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Reinforcement Learning-Based Routing in Underwater Acoustic Sensor Networks by B. S. Halakarnimath; A. V. Sutagundar is a Engineering article available to read on EtoBox.

What is Reinforcement Learning-Based Routing in Underwater Acoustic Sensor Networks about?

Underwater acoustic sensor networks (UASNs) play an essential role in exploring the marine world applications in rivers, ponds, and oceans. In recent years, prominent developments have addressed and resolved the numerous issues of critical underwater applications. Routing is one of the vital problems discussed widely, and various protocols are designed to solve the routing-related issues effectively. In this paper, we propose reinforcementlearning based routing using Steiner-points to minimize energy consumption and enhance the network lifetime of UASNs. Initially, the sensor nodes form a group of three nodes and calculate the Steiner point for each such group. The isolated nodes and the Steiner points in the network are vertices of the Steiner tree, which is the routing path to be constructed dynamically by the sink node agent to travel with the assistance of AUV. The sink node agent in the unknown environment initiates the Steiner tree construction process by visiting either the isolated neighbor node or neighbor sensor node's Steiner point to collect the data from sensor nodes based on the reward function and the environmental conditions. If agent visits the isolated node, then

Who reads Reinforcement Learning-Based Routing in Underwater Acoustic Sensor Networks?

Author

B. S. Halakarnimath; A. V. Sutagundar

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Engineering (Physical Sciences)