Edge-Cloud Architectures Using UAVs Dedicated To Industrial IoT Monitoring And Control Applications

Salhaoui, Marouane; González, Antonio Guerrero; Arioua, Mounir; Molina, Juan Carlos Molina; Ortiz, Francisco; Oualkadi, Ahmed El

doi:10.1109/isaect50560.2020.9523700

Cited by 4 publications

(8 citation statements)

References 18 publications

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“…Some of the components of these systems act as independent entities that interact toward a goal. They can act as sensors that transmit data to cloud servers [16,48,55,58,62,86,101] or systems' subcomponents that hide their own data model [47,67,98,99,111].…”

Section: Data As a First Class Citizenmentioning

confidence: 99%

“…For example, systems can use API calls to collect data from data sources [10,81] or to receive requests from and send responses to end users [49] reviewed papers (i.e., 56.5%) do not adopt data coupling as data is hidden by each systems' components and these components interact using traditional process calls. They can use API calls [14,31,48,55,62,67,75,86,99,102,109] or remote procedure calls (RPC) [8,15,16,29,43,47,57,58,98,101,103,111,123,126] for communication. We found that not all the systems where components share a common data model necessarily follow the data coupling sub-principle.…”

Section: Data As a First Class Citizenmentioning

confidence: 99%

“…They have distributed devices acting as sensors, which can store some data but mainly transmit it to cloud nodes where the main processing takes place. Among these devices we found IoT sensors [9,14,16,36,48,55,75,86,102,112], smartphones [29], robots [58], and UAVs [101]. We found that 30.4% of the reviewed systems prioritise local processing before requesting centralised entities (e.g., cloud servers).…”

Section: Prioritise Decentralisationmentioning

confidence: 99%

“…Workflow orchestration tools, such as Apache Airflow, can also be used for similar purposes [22]. Fifeteen percent of the reviewed systems partially adopt the local first principle [8,29,58,101,102]. Different nodes are in charge of different stages of learning models' life-cycle in these systems.…”

Section: Prioritise Decentralisationmentioning

confidence: 99%

See 3 more Smart Citations

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

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With the upsurge of interest in artificial intelligence machine learning (ML) algorithms, originally developed in academic environments, are now being deployed as parts of real-life systems that deal with large amounts of heterogeneous, dynamic, and high-dimensional data. Deployment of ML methods in real life is prone to challenges across the whole system life-cycle from data management to systems deployment, monitoring, and maintenance. Data-Oriented Architecture (DOA) is an emerging software engineering paradigm that has the potential to mitigate these challenges by proposing a set of principles to create data-driven, loosely coupled, decentralised, and open systems. However DOA as a concept is not widespread yet, and there is no common understanding of how it can be realised in practice. This review addresses that problem by contextualising the principles that underpin the DOA paradigm through the ML system challenges. We explore the extent to which current architectures of ML-based real-world systems have implemented the DOA principles. We also formulate open research challenges and directions for further development of the DOA paradigm.

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Section: Data As a First Class Citizenmentioning

confidence: 99%

Section: Data As a First Class Citizenmentioning

confidence: 99%

Section: Prioritise Decentralisationmentioning

confidence: 99%

Section: Prioritise Decentralisationmentioning

confidence: 99%

See 2 more Smart Citations

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…• Coil radius, sagging of a steel sheet or similar positionrelated measurements by IIoT device may not give more accurate or better result in comparison to standard field sensors, but speed or torque control, crucial for maintaining continuity of the line operation is extremely time-critical and the ability to unify all key measured device under same communication platform greatly improves the chances for a timely reaction of automated control system. The aforementioned use of IIoT devices is not a novelty within the concept of a smart factory, and although traditionally IIoT devices do not belong to the SCADA system, their communication and computing capabilities transcend the standard operation an information technology (OT/IT) division within ICS and greatly influence communication protocols and cyber security [21]- [25], design patterns and architecture of the future SCADA systems [26]- [29] and shift the entire ICS toward big data, internet and cloud solutions [30], [31].…”

Section: E Industrial Iot (Iiot) and Interconnectionmentioning

confidence: 99%

SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0

2022

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Recent technological advances encompassed by the smart factory concept have fundamentally changed industrial control systems in the way they are structured and how they operate. Majority of these changes affect Supervisory Control And Data Acquisition (SCADA) systems, shifting them to a higher level of interoperability, heterogeneous networks, big data and toward internet technologies and services in general. However, this transformation does not affect all SCADA systems equally. The immediate industrial environment and controlled processes have a significant impact as well. This paper presents a holistic approach to SCADA systems implemented in continuous flow production control within the steel industry production environment. We outline the multi-layer architecture of the SCADA control framework and the aspects of interoperability and interconnection within the architecture reference models, together with the research challenges and opportunities arising from the recent rapid increasement of the industrial control systems complexity and digital transformation under the Industry 4.0 paradigm, resulting in disrupting levels of the traditional automation pyramid based on Purdue model toward a higher level of integration and interoperability enabling cross-level data exchange empowered by the Industrial Internet of Things. Furthermore, the paper addresses the problem of proprietary SCADA systems and elaborates the causal correlation between SCADA quality requirements and adoption of new technology in relation to the specific industrial environment of the steel manufacturing process.

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Smart Anomaly Detection and Monitoring of Industry 4.0 by Drones

Pensec

Espes

Dezan

2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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Edge-Cloud Architectures Using UAVs Dedicated To Industrial IoT Monitoring And Control Applications

Cited by 4 publications

References 18 publications

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0

Smart Anomaly Detection and Monitoring of Industry 4.0 by Drones

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