IoT System Design—A Project Based Approach

James, Alice; Seth, Avishkar; Mukhopadhyay, Subhas Chandra

doi:10.1007/978-3-030-85863-6_2

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…For many scenarios, the sensors used provide measurement data at low frequency (usually 100-500 Hz, sporadically 1600-6700 Hz), moderate accuracy (±10%) and low resolution (8-12 bits). Building blocks and circuit boards are used, which are also installed in smartphones or are known from the maker scene [5]. This results from the desire to manage the costs and keep the entry barrier as low as possible.…”

Section: State Of the Artmentioning

confidence: 99%

An IIoT-Device for Acquisition and Analysis of High-Frequency Data Processed by Artificial Intelligence

Kneifel

Roj

Woyand

et al. 2023

IoT

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This publication presents the development of an Industrial-Internet-of-Things device. The device is capable of completing several tasks, such as the acquisition of high-frequency measurement data and evaluating data via machine learning methods in an artificial intelligence application. The installed measurement technology generates data which is comparable to data generated by costly laboratory equipment, meaning that it can be used as a low-budget and open-source alternative. A workflow method has been designed that promotes experimental work and simplifies the effort required to implement artificial intelligence solutions. At the end of this paper, the results of the experiment, which aimed to collect measurement data, extract suitable features, and train artificial intelligence models, are presented. Techniques from vibration analysis were used for feature extraction, and concepts for the extrapolation and enhancement of data sets were investigated. The test results have proven that the development is comparable with high-end laboratory equipment. The created application has demonstrated sufficient accuracy in predictions, and the designed process can be used for arbitrary, artificial intelligence-based rapid prototyping.

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Section: State Of the Artmentioning

confidence: 99%

An IIoT-Device for Acquisition and Analysis of High-Frequency Data Processed by Artificial Intelligence

Kneifel

Roj

Woyand

et al. 2023

IoT

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“…T HE global Industrial Internet of Things (IIoT) accounts for more than 10% of the global Internet of Things (IoT) market share, and the demand for IIoT is expected to reach U.S. $110 billion by 2025 [1], [2], [3]. Moreover, IIoT plays a crucial role in smart manufacturing, an integral objective of Industry 4.0 [4], [5], [6]. Smart manufacturing involves factory automation systems (FAS), supply chain management (SCM), cyber-physical systems (CPS), and metaverse, which rely heavily on IIoT and associated entities, such as the Internet of Robotic Things and robotics generally [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, IIoT plays a crucial role in smart manufacturing, an integral objective of Industry 4.0 [4], [5], [6]. Smart manufacturing involves factory automation systems (FAS), supply chain management (SCM), cyber-physical systems (CPS), and metaverse, which rely heavily on IIoT and associated entities, such as the Internet of Robotic Things and robotics generally [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…This phenomenon is characterized by 3VS, i.e., volume, variety, and velocity of sensors which are growing rapidly in DSM [11]. Moreover, the 3VS yields excessive volume, variety, and velocity of data (3VD), big data [6], [11]. The significant growth in IIoT and the 3VD of 3VS poses a formidable challenge for acquiring and processing data while preserving the essential quality of service (QoS) requirements in DSM [3], [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a top-down software AMP is a cognitive solution to addressing ad hoc issues. The SRA is a well-known holistic AMP [6], [15], [19], [58]. Therefore, we propose a novel crosschain-based SRA leading to an AMP for DSM called the IoC of IIoFC for DSM.…”

Section: Introductionmentioning

confidence: 99%

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QoS-Aware Federated Crosschain-Based Model-Driven Reference Architecture for IIoT Sensor Networks in Distributed Manufacturing

Siriweera,

Naruse

2023

IEEE Sensors J.

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The realm of the Industrial Internet of Things (IIoT) encompasses a broad spectrum of sensors that are integral to distributed smart manufacturing (DSM). The miscellaneous IIoT sensors deployed for DSM are distributed and operate in a hierarchical and federated structure. Nonetheless, fulfilling essential quality of service (QoS) requirements, such as ensuring security and privacy (integrity) while maintaining scalability and interoperability (robustness), poses a profound challenge for the DSM cloud service platform. Although blockchain technologies have been used to safeguard integrity, the first two generations have imposed constraints on robustness. In contrast, the third-generation blockchain, a.k.a. decentralized crosschain ecosystem, complements Web 3.0 and metaverse and can mitigate the constraints of previous generations. Moreover, blockchain-based ad hoc solutions for DSM use cases are abundant; they often suffer from limited adaptability and unique or homogeneous use cases from a software engineering perspective. A holistic architectural modeling process (AMP) leading to a software reference architecture (SRA) is preferred when alleviating ad hoc constraints. Therefore, we proposed an AMP for SRA for crosschain-based DSM that safeguards integrity while preserving robustness. In the work described in this article, we have conducted the following system-modeling process. First, we propose a novel software AMP for DSM. Second, we deduce a modeled SRA based on a crosschain. Third, we infer a modeled system architecture (SA). Empirical experiment results demonstrate that our proposed crosschain-based method outperforms the widely used on-chainbased method while achieving our objectives efficiently.

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