Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms

Javed, Saleha; Javed, Salman; Deventer, Jan van; Sandin, Fredrik; Delsing, Jerker; Liwicki, Marcus; Martin-del-Campo, Sergio

doi:10.1109/icps51978.2022.9816960

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…The learning rate was initialized to 0.001 and reduced to 10 −4 in 25 thousand iterations, with an exponentially decaying rate. After 25 thousand iterations, we operated a linearly decaying learning rate, as in Equation (6). A total of one million iterations with 16 batch sizes were used to train the proposed system.…”

Section: Training Proceduresmentioning

confidence: 99%

“…The focus of such mergers has been reshaping society [2] by bridging physical divides via digital connectivity using IIoT and digitization applications. Applications include automation of manufacturing processes [3,4], agriculture for precision fertilization programs [5], smart farming, condition monitoring of wind turbines [6] and farms, smart factories [7], smart buildings and cities [8], and many others. By digitizing physical processes, these applications have lowered the overheads associated with human dependency, as well as the cost, time, and computation required.…”

Section: Introductionmentioning

confidence: 99%

“…We focus on designing an ontology alignment model as a first step toward developing automatic dynamic translation between IIoT heterogeneous devices. The proposed model could be embedded into dynamic automated IIoT applications with multiple interconnected and heterogeneous devices, for IIoT applications that require intercommunication for performing a mutual task, such as condition monitoring of wind turbines [6] or access control systems [3]. Our model can utilize online M2M translation across devices with varying ontologies, to allow seamless operations.…”

Section: Introductionmentioning

confidence: 99%

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Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT

Javed,

Usman,

Sandin

et al. 2023

Sensors

Self Cite

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The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device’s message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.

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Section: Training Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT

Javed,

Usman,

Sandin

et al. 2023

Sensors

Self Cite

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show abstract

Section: Introductionmentioning

confidence: 99%

Deep Ontology Alignment using Natural Language Processing Approach for Automatic M2M Translation in IIoT

Javed,

Joyia,

Sandin

et al. 2023

Preprint

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The technical capabilities of modern Industry 4.0 and Industry 5.0 are rather vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnections and communications among heterogeneous devices. Smart cities are established with sophisticated designs and control for seamless Machine-to-Machine (M2M) communication to optimize resources, costs, performances, and energy distribution. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, it encompasses quite a few challenges for devices that lack compatible and interoperable designs. Conventional solutions are restricted to limited domains or rely on engineers to design and deploy translators for each pair of ontologies. This is a costly process in terms of engineering efforts and computational resources. The issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontology meta-data and structural information. The model finds matches between two distinct ontologies using the Natural Language Processing (NLP) approach for learning linguistic contexts. Then, by visualizing the ontology network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, it can align entities of both ontology graphs similar in context and structure. Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware efforts.

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“…The applications of IIoT include Cyber Physical Systems (CPS), Machine to Machine (M2M) data exchange and translation, and many more [5]. Such applications of IIoT are referred to as Industry 5.0 [6] [7].…”

mentioning

confidence: 99%

A Blockchain Based Scalable Domain Access Control Framework for Industrial Internet of Things

Usman,

Sarfraz,

Aftab

et al. 2024

IEEE Access

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Industrial Internet of Things (IIoT) applications consist of resource constrained interconnected devices that make them vulnerable to data leak and integrity violation challenges. The mobility, dynamism, and complex structure of the network further make this issue more challenging. To control the information flow in such environments, access control is critical to make collaboration and communication safe. To deal with these challenges, recent studies employ attribute-based access control on top of blockchain technology. However, the attribute-based access control frameworks suffer due to high computational overhead. In this paper, we propose an improved role-based access control framework using hyperledger blockchain to deal with IIoT requirements with less computational overhead making the information control process more efficient and real-time. The proposed framework leverages a layered architecture of chaincodes to implement the improved access control framework that handles the permission delegation and conflict management to deal with the dynamism of the IIoT network. The system uses a Policy Contract, Device Contract, and Access Contract to manage the workflow of the whole access control process. Each chaincode in the proposed framework is isolated in terms of its responsibilities to make the design low coupled. The integration of improved access control with blockchain enables the proposed framework to provide a highly scalable solution, tamper-proof, and flexible to manage conflicting scenarios. The proposed system outperforms the recent studies significantly in computational overhead in extensive simulation results. To verify the scalability and efficiency, the proposed is evaluated against a large number of concurrent virtual clients in simulation and statistical analysis proves that the proposed system is promising for further research in this domain.

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Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms

Cited by 5 publications

References 16 publications

Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT

Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT

Deep Ontology Alignment using Natural Language Processing Approach for Automatic M2M Translation in IIoT

A Blockchain Based Scalable Domain Access Control Framework for Industrial Internet of Things

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