Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach
Due to the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply directly in the underwater environment. Especially for seabed topography, it is impossible to perform long-distance and accurate detection by electromagnetic waves. Therefore,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3877 |
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| author | Mohamed Ould-Elhassen Aoueileyine Hajar Bennouri Amine Berqia Pedro G. Lind Hårek Haugerud Ondrej Krejcar Ridha Bouallegue Anis Yazidi |
| author_facet | Mohamed Ould-Elhassen Aoueileyine Hajar Bennouri Amine Berqia Pedro G. Lind Hårek Haugerud Ondrej Krejcar Ridha Bouallegue Anis Yazidi |
| author_sort | Mohamed Ould-Elhassen Aoueileyine |
| collection | DOAJ |
| description | Due to the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply directly in the underwater environment. Especially for seabed topography, it is impossible to perform long-distance and accurate detection by electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have been used. Equipped with submersibles, these underwater sensors can detect a wide underwater range accurately. In addition, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. In this paper, we propose a multiagent approach for optimizing the quality of monitoring (QoM) in underwater sensor networks. Our framework aspires to optimize the QoM by resorting to the machine learning concept of <i>diversity</i>. We devise a multiagent optimization procedure which is able to both reduce the redundancy among the sensor readings and maximize the diversity in a distributed and adaptive manner. The mobile sensor positions are adjusted iteratively using a gradient type of updates. The overall framework is tested through simulations based on realistic environment conditions. The proposed approach is compared to other placement approaches and is found to achieve a higher QoM with a smaller number of sensors. |
| first_indexed | 2024-03-11T04:32:56Z |
| format | Article |
| id | doaj.art-598abe7518fc48c9a74df9ba1bdd1682 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:32:56Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-598abe7518fc48c9a74df9ba1bdd16822023-11-17T21:15:53ZengMDPI AGSensors1424-82202023-04-01238387710.3390/s23083877Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient ApproachMohamed Ould-Elhassen Aoueileyine0Hajar Bennouri1Amine Berqia2Pedro G. Lind3Hårek Haugerud4Ondrej Krejcar5Ridha Bouallegue6Anis Yazidi7Innov’COM Laboratory, Higher School of Communication of Tunis (SUPCOM), University of Carthage, Ariana 2083, TunisiaSmart Systems Laboratory (SSL), ENSIAS, Rabat IT Center, Mohammed V University, Rabat BP 713, MoroccoSmart Systems Laboratory (SSL), ENSIAS, Rabat IT Center, Mohammed V University, Rabat BP 713, MoroccoDepartment of Computer Science, OsloMet—Oslo Metropolitan University, 0176 Oslo, NorwayDepartment of Computer Science, OsloMet—Oslo Metropolitan University, 0176 Oslo, NorwayCenter for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, 500 03 Hradec Kralove, Czech RepublicInnov’COM Laboratory, Higher School of Communication of Tunis (SUPCOM), University of Carthage, Ariana 2083, TunisiaDepartment of Computer Science, OsloMet—Oslo Metropolitan University, 0176 Oslo, NorwayDue to the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply directly in the underwater environment. Especially for seabed topography, it is impossible to perform long-distance and accurate detection by electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have been used. Equipped with submersibles, these underwater sensors can detect a wide underwater range accurately. In addition, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. In this paper, we propose a multiagent approach for optimizing the quality of monitoring (QoM) in underwater sensor networks. Our framework aspires to optimize the QoM by resorting to the machine learning concept of <i>diversity</i>. We devise a multiagent optimization procedure which is able to both reduce the redundancy among the sensor readings and maximize the diversity in a distributed and adaptive manner. The mobile sensor positions are adjusted iteratively using a gradient type of updates. The overall framework is tested through simulations based on realistic environment conditions. The proposed approach is compared to other placement approaches and is found to achieve a higher QoM with a smaller number of sensors.https://www.mdpi.com/1424-8220/23/8/3877underwater communicationsquality of monitoringdiversitydetrimental point process |
| spellingShingle | Mohamed Ould-Elhassen Aoueileyine Hajar Bennouri Amine Berqia Pedro G. Lind Hårek Haugerud Ondrej Krejcar Ridha Bouallegue Anis Yazidi Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach Sensors underwater communications quality of monitoring diversity detrimental point process |
| title | Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach |
| title_full | Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach |
| title_fullStr | Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach |
| title_full_unstemmed | Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach |
| title_short | Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach |
| title_sort | quality of monitoring optimization in underwater sensor networks through a multiagent diversity based gradient approach |
| topic | underwater communications quality of monitoring diversity detrimental point process |
| url | https://www.mdpi.com/1424-8220/23/8/3877 |
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