Grid Deployment Scheme for Enhancing Network Performance in Underwater Acoustic Sensor Networks
Researchers have achieved significant progress on the problem of Sensor Node Deployment (SND) in Underwater Acoustic Sensor Networks (UASNs) in terms of enhancing underwater communication. However, most studies focus on enhancing coverage, connectivity, selective network performance metrics, and/or...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10283819/ |
| _version_ | 1797655650769043456 |
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| author | Ahmed Aljughaiman |
| author_facet | Ahmed Aljughaiman |
| author_sort | Ahmed Aljughaiman |
| collection | DOAJ |
| description | Researchers have achieved significant progress on the problem of Sensor Node Deployment (SND) in Underwater Acoustic Sensor Networks (UASNs) in terms of enhancing underwater communication. However, most studies focus on enhancing coverage, connectivity, selective network performance metrics, and/or deployment expenses at the cost of other critical factors. Given the limited resources and difficulties with using renewable energy sources to recharge UASN batteries, it is necessary to minimize Energy Consumption (EC) through the node deployment mechanism to extend network lifetime. Therefore, in this paper, the author proposes the Distributed Deployment Optimization algorithm using Grid-based Depth Adjustable (DDOGDA) based on grid node deployment for 3D network architecture. With this model, this paper endeavors to monitor the underwater environment with the minimum number of underwater nodes while meeting the QoS requirements of a tsunami-monitoring application in the Solomon Islands. The proposed algorithm considers Geographic Information System (GIS) data, non-environmental factors, and the unique characteristics of underwater sensors. These factors were used to provide guidance on how to place nodes properly to achieve certain objectives. Herein, the proposed algorithm is compared to six other node deployment algorithms. Simulation results indicate that our proposed algorithm surpasses random, tetrahedron, cuboid, triangular, pipeline, and grid node deployment in terms of End-to-End Delay (E2ED), EC, and Packet Delivery Ratio (PDR) by 266%, 183%, and 22%, respectively. |
| first_indexed | 2024-03-11T17:17:33Z |
| format | Article |
| id | doaj.art-82ac6ce0cb9d413eb3d5cd5d78f25e38 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-82ac6ce0cb9d413eb3d5cd5d78f25e382023-10-19T23:01:15ZengIEEEIEEE Access2169-35362023-01-011111297311298710.1109/ACCESS.2023.332429210283819Grid Deployment Scheme for Enhancing Network Performance in Underwater Acoustic Sensor NetworksAhmed Aljughaiman0https://orcid.org/0000-0001-9176-9453Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi ArabiaResearchers have achieved significant progress on the problem of Sensor Node Deployment (SND) in Underwater Acoustic Sensor Networks (UASNs) in terms of enhancing underwater communication. However, most studies focus on enhancing coverage, connectivity, selective network performance metrics, and/or deployment expenses at the cost of other critical factors. Given the limited resources and difficulties with using renewable energy sources to recharge UASN batteries, it is necessary to minimize Energy Consumption (EC) through the node deployment mechanism to extend network lifetime. Therefore, in this paper, the author proposes the Distributed Deployment Optimization algorithm using Grid-based Depth Adjustable (DDOGDA) based on grid node deployment for 3D network architecture. With this model, this paper endeavors to monitor the underwater environment with the minimum number of underwater nodes while meeting the QoS requirements of a tsunami-monitoring application in the Solomon Islands. The proposed algorithm considers Geographic Information System (GIS) data, non-environmental factors, and the unique characteristics of underwater sensors. These factors were used to provide guidance on how to place nodes properly to achieve certain objectives. Herein, the proposed algorithm is compared to six other node deployment algorithms. Simulation results indicate that our proposed algorithm surpasses random, tetrahedron, cuboid, triangular, pipeline, and grid node deployment in terms of End-to-End Delay (E2ED), EC, and Packet Delivery Ratio (PDR) by 266%, 183%, and 22%, respectively.https://ieeexplore.ieee.org/document/10283819/Disaster-monitoring applicationend-to-end delayenergy consumptionnetwork performancenode deploymentpacket delivery ratio |
| spellingShingle | Ahmed Aljughaiman Grid Deployment Scheme for Enhancing Network Performance in Underwater Acoustic Sensor Networks IEEE Access Disaster-monitoring application end-to-end delay energy consumption network performance node deployment packet delivery ratio |
| title | Grid Deployment Scheme for Enhancing Network Performance in Underwater Acoustic Sensor Networks |
| title_full | Grid Deployment Scheme for Enhancing Network Performance in Underwater Acoustic Sensor Networks |
| title_fullStr | Grid Deployment Scheme for Enhancing Network Performance in Underwater Acoustic Sensor Networks |
| title_full_unstemmed | Grid Deployment Scheme for Enhancing Network Performance in Underwater Acoustic Sensor Networks |
| title_short | Grid Deployment Scheme for Enhancing Network Performance in Underwater Acoustic Sensor Networks |
| title_sort | grid deployment scheme for enhancing network performance in underwater acoustic sensor networks |
| topic | Disaster-monitoring application end-to-end delay energy consumption network performance node deployment packet delivery ratio |
| url | https://ieeexplore.ieee.org/document/10283819/ |
| work_keys_str_mv | AT ahmedaljughaiman griddeploymentschemeforenhancingnetworkperformanceinunderwateracousticsensornetworks |