Edge Computing architecture to support Real Time Analytic applications : A State-of-the-art within the application area of Smart Factory and Industry 4.0

Trinks, Sebastian; Felden, Carsten

doi:10.1109/bigdata.2018.8622649

Cited by 38 publications

(14 citation statements)

References 35 publications

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“…The concepts of edge and fog can dramatically reduce the latency by bringing the computing nodes closer to the data sources [41]. Whilst this approach perfectly matches the demands for rapid processing of large amounts of data of some emerging use cases, such as smart health, co-operative intelligent transport systems and Industry 4.0 [38], it also introduces a new set of challenges related to the end-to-end orchestration of resources, i.e., from the "things" to the cloud.…”

Section: Background and Related Workmentioning

confidence: 95%

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The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption

Tusa

Clayman

2021

J Grid Computing

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Edge microservice applications are becoming a viable solution for the execution of real-time IoT analytics, due to their rapid response and reduced latency. With Edge Computing, unlike the central Cloud, the amount of available resource is constrained and the computation that can be undertaken is also limited. Microservices are not standalone, they are devised as a set of cooperating tasks that are fed data over the network through specific APIs. The cost of processing these feeds of data in real-time, especially for massive IoT configurations, is however generally overlooked. In this work we evaluate the cost of dealing with thousands of sensors sending data to the edge with the commonly used encoding of JSON over REST interfaces, and compare this to other mechanisms that use binary encodings as well as streaming interfaces. The choice has a big impact on the microservice implementation, as a wrong selection can lead to excessive resource consumption, because using a less efficient encoding and transport mechanism results in much higher resource requirements, even to do an identical job.

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Section: Background and Related Workmentioning

confidence: 95%

“…The edge has been gaining a consensus as one of the most viable approaches for the execution of realtime IoT analytics tasks [38]. It can help reduce the processing latency as data can be processed closer to the source instead of being sent to the central cloud.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption

Tusa

Clayman

2021

J Grid Computing

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“…It is poised to bring revolutionary changes to realize the vision of the nextgeneration industrial revolution (known as Industry 4.0) [63]. With massive numbers of smart sensors and actuators interconnected and integrated into industrial systems, the M2M communication and the cloud/edge intelligence play a key role in enabling intelligent manufacturing [64] [65]. While IIoT has become an increasingly important area where security protection would be extremely critical [66], the attacks and defenses discussed in our survey could be applied to systems with physical interfaces including IIoT.…”

Section: B Cyber-physical World Interfacingmentioning

confidence: 99%

Security and Privacy in the Emerging Cyber-Physical World: A Survey

Kaplan

Yan

et al. 2021

IEEE Commun. Surv. Tutorials

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With the emergence of low-cost smart and connected IoT devices, the area of cyber-physical security is becoming increasingly important. Past research has demonstrated new threat vectors targeting the transition process between the cyber and physical domains, where the attacker exploits the sensing system as an attack surface for signal injection or extraction of private information. Recently, there have been attempts to characterize an abstracted model for signal injection, but they primarily focus on the path of signal processing. This paper aims to systematize the existing research on security and privacy problems arising from the interaction of cyber world and physical world, with the context of broad CPS applications. The primary goals of the systematization are to (1) reveal the attack patterns and extract a general attack model of existing work, (2) understand possible new attacks, and (3) motivate development of defenses against the emerging cyber-physical threats.

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“…To this end, the literature has proposed asynchronous communications protocols, such as the Advanced Message Queuing Protocol (AMQP) (Vinoski, 2006) and the Message Queuing Telemetry Transport (MQTT) (MQTT, 2019), as well as scalable architectures, such as Lambda and Kappa Architecture (Davoudian and Liu, 2020). Moreover, the edge computing paradigm has emerged, aiming at addressing networking and computing challenges that cannot be met by existing cloud computing infrastructure (Trinks and Felden, 2018;Varghese and Buyya, 2018). Further, the fog computing aims at leveraging the existing computing resources on edge nodes or integrating additional computing capability between user devices and cloud data centers (Varghese and Buyya, 2018;Papageorgiou et al, 2019).…”

Section: Big Data Processingmentioning

confidence: 99%

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Bousdekis

Mentzas

2021

Front. Big Data

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Traditional manufacturing businesses lack the standards, skills, processes, and technologies to meet today's challenges of Industry 4.0 driven by an interconnected world. Enterprise Integration and Interoperability can ensure efficient communication among various services driven by big data. However, the data management challenges affect not only the technical implementation of software solutions but the function of the whole organization. In this paper, we bring together Enterprise Integration and Interoperability, Big Data Processing, and Industry 4.0 in order to identify synergies that have the potential to enable the so-called “Fourth Industrial Revolution.” On this basis, we propose an architectural framework for designing and modeling Industry 4.0 solutions for big data-driven manufacturing operations. We demonstrate the applicability of the proposed framework through its instantiation to predictive maintenance, a manufacturing function that increasingly concerns manufacturers due to the high costs, safety issues, and complexity of its application.

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Edge Computing architecture to support Real Time Analytic applications : A State-of-the-art within the application area of Smart Factory and Industry 4.0

Cited by 38 publications

References 35 publications

The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption

The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption

Security and Privacy in the Emerging Cyber-Physical World: A Survey

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

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