A Novel Energy Adaptive Neural Network and Deep Q-Learning Network for Improved Energy Efficiency in Dynamic Underwater IoT Environment
Optimizing energy efficiency and communication reliability is essential for an underwater Internet of Things (IoT) network that utilizes hybrid optical-acoustic communication system. The proposed research work has an objective to reduce the energy consumption in a dynamic underwater IoT network by a...
Saved in:
| Main Authors: | Judy Simon, Nellore Kapileswar, Anoop Mohanakumar |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11121175/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MM-LoRa-Mod: A non-coherent LoRa modulation scheme for underwater acoustic communications
by: YU Xiao, et al.
Published: (2024-09-01) -
Acoustic Sensors data transmission integrity and endurance with IoT-enabled location-aware framework
by: Shujaat Ali, et al.
Published: (2024-12-01) -
A comprehensive review of energy harvesting and routing strategies for IoT sensors sustainability and communication technology
by: Hesam Nejati Sharif Aldin, et al.
Published: (2024-01-01) -
Novel and Optimized Efficient Transmission Using Dynamic Routing Technique for Underwater Acoustic Sensor Networks
by: Swapna Babu, et al.
Published: (2023-12-01) -
Smart Underwater Sensor Network GPRS Architecture for Marine Environments
by: Blanca Esther Carvajal-Gámez, et al.
Published: (2025-05-01)