From Cloud Governance to IoT Governance

Copie, Adrian; Fortiş, Teodor-Florin; Munteanu, Victor Ion; Negru, Viorel

doi:10.1109/waina.2013.169

Cited by 21 publications

(13 citation statements)

References 20 publications

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“…[4,12]. We have focused CPS and IoT for increased industrial autonomy, flexibility, efficiency, self-configuration, self-adaptiveness and robustness.…”

Section: Discussionmentioning

confidence: 99%

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Industry IoT Gateway for Cloud Connectivity

Zolotová

Bundzel

Lojka

2015

Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth

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Abstract. New approaches and technologies like Internet of Things (IoT), cloud computing and Big Data are giving rise to another industrial revolution. We propose here an implementation of an industrial gateway architecture adopting the idea of IoT, intelligent methods, Machine-to-Machine and Cyber-Physical Systems. The proposed gateway creates a virtual representation of the physical world scanning the technological layer's devices in real time. It creates a uniform communication interface for the heterogeneous technological layer, enables selfmanagement of devices, diagnostics and self-reconfiguration to improve Quality of Service aided with cloud SCADA and MES services. We have tested the proposed gateway in an experimental setup with a programmable logic controller.

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“…[4,12]. We have focused CPS and IoT for increased industrial autonomy, flexibility, efficiency, self-configuration, self-adaptiveness and robustness.…”

Section: Discussionmentioning

confidence: 99%

“…This solution is more suited for the world representation management when using various physical devices [9,10]. This layer must implement ad hoc behavior and it must be manageable by central or distributed nodes [11,12].…”

Section: Industry 40 and Iotmentioning

confidence: 99%

Industry IoT Gateway for Cloud Connectivity

Zolotová

Bundzel

Lojka

2015

Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth

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“…The traditional data governance practices comprising of people, process and technology (Merkus, 2015) are going through a fundamental shift or transformation phase. This can be attributed to the changes in regulations (Wachter, 2018) as well as advancement in technologies such as Big Data, Blockchain, Cloud, IoT and Mobile (Copie et al, 2013). Thus, there is a need to tailor the data governance practices in an IoT context (Al-Ruithe et al, 2016), (Porambage et al, 2016), (IERC, 2015), (Banerjee and Sheth, 2017)).…”

Section: Related Iot Data Management and Governance Frameworkmentioning

confidence: 99%

A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems

Dasgupta

Gill

Hussain

2019

Proceedings of the 8th International Conference on Data Science, Technology and Applications

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There is a growing interest in the use of Internet of Things (IoT) in information systems (IS). Data or information governance is a critical component of IoT enabled digital IS ecosystem. There is insufficient guidance available on how to effectively establish data governance for IoT enabled digital IS ecosystem. The introduction of new regulations related to privacy such as General Data Protection Regulation (GDPR) as well as existing regulations such as Health Insurance Portability and Accountability Act (HIPPA) has added complexity to this issue of data governance. This could possibly hinder the effective IoT adoption in healthcare digital IS ecosystem. This paper enhances the 4I framework, which is iteratively developed and updated using the design science research (DSR) method to address this pressing need for organizations to have a robust governance model to provide the coverage across the entire data lifecycle in IoT-enabled digital IS ecosystem. The 4I framework has four major phases: Identify, Insulate, Inspect and Improve. The application of this framework is demonstrated with the help of a Healthcare case study. It is anticipated that the proposed framework can help the practitioners to identify, insulate, inspect and improve governance of data in IoT enabled digital IS ecosystem.

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“…More details on these examples of the IoT-Cloud convergence can be found in other works. [20][21][22][23] As already said, Cloud Computing has offered its seemingly infinite elastic resources to support data-intensive tasks. As opposed to this hardware-driven approach, the research community has also offered a number of software frameworks and algorithms, specifically aiming to address the ''four Vs'' of Big Data generated in the IoT contexts -namely, Volume, Velocity, Variety, and Veracity.…”

Section: Convergence Among the Iot Cloud Computing And Big Datamentioning

confidence: 99%

Data agility through clustered edge computing and stream processing

Dautov

Distefano

Bruneo

et al. 2018

Concurrency and Computation

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The Internet of Things is underpinned by the global penetration of network-connected smart devices continuously generating extreme amounts of raw data to be processed in a timely manner. Supported by Cloud and Fog/Edge infrastructures-on the one hand, and Big Data processing techniques-on the other, existing approaches, however, primarily adopt a vertical offloading model that is heavily dependent on the underlying network bandwidth. That is, (constrained) network communication remains the main limitation to achieve truly agile IoT data management and processing. This paper aims to bridge this gap by defining Clustered Edge Computing-a new approach to enable rapid data processing at the very edge of the IoT network by clustering edge devices into fully functional decentralized ensembles, capable of workload distribution and balancing to accomplish relatively complex computational tasks. This paper also proposes ECStream Processing that implements Clustered Edge Computing using Stream Processing techniques to enable dynamic in-memory computation close to the data source. By spreading the workload among a cluster of collocated edge devices to process data in parallel, the proposed approach aims to improve performance, thereby supporting agile data management. The experimental results confirm that such a distributed in-memory approach to data processing at the very edge of an IoT network can outperform currently adopted Cloud-enabled architectures, and has the potential to address a wide range of IoT-related data-intensive time-critical scenarios. KEYWORDS cloud computing, clustered edge computing, data agility, edge computing, internet of things, stream processing 1 INTRODUCTION AND MOTIVATION In the last decade, rapid advances in networking technologies and pervasive Internet connectivity guided the development of Information and Communication Technologies (ICT) in distributed, mobile, and embedded directions. On the one hand, supported by continuously growing datacenter infrastructures, virtualization technologies, service-oriented approaches and utility models, Cloud computing has emerged as an effective paradigm, enabling Internet-based, convenient, Quality-of-Service (QoS)-guaranteed access to a shared pool of configurable computing resources. 1 On the other hand, more and more powerful devices, either static or mobile, such as smartphones, single-board computers (eg, Raspberry Pi and Arduino Yun), or smart objects (eg, home appliances, wearables, and vehicles), have been interconnected as ''things'' through the Internet, giving rise to the Internet of Things (IoT) paradigm and corresponding services and applications. There are already tens of billions connected things, which is estimated to reach hundreds of billions in the next 5-10 years. The total amount of data produced by these objects on a daily basis, measured on a scale of Zettabytes (2 70 bytes), requires corresponding technological solutions for their efficient management, usually framed into the context of the Big Data research.

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From Cloud Governance to IoT Governance

Cited by 21 publications

References 20 publications

Industry IoT Gateway for Cloud Connectivity

Industry IoT Gateway for Cloud Connectivity

A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems

Data agility through clustered edge computing and stream processing

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