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Underwater Wireless Sensor Networks (UWSNs) have emerged as important tools for a wide range of applications, including environmental monitoring, marine biology, and disaster management. However, one of the major challenges in UWSNs is the energy consumption of sensor nodes, especially considering the limitations of battery life and the high cost of recharging or replacing sensors in remote underwater environments. In prior investigations, it was discovered that the Q-learning approach increases the time complexity and computing overhead. Designing routing methods for underwater sensor networks is complicated by the issue of energy constraint. Classical routing algorithms conform to the majority of distance-oriented algorithms in order to construct the optimal route, which in turn reduces the consumption of energy and overall delay. Nevertheless, a small number of seldom utilized nodes might impede network performance, resulting in a short lifespan. A large variety of algorithms have been created so far in order to extend the network's lifespan, but the packet delivery ratio is being compromised. The succeeding nodes with more residual energy are placed in a queue for the next hop. These routing approaches often impair the network's latency and life expectancy due to their emphasis on energy efficiency. To extend the lifespan of a network with limited energy consumption, an energyefficient intelligent routing protocol appropriate for underwater communication has been proposed. The proposed solution is demonstrated through simulations, showcasing its effectiveness in energy conservation, scalability, and the reduction of redundant data transmission. The limitations and potential future directions in UWSNs is also discussed here.
Keywords:
Underwater Wireless Sensor Networks, Data Routing, Energy Efficiency, Machine Learning, Routing Optimization
Cite Article:
"Enhanced Data Routing in Underwater Wireless Sensor Networks for Energy Consumption Reduction", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 12, page no.a565-a574, December-2024, Available :http://www.ijrti.org/papers/IJRTI2412061.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator