ensure that they work as intended. Specifically, automation in production lines can be improved with machine vision capabilities in factory systems, such as robotic arms used for bin picking [3] or product quality check via multi-camera surveillance [36]. Such computer vision architectures can divide the processing between the Cloud and the components at the production plant to accelerate production.…”

Section: Applicationsmentioning

confidence: 99%

“…In order to normalize this full continuous computing strategy in the IoT world, the IoTinuum architecture [39] serves as a reference for developing different flavors of architectures. Here, we must emphasize the role of the IoT gateway, which not only bridges the Mist to the Fog or Cloud but also supports executing part of the Cloud/Fog business logic closer to the IoT network to satisfy real-time case requirements [36].…”

Section: Architecturementioning

confidence: 99%

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In-depth analysis and open challenges of Mist Computing

2022

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The advent and consolidation of the Massive Internet of Things (MIoT) comes with a need for new architectures to process the massive amount of generated information. A new approach, Mist Computing, entails a series of changes compared to previous computing paradigms, such as Cloud and Fog Computing, with regard to extremely low latency, local smart processing, high mobility, and massive deployment of heterogeneous devices. Hence, context awareness use cases will be enabled, which will vigorously promote the implementation of advantageous Internet of Things applications. Mist Computing is expected to reach existing fields, such as Industry 4.0, future 6G networks and Big Data problems, and it may be the answer for advanced applications where interaction with the environment is essential and lots of data are managed. Despite the low degree of maturity, it shows plenty of potential for IoT together with Cloud, Fog, and Edge Computing, but it is required to reach a general agreement about its foundations, scope, and fields of action according to the existing early works. In this paper, (i) an extensive review of proposals focused on Mist Computing is done to determine the application fields and network elements that must be developed for certain objectives, besides, (ii) a comparative assessment between Cloud, Fog, Edge, and Mist is completed and (iii) several research challenges are listed for future work. In addition, Mist Computing is the last piece to benefit from the resources of complete network infrastructures in the Fluid Computing paradigm.

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Section: Applicationsmentioning

confidence: 99%

Section: Architecturementioning

confidence: 99%

In-depth analysis and open challenges of Mist Computing

2022

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“…Craciunescu et al (2020) [ 46 ] presented an IIoT gateway for edge computing applications. However, despite their statement regarding the versatility of the solution for different industrial scenarios, no information was given on how to achieve this, neither on how to add extra functionalities to the gateway nor on how to extend the existing communication interfaces.…”

Section: Literature Reviewmentioning

confidence: 99%

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

Resende

Folgado

Oliveira

et al. 2021

Sensors

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Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios.

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“…With regard to industry 4.0, also known as industrial internet of things (IIoT), it interconnects numerous variable industrial equipment devices through the network [113]. This way, data acquisition and exchange, as well as collaborative decision-making, are carried out through distributed computation in near-real-time [114]. Hence, efficient, intelligent, and decentralized solutions are available [115], allowing the interaction of multivendor devices through heterogeneous networks in an optimal manner [116], where a higher level of trust may be imposed by using blockchain technology [117].…”

Section: Edge Computing Applicationsmentioning

confidence: 99%

Modeling of a Generic Edge Computing Application Design

Roig

Alcaraz

Gilly

et al. 2021

Sensors

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Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.

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IIoT Gateway for Edge Computing Applications

Cited by 11 publications

References 20 publications

In-depth analysis and open challenges of Mist Computing

In-depth analysis and open challenges of Mist Computing

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

Modeling of a Generic Edge Computing Application Design

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