Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor Networks
In this paper, we propose a reinforcement learning (RL) based Medium Access Control (MAC) protocol with dynamic transmission range control (TRC). This protocol provides an adaptive, multi-hop, energy-efficient solution for communication in underwater sensors networks. It features a contention-based...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/10/1727 |
| _version_ | 1797550441637085184 |
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| author | Dmitrii Dugaev Zheng Peng Yu Luo Lina Pu |
| author_facet | Dmitrii Dugaev Zheng Peng Yu Luo Lina Pu |
| author_sort | Dmitrii Dugaev |
| collection | DOAJ |
| description | In this paper, we propose a reinforcement learning (RL) based Medium Access Control (MAC) protocol with dynamic transmission range control (TRC). This protocol provides an adaptive, multi-hop, energy-efficient solution for communication in underwater sensors networks. It features a contention-based TRC scheme with a reactive multi-hop transmission. The protocol has the ability to adjust to network conditions using RL-based learning algorithm. The combination of TRC and RL algorithms can hit a balance between the energy consumption and network performance. Moreover, the proposed adaptive mechanism for relay-selection provides better network utilization and energy-efficiency over time, comparing to existing solutions. Using a straightforward ALOHA-based channel access alongside “helper-relays” (intermediate nodes), the protocol is able to obtain a substantial amount of energy savings, achieving up to 90% of the theoretical “best possible” energy efficiency. In addition, the protocol shows a significant advantage in MAC layer performance, such as network throughput and end-to-end delay. |
| first_indexed | 2024-03-10T15:29:20Z |
| format | Article |
| id | doaj.art-317588e6db484875ad9f029a6d91c9a9 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T15:29:20Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-317588e6db484875ad9f029a6d91c9a92023-11-20T17:46:51ZengMDPI AGElectronics2079-92922020-10-01910172710.3390/electronics9101727Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor NetworksDmitrii Dugaev0Zheng Peng1Yu Luo2Lina Pu3Computer Science, The City University of New York -The Graduate Center, 365 5th Ave, New York, NY 10016, USAComputer Science, The City College of New York, 160 Convent Ave, New York, NY 10031, USAElectrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USAComputer Science, University of Alabama, Tuscaloosa, AL 35487, USAIn this paper, we propose a reinforcement learning (RL) based Medium Access Control (MAC) protocol with dynamic transmission range control (TRC). This protocol provides an adaptive, multi-hop, energy-efficient solution for communication in underwater sensors networks. It features a contention-based TRC scheme with a reactive multi-hop transmission. The protocol has the ability to adjust to network conditions using RL-based learning algorithm. The combination of TRC and RL algorithms can hit a balance between the energy consumption and network performance. Moreover, the proposed adaptive mechanism for relay-selection provides better network utilization and energy-efficiency over time, comparing to existing solutions. Using a straightforward ALOHA-based channel access alongside “helper-relays” (intermediate nodes), the protocol is able to obtain a substantial amount of energy savings, achieving up to 90% of the theoretical “best possible” energy efficiency. In addition, the protocol shows a significant advantage in MAC layer performance, such as network throughput and end-to-end delay.https://www.mdpi.com/2079-9292/9/10/1727underwater wireless sensor networkunderwater acoustic communicationmedium access controlreinforcement learningtransmission range control |
| spellingShingle | Dmitrii Dugaev Zheng Peng Yu Luo Lina Pu Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor Networks Electronics underwater wireless sensor network underwater acoustic communication medium access control reinforcement learning transmission range control |
| title | Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor Networks |
| title_full | Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor Networks |
| title_fullStr | Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor Networks |
| title_full_unstemmed | Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor Networks |
| title_short | Reinforcement-Learning Based Dynamic Transmission Range Adjustment in Medium Access Control for Underwater Wireless Sensor Networks |
| title_sort | reinforcement learning based dynamic transmission range adjustment in medium access control for underwater wireless sensor networks |
| topic | underwater wireless sensor network underwater acoustic communication medium access control reinforcement learning transmission range control |
| url | https://www.mdpi.com/2079-9292/9/10/1727 |
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