A Big Data Security Layer Meta-Model Proposition

Erraissi, Allae; Belangour, Abdessamad

doi:10.25046/aj040553

Cited by 13 publications

(4 citation statements)

References 38 publications

(39 reference statements)

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“…The IoT is responsible for the collection and/or creation of a large volume of data. This enormous volume of data, known by the term "Big Data" [34,35], allows, on the one hand, to have an incredible wealth in terms of information allowing the offer of advanced services. On the other hand, this volume of data creates new challenges to be considered such as securing, processing, and real-time accessibility of this data [36,37].…”

Section: A Iotmentioning

confidence: 99%

A Comparative Study of Microservices-based IoT Platforms

Khalyly¹,

Belangour²,

Banane³

et al. 2020

IJACSA

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Internet of Things (IoT) is a set of technologies that aim at fitting together smart devices and applications to build an IoT ecosystem. The target of this kind of ecosystem is to enhance interaction between machines and humans through hardware to software binding while reducing cost and resource consumption. On the application level, IoT ecosystems were implemented by various technologies that all seek better interconnection, monitoring, and controlling of IoT smart devices. Among recent technologies, Microservices, which are a variant of the serviceoriented architecture, are subject to great excitement. In fact, Microservices are an emerging technology built around Microservice paradigm which goal is to offer services with a small granularity, which exactly meets the distributed nature of IoT devices while maintaining a loosely coupled architecture between IoT components among other advantages. Efforts to build Microservice-based IoT platforms sooner emerged to take advantage of the numerous benefits of the Microservice paradigm to build scalable, interoperable, and dynamic ecosystems. The goal of this paper is to list these approaches, classify them and compare them using a Weighted Scoring Model (WSM) method. This involves, studying these platforms, establishing relevant criteria for comparison, assigning weights for each criterion, and finally calculating scores. The obtained results reveal the weaknesses and strengths of each of the studied platforms.

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Section: A Iotmentioning

confidence: 99%

A Comparative Study of Microservices-based IoT Platforms

Khalyly¹,

Belangour²,

Banane³

et al. 2020

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…The IoT is responsible for the collection and / or creation of a large volume of data. This enormous volume of data, known by the term "Big Data" [38,39,57,58], allows, on the one hand, to have an incredible wealth in terms of information allowing the offer of advanced services. On the other hand, this volume of data creates new challenges to be considered such as securing, processing, and real-time accessibility of this data [40,41].…”

Section: Related Workmentioning

confidence: 99%

“…It orchestrates the computing, networking and storage infrastructure on the users' workloads. This Meta-model can be used in different domains [16,17,18]. The fourth section talks about improving the Internet of Things Meta-model.…”

Section: Introductionmentioning

confidence: 99%

Devops and Microservices Based Internet of Things Meta-Model

2020

IJETER

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The ecosystem of the Internet of Things is a set of physical devices such as sensors and actuators. It includes a set of servers and gateways that provide connectivity. These devices can be installed at three levels: the edge level, the fog level, and the cloud level. Applications that monitor things and collect data from sensors are deployed at the edge, fog, and cloud levels. These applications can be containerized and deployed in different devices by Docker, which takes advantage of its advantage to create executable containers that are isolated from each other. This paper presents a generic Meta-model of the Internet of Things ecosystem based on microservices and supported by Docker as a containerization tool, Ansible as a monitoring tool and Kubernetes as scaling tool. This Meta-model allows us to generate a system of connected objects that can be deployed on three levels: Fog, Edge, Cloud. This Meta-model can be used in different types of domains such as Smart Home -Smart City -Smart Vehicle -Smart Health -Smart Farm -Smart Factory. The Meta-model proposed in this article is a fusion between 5 Meta-models: Internet of Things -Microservices -Ansible -Docker -Kubernetes.

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“…We now present extracts from the ATL code that we used to transform the meta-models proposed for Predictive methodology to the meta-model proposed for Agile methodology [32,33]. These defined meta-models present the PIM (Platform Independent Model) level according to the architecture led by the 'MDA' models [24,25,35,36].…”

Section: Transformationmentioning

confidence: 99%

MDA Transformation Process from predictive project management methodologies to agile project management methodologies

2020

IJETER

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Project management models are constantly evolving over time, among the most widely answered models we find Agile models or else called "Adaptive" and "Predictive" models. A study by the Standish group confirms that Agile projects achieve the desired result three times more than projects that are carried out by conventional (predictive) Methodologies. Thus, many organizations tend to apply the new project management model or migrate from the standard model to the agile one. In this article, building on previous research, we'll build on the principles of MDA to define a metamodel of Agile and Predictive Methodologies, and then we'll effect a transformation that will help organizations transform project management.

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A Big Data Security Layer Meta-Model Proposition

Cited by 13 publications

References 38 publications

A Comparative Study of Microservices-based IoT Platforms

A Comparative Study of Microservices-based IoT Platforms

Devops and Microservices Based Internet of Things Meta-Model

MDA Transformation Process from predictive project management methodologies to agile project management methodologies

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