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Communication Dans Un Congrès Année : 2022

An Extended Modeling Approach for Marine/Deep-Sea Observatory

Résumé

A Sensor Network (S.N.) is responsible for performing two main activities: (1) observation/measurement, which means accumulating data collected at each sensor node; (2) transferring the collected data to processing centers (e.g., Smart Sensors, Smart Fusion Servers) within the S.N. The infrastructure of Marine/Deep-sea Observatory is an Underwater Sensor Networks (UW-SN) to perform collaborative monitoring tasks over a given ocean/sea area. This observation should consider the environmental constraints since it may require specific logical and physical components. The physical ones could be specific tools, materials, and devices such as marine cables, servers, etc.). As for the logical ones, specific algorithms could validate the implementation phase early, such as validating the allowable entered bandwidth ranges of underwater acoustic channels. This paper presents our approach in extending the modeling languages to include new domain-specific concepts and constraints. Thus, we propose an extended meta-model that is used to generate a new design tool that contains the new constraints. We illustrate our proposal with an example from the Marine Observatory (MO) domain on object localization with several acoustics sensors. Additionally, we generate the corresponding simulation code for a standard network simulator using our self-developed domain-specific model compiler. Our approach helps to reduce the complexity and time of the design activity of a Marine Observatory. It provides a way to share the different viewpoints of the designers in the MO domain and obtain simulation results to estimate the network capabilities. The major improvement is to provide an early validation step via models and a simulation approach to consolidate the system design.
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Dates et versions

hal-03687138 , version 1 (03-06-2022)

Identifiants

Citer

Charbel Geryes Aoun, Loïc Lagadec, Mohammad Habes. An Extended Modeling Approach for Marine/Deep-Sea Observatory. The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022), May 2022, The Caire, Egypt. pp.502-514, ⟨10.1007/978-3-031-03918-8_42⟩. ⟨hal-03687138⟩
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