Data Management Model for Internet of Everything

AlSuwaidan, Lulwah

doi:10.1007/978-3-030-27192-3_26

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…There are some IoE works that have looked at challenges in sensors connection and data collection and processing. AlSuwaidan [ 49 ] adopted Cloud as a Service (CaaS) and the Fog-to-Cloud concept to overcome the challenge of integrating, storing, and migrating distributed data. Lv and Kumar [ 50 ] proposed the software definition to sensors in the 6G/IoE network, along with Software Defined Network (SDN) technology to provide better control.…”

Section: Background and Related Workmentioning

confidence: 99%

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Janbi

Katib

Albeshri

et al. 2020

Sensors

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Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

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Section: Background and Related Workmentioning

confidence: 99%

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Janbi

Katib

Albeshri

et al. 2020

Sensors

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“…Data sources refer to data that are available, and how we can collect them. In our data-driven management model for the IoE, 2 we discussed five types of data sources: services, processes, people, documents, and machines. Machines are the core source, since each part in the IoT/IoE/IIoT follows M2M machine-to-machine.…”

Section: Data Sourcesmentioning

confidence: 99%

“…1 Services such as accurate perception, monitoring, and reaction open the way for new insights and new applications. This huge extension of network services and development of information systems has promoted a new type of network, called the Internet of Things (IoT), 2 which has become an underlying network for various applications and can occur everywhere. Government sectors and large companies have invested in this new era of technology by developing various solutions.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, data management in the IoT is a middle layer between the objects and devices producing the data, and the applications accessing the data for analysis purposes and services. 3 In our previous work, 2 we proposed a novel data management model for the IoT/IoE. This model is a data-driven management model that is able to collect, manage, analyze, and preserve data.…”

Section: Introductionmentioning

confidence: 99%

“…The model has additional top-level services that enhance the performance and efficiency of managing organizational data. 2 Many efforts have been made by governments and companies to address the challenges and issues in the IIoT. Governments, on the one hand, have released national programs and authorities for developing smart manufacturers, smart cities, smart grids, and more.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The role of data management in the Industrial Internet of Things

AlSuwaidan

2020

Concurrency and Computation

Self Cite

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The rapid development of Internet technology, networking, and emergence technologies has deeply impacted manufacturing. Moreover, the massive data being generated have increased tremendously. All of these factors have supported the transformation of the traditional manufacturing paradigm toward smart manufacturing. At present, the Industrial Internet of Things (IIoT) is an underlying platform for various smart manufacturers seeking an effective and powerful structure. Oil and gas is an industry affected by this transformation. This article discusses the role of data management in supporting the IIoT and smart manufacturing. It also proposes a taxonomy that classifies and categorizes the pillars of data management in the IIoT. It includes the basis of important parameters such as data sources, machine learning, performance management, business intelligent, and big data. In order to measure the role of data management in the IIoT, we consider Saudi Arabia as a case study for the IIoT, and review and explore the current challenges and potential future solutions. In addition, we propose an Internet of Things (IoT)-based reliable architecture for monitoring the flow of operations in the oil and gas industry.

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Scenarios for the Use of Master Data Management in the Context of the Internet of Things (IoT)

Krause

Becker

2021

Lecture Notes in Information Systems and Organisation

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Data Management Model for Internet of Everything

Cited by 6 publications

References 29 publications

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

The role of data management in the Industrial Internet of Things

Scenarios for the Use of Master Data Management in the Context of the Internet of Things (IoT)

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