A mapping approach for Marine Observatory relying on enterprise architecture

Aoun, Charbel Geryes; Alloush, Iyas; Kermarrec, Yvon; Champeau, Joël; Zein, Oussama Kassem

doi:10.23919/oceans.2015.7404633

Cited by 3 publications

(9 citation statements)

References 21 publications

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“…The extended ArchiMO represents the specificity of the SSN domain in terms of certain UECs that are required for the proper deployment of USSNs to ensure its appropriate performance and functioning. Regarding the generation process of a new version for our ArchiMO, it is similar to what we conducted in our previous research in [ 2 , 3 , 16 ], which will be discussed in the contribution section.…”

Section: Introductionmentioning

confidence: 92%

“…In this section, we present our contribution to extend the ArchiMO meta-model that we previously developed and published in [ 2 , 3 , 16 ]. Our previous contribution ArchiMO is presented in ( Figure 3 and Figure 4 ).…”

Section: Contributionmentioning

confidence: 99%

“…The defined model is then used to run various error checks and automatically produce simulation code for NS-3 using a model compiler (see to Section 6.4 ) that we have created in [ 2 , 3 ], and [ 21 ]. A standard and classical networking simulator, the NS-3 tool, is used to run this simulation code.…”

Section: Underwater Object Localization Service Case Study Relying On...mentioning

confidence: 99%

“… The network topology for the underwater object (dolphin) localization example is represented by a snapshot taken from the NS-3 simulator’s Net Animator tool [ 2 ]. …”

Section: Figurementioning

confidence: 99%

“…The Intelligent Internet of Underwater Things (IIoUT) is proposed for sensing, collecting, and storing underwater information [ 1 ]. IIoUT is an application based on Underwater Smart Sensor Networks (USSNs) since it enhances deep sea monitoring; the tracking of various aquatic creatures; and ocean monitoring and observation systems, which are based on acoustic communication [ 2 , 3 ]. Logically, USSNs operate in the same manner as Smart Sensor Networks (SSN) that are not submerged in water, providing the same services, such as deep sea/underwater monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

Aoun,

Mansour,

Dornaika

et al. 2024

Sensors

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Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices’ hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems’ development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity’s complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs.

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Section: Introductionmentioning

confidence: 92%

Section: Contributionmentioning

confidence: 99%

Section: Underwater Object Localization Service Case Study Relying On...mentioning

confidence: 99%

“… The network topology for the underwater object (dolphin) localization example is represented by a snapshot taken from the NS-3 simulator’s Net Animator tool [ 2 ]. …”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

Aoun,

Mansour,

Dornaika

et al. 2024

Sensors

Self Cite

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Recent Trends in Underwater Wireless Sensor Networks (UWSNs) – A Systematic Literature Review

Tariq

Azam

Anwar

et al. 2020

Program Comput Soft

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Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

AOUN,

MANSOUR,

DORNAIKA

et al. 2024

Preprint

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Through the use of Smart Sensor Networks (SSN), Marine Observatories (MO) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of SSN devices’ hardware and software when combined with the Internet. Hence, SSN are regarded as complex distributed systems. As such, SSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed SSN. To this end, we describe in this article how we extended our previously proposed ArchiMO meta-model and design tool by extending an Enterprise Architecture Framework based on the ArchiMate Modeling Language. This extension proposes the incorporation of new Underwater Environmental Constraints (UEC) for SSN in ArchiMO which is coupled with the fusion of sensors with Artificial Intelligence. This serves as the basis for generating a new version of our ArchiMO design tool, which incorporates the new added constraints and invokes the AI created Database. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to handle and control the uncertainties and variances of the provided quality of service that may occur during the performance of the SSN, hence reducing the design activity’s complexity and time. It provides a way to share the different viewpoints of the designers in the domain of SSN.

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A mapping approach for Marine Observatory relying on enterprise architecture

Cited by 3 publications

References 21 publications

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

Recent Trends in Underwater Wireless Sensor Networks (UWSNs) – A Systematic Literature Review

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

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