AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3729 |
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| author | A. Ur Rehman Laura Galluccio Giacomo Morabito |
| author_facet | A. Ur Rehman Laura Galluccio Giacomo Morabito |
| author_sort | A. Ur Rehman |
| collection | DOAJ |
| description | Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment. |
| format | Article |
| id | doaj-art-206924916f5044c6844ebcb645ab42e9 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-206924916f5044c6844ebcb645ab42e92025-08-20T03:29:43ZengMDPI AGSensors1424-82202025-06-012512372910.3390/s25123729AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor NetworksA. Ur Rehman0Laura Galluccio1Giacomo Morabito2Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), University of Catania & CNIT, Viale A. Doria 6, 95125 Catania, ItalyDipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), University of Catania & CNIT, Viale A. Doria 6, 95125 Catania, ItalyDipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), University of Catania & CNIT, Viale A. Doria 6, 95125 Catania, ItalyUnderwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment.https://www.mdpi.com/1424-8220/25/12/3729underwater communicationsenergy efficiencyCNNsoftware-defined radio systemsenvironmental sustainability |
| spellingShingle | A. Ur Rehman Laura Galluccio Giacomo Morabito AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks Sensors underwater communications energy efficiency CNN software-defined radio systems environmental sustainability |
| title | AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks |
| title_full | AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks |
| title_fullStr | AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks |
| title_full_unstemmed | AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks |
| title_short | AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks |
| title_sort | ai driven adaptive communications for energy efficient underwater acoustic sensor networks |
| topic | underwater communications energy efficiency CNN software-defined radio systems environmental sustainability |
| url | https://www.mdpi.com/1424-8220/25/12/3729 |
| work_keys_str_mv | AT aurrehman aidrivenadaptivecommunicationsforenergyefficientunderwateracousticsensornetworks AT lauragalluccio aidrivenadaptivecommunicationsforenergyefficientunderwateracousticsensornetworks AT giacomomorabito aidrivenadaptivecommunicationsforenergyefficientunderwateracousticsensornetworks |