Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture

Trakadas, Panagiotis; Νομικός, Νικόλαος; Michailidis, Emmanouel T.; Zahariadis, Theodore; Facca, Federico Michele; Breitgand, David; Rizou, Stamatia; Masip-Bruin, X.; Gkonis, Panagiotis K.

doi:10.3390/s19163591

Cited by 49 publications

(34 citation statements)

References 24 publications

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“…On the contrary, fog computing goes beyond and supports computation offloading of heavy tasks via autonomously and locally operated IIoT nodes, being close to the edge of the network, which in turns leads to decreased network traffic, improved scalability and efficiency, and enhanced security [30]. Recently, cloud/fog/edge architectures for data-intensive IoT applications, relying on efficient knowledge extraction from data existing in different areas of the decentralized hybrid clouds and within data lakes, in the form of unstructured data, were presented [31]. In the meantime, using ML at the edge can offer advanced prediction capabilities and efficient resource management given the resource-constrained nature of IoT-based devices.…”

Section: Cloud/fog/edge Architecturesmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

Self Cite

238

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Cloud/fog/edge Architecturesmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

Self Cite

238

View full text Add to dashboard Cite

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“…Clouds are hardware services offering computing, networking, and storage capacity [34]. Mostly, clouds are operated on a cloud deployment models basis [35]: public cloud, private cloud, hybrid cloud, and community cloud [36][37][38]. The cloud inherently includes the concept of cloud computing, based on the provision of services or programs stored on servers and the internet.…”

Section: Cloud Storagementioning

confidence: 99%

Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0

Vrchota

Pech

2019

Applied Sciences

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Industry 4.0 includes digital process transformation, information technology (IT) development, mobile devices, learning software, automation, and robotics, as well as intelligent sensors to collect large datasets, store, analyze, and use them in business, including simulation, virtual reality, and digital twins. The aim of the paper is to characterize the readiness of the enterprise to use Industry 4.0. In the research, a questionnaire survey was carried out on a sample of 276 enterprises mainly from the manufacturing industry. Using explorative factor analysis, the index of Industry 4.0 (VPi4) was designed to determine the level of Industry 4.0 implementation in the enterprises. The results were further verified by a statistical analysis, using Mann–Whitney test and correlation coefficients. The results indicate that the VPi4 index was consistent in terms of distribution when comparing the results on the verification sample. Its results correlate with the subjective perception of the enterprises, and different levels of the index reflect the difference in technological intensity of the industry. The VPi4 index enables the enterprises to determine their own level of current state of readiness for Industry 4.0, to better prioritize business development. The proposed solution categorizes Industry 4.0 components into a useful theoretical framework. Further research offers the possibility of applying the index in other sectors, its relation to the size of enterprises, and updating with respect to new trends in information technology.

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“…31,33 Despite the gains in terms of modularity and deployment, several issues still need further research like efficient monitoring and security. 34 Experimental works 30,35 instantiate core services in LTE networks like the Evolved Packet Core (EPC) using microservices and employ OpenAirInterface for the radio access network, being able to achieve end-to-end delay below 20 s.…”

Section: Microservices and Services In 5g Networkmentioning

confidence: 99%

Next generation of microservices for the 5G Service‐Based Architecture

et al. 2020

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The architecture for 5G core includes a Service-Based Architecture for the diverse network functions (NFs), which relies on HTTP/2 for the SBI and TCP as the underlying transport protocol. The specifications of the HTTP family is moving towards more efficient and secure protocols, which are based on UDP to assure enhanced transport but using TLS to secure the communication channel. The next generation of microservices needs to be more secure, performant and easily manageable, where HTTP/3 and containers orchestration platforms (like Kubernetes) can provide significative contributions towards such goals. Different deployment approaches can be followed for services implemented in compliance to the 5G SBA. This paper contributes with the assessment of deployment models for services in 5G networks, where NFs are implemented following traditional architectures (all the functions in a VM) or as serverless architectures (with functions distributed in containers). The performance of microservices in Kubernetes is also evaluated. The evaluation conducted also considers the employment of different versions of HTTPs to empower the service-based interfaces of 5G services. Results demonstrate performance benefits of employing HTTP/3, based on QUIC protocol, in scenarios with networks characterised by losses or delay conditions. Despite such gain, deployment in 5G networks needs to carefully consider aspects related with connection tracking mechanisms to support high volumes of requests.

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Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture

Cited by 49 publications

References 24 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0

Next generation of microservices for the 5G Service‐Based Architecture

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