The role of data management in the Industrial Internet of Things

AlSuwaidan, Lulwah

doi:10.1002/cpe.6031

Cited by 12 publications

(5 citation statements)

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“…AlSuwaidan 27 investigated the IoT and the role of data management. The role of data management and intelligent production in IoT and its challenges such as performance management, big data, data source, and so on are investigated in this study.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the IoT's importance in many fields, further research is still needed to improve its performance, particularly in IoT data management. Several articles and technical reviews are not systematically focused on IoT data management 5,7,14,18,20–28 . These review articles have considered no effective analytical evaluation for data management in the IoT.…”

Section: Introductionmentioning

confidence: 99%

“…Several articles and technical reviews are not systematically focused on IoT data management. 5,7,14,18,[20][21][22][23][24][25][26][27][28] These review articles have considered no effective analytical evaluation for data management in the IoT. Furthermore, several studies have not examined the essential criteria for evaluating data management in the IoT, and the review studies presented lack a systematic organization.…”

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confidence: 99%

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Internet of Things data management: A systematic literature review, vision, and future trends

Shirvanian

Shams

Rahmani

2022

Int J Communication

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The Internet of Things (IoT) is expected to connect devices with unique identifiers over a network to create an equilibrium system with high speeds and volumes of data while presenting an interoperability challenge. The IoT data management system is indispensable for attaining effective and efficient performance because IoT sensors generate and collect large amounts of data used to express large data sets. IoT data management has been analyzed from various perspectives in numerous studies. In this study, a Systematic Literature Review (SLR) method was used to investigate the various topics and key areas that have recently emerged in IoT data management. This study aims to classify and evaluate studies published between 2015 and 2021 in IoT data management. Therefore, the classification of studies includes five categories, data processing, data smartness application, data collection, data security, and data storage. Then, studies in each field are compared based on the proposed classification. Each study investigates novel findings, simulation/implementation, data set, application domain, experimental results, advantages, and disadvantages. In addition, the criteria for evaluating selected articles for each domain of IoT data management are examined. Big data accounts for the highest percentage of data processing fields in IoT data management, at 34%. In addition, fast data processing, distributed data, artificial intelligence data with 22%, and data uncertainty analysis account for 11% of the data processing field. Finally, studies highlight the challenges of IoT data management and its future directions.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Internet of Things data management: A systematic literature review, vision, and future trends

Shirvanian

Shams

Rahmani

2022

Int J Communication

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“…In recent years, machine learning has attracted considerable attention from the oil and gas industry [7]. The annual number of machine learning research papers in the industry reaches several hundreds and is rising exponentially [24].…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Vuttipittayamongkol¹,

Tung

Elyan

2021

IEEE Access

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A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders' concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.

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“…AlSuwaidan 9 carried out an investigation into the role of data management solutions for Industrial Internet of Things (IIoT) which is an underlying platform for various smart manufacturers seeking effective and powerful structures. This article discussed the role of data management in supporting the IIoT and smart manufacturing and proposed a taxonomy to classify and categorize various important parameters such as data sources, machine learning, performance management, business intelligent, and big data.…”

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confidence: 99%

Convergence of cloud, Internet of Things, and big data: New platforms and applications

Awan

Younas

2021

Concurrency and Computation

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Cloud, Internet of Things (IoT), and big data have not only become the fundamental technologies and platforms in computing and network communication, but they have greater potential in a wide range of other areas such as business, finance, transportation, healthcare, social media, and engineering, among others. Cloud computing provides a large pool of compute and storage resources and enables transparent access to such resources through the Internet. IoT is used to connect physical devices through the Internet that can generate large volume of (big) data. For instance, IoT can connect most modern devices such as smart phones, smart TVs, traffic and weather sensors, and other smart digital devices that generate large volumes of the data. Thus cloud, IoT, and big data can be used together to offer new and exciting services in the aforementioned areas.This special issue has been organized to solicit papers via an open call as well as selected papers from the 7th International Conference on Future Internet of Things and Cloud (FiCloud-2019), IEEE TCI, and the 5th International Conference on Big Data Innovations and Applications (Innovate-Data 2019), Springer, which were held during August 26-28, 2019, in Istanbul, Turkey. The conferences were attended by a large number of participants from different countries across the world. The conferences featured a number of technical sessions of research papers, industry talks, and keynote talks. A number of workshops and symposia were also organized alongside the main conferences. THEMES OF THE SPECIAL ISSUEThis special issue aims to foster the dissemination of high-quality research with new ideas, approaches, theories and practice to solve the challenging issues in the design and development of new platforms and application areas related to the convergence of cloud, IoT, and big data. Some of the cutting-edge topics covered by the research articles in this special issue include: the design and development of new models and architectures, analysis and evaluation of security, privacy and trust models, performance evaluation, and data mining techniques.A number of papers were submitted to this special issue and each was reviewed by multiple referees. Based on the reviews, 13 papers were accepted for this special issue. The work presented in these papers is summarized as follow.Stavrinides and Karatza 1 highlighted the significance of supplementary resources from public cloud to effectively handle the increasing IoT workflow jobs in a fog environment. As the cloud involves higher data transfer latency and monetary cost, the proposed approach takes into account these two factors, in addition to the real-time constraints of the workload. The proposed scheduling heuristic is based on the trade-off between performance and monetary cost. The proposed scheduling strategy is compared against a baseline policy that utilizes only the fog resources. The simulation experiments have been carried out under varying sizes of workflow input data and for workloads with soft and hard deadlines.Bar...

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The role of data management in the Industrial Internet of Things

Cited by 12 publications

References 68 publications

Internet of Things data management: A systematic literature review, vision, and future trends

Internet of Things data management: A systematic literature review, vision, and future trends

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Convergence of cloud, Internet of Things, and big data: New platforms and applications

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