Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems

Pincheira, Miguel; Antonini, Mattia; Vecchio, Massimo

doi:10.3390/s22030899

Cited by 17 publications

(7 citation statements)

References 34 publications

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“…Tracking these challenges become increasingly more difficult under the centralized framework of a smart environment. By some literature research and analysis, an emerging technology called blockchain is paid attention to this paper [7]. Bitcoin is a decentralized digital currency that is transferred using the peer-to-peer bitcoin network.…”

Section: Introductionmentioning

confidence: 99%

A Smart Campus Implementation Architecture Based on Blockchain Technology

2022

Journal of Sensors

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With the application of 5G technology in the field of education, the construction of smart campus has set off a wave of digital transformation. At the same time, the traditional smart campus is also facing the exponential growth of the number of Internet of Things devices, servers, and application terminals, which makes it difficult to achieve flat management. In view of the current difficulties in the construction of smart campus, this paper proposes smart campus architecture based on blockchain technology. Unlike the traditional smart campus architecture, this paper combines the characteristics of decentralization, high confidentiality, and data sharing of block chain with the Internet of Things technology, which greatly reduces the demand for data storage and physical network equipment. The new smart campus architecture plans the application of smart education based on blockchain, and provides a new solution model and research ideas.

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Section: Introductionmentioning

confidence: 99%

A Smart Campus Implementation Architecture Based on Blockchain Technology

2022

Journal of Sensors

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“…The reason for selecting a public infrastructure is two-fold: First, we create an open registry of information (in this case, anomalies), enabling the inclusion of additional actors interested in that information. This approach follows the current trend of modern blockchain-based applications using public networks o [ 5 , 39 , 40 ]. Second, by relying on external public infrastructure, we bypass the additional resources (i.e., energy, processing power, and communications) required to run the consensus algorithms and maintain the distributed ledger [ 41 ].…”

Section: Proposed Systemmentioning

confidence: 99%

“…edge computing promises to open up new opportunities to create novel, “latency-sensitive” (i.e., more able to respond in real-time) and “context-aware” (i.e., able to adapt to the execution environment) applications. These advantages are particularly valuable for extreme industrial environments, where the operating conditions may affect communication, physical access, and safety of equipment, systems, and workers [ 5 ]. Thus, edge computing applications designed to deal with these harsh conditions can play a critical role in making the environment safer, more efficient, and more productive [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we design and deliver an adaptable and unsupervised anomaly detection system for extreme industrial scenarios, where the operating conditions present several challenges in terms of energy, communication, physical access, and safety [ 5 ]. We combine the edge computing paradigm with IoT best practices and AI tools to ensure reliable and safe performance.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments

Antonini

Pincheira

Vecchio

et al. 2023

Sensors

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Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate corrective actions. Typically, these analyses are conducted on servers located in data centers or the cloud. However, this approach increases system complexity and is susceptible to failure in cases where connectivity is unavailable. Furthermore, this communication restriction limits the approach’s applicability in extreme industrial environments where operating conditions affect communication and access to the system. This paper proposes and evaluates an end-to-end adaptable and configurable anomaly detection system that uses the Internet of Things (IoT), edge computing, and Tiny-MLOps methodologies in an extreme industrial environment such as submersible pumps. The system runs on an IoT sensing Kit, based on an ESP32 microcontroller and MicroPython firmware, located near the data source. The processing pipeline on the sensing device collects data, trains an anomaly detection model, and alerts an external gateway in the event of an anomaly. The anomaly detection model uses the isolation forest algorithm, which can be trained on the microcontroller in just 1.2 to 6.4 s and detect an anomaly in less than 16 milliseconds with an ensemble of 50 trees and 80 KB of RAM. Additionally, the system employs blockchain technology to provide a transparent and irrefutable repository of anomalies.

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“…With its help, information about various processes can be transferred to other participants in the extraction and processing of minerals, which not only include equipment manufacturers, mechanics, electricians, environmentalists, etc., but also company owners. The high level of blockchain and Internet of Things integration makes it possible to connect the capabilities of digital peripherals, cloud computing, mobile devices, and applications (Figure 6) [72]. An important information technology for the Surface Mining 4.0 platform is the blockchain, which makes it possible to create conditions for reliable and confidential data exchange.…”

Section: Review Of End-to-end Technologies Of Industry 40 In Surface ...mentioning

confidence: 99%

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Zhironkin

Taran

2023

Energies

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The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource consumption, and environmental friendliness. In the future, the development of Surface Mining 4.0 will provide a response to the technological shock associated with the acceleration of the digital modernization of the mining sector and the increase in labor productivity, which are reducing the operating costs of raw materials extraction. In this regard, the given review is an attempt to analyze the surface mining digital transformation over the course of the diffusion of Industry 4.0 technologies covered in scientific publications. The authors tried to show the core and frontiers of Surface Mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction. Particular attention was paid to the review of research on the role of Surface Mining 4.0 in achieving sustainable development goals.

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Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems

Cited by 17 publications

References 34 publications

A Smart Campus Implementation Architecture Based on Blockchain Technology

A Smart Campus Implementation Architecture Based on Blockchain Technology

An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

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