Network Selection over Heterogeneous Health System Using Deep Reinforcement Learning

Authors

  • Kumaran K., Sivasakthi P., Pandiyan G., Saranya G., Kirubakaran S., Nithish V.

Keywords:

Heterogeneous mobile nodes, Deep Reinforcement Learning, Random Forest (RF), Decision Tree (DT) Energy efficient routing protocol (EERPS), Grey Wolf Optimizer (GWO)

Abstract

The proposed method involves using a combination of techniques such as energy-efficient routing, heterogeneous mobile nodes, region-based grid formation, GWO optimization, LSTM prediction, ESPIRIT estimation, and active zones to minimize the overall energy consumption. It works by first randomly deploying mobile nodes throughout the experimental region. Next, the area is split up into grids according to certain parameters such as remaining energy levels and distances between nodes. Every grid has a base node that is chosen using GWO optimization. Next, a deep learning-based machine learning algorithm known as LSTM is employed to predict the future direction of movement of each mobile node within the grid. Finally, the sender node selects the closest node in the active region to transmit data to. Sensor networks are designed to facilitate efficient information sharing by allowing for the distribution of resources and information across a decentralized network. Ad-hoc networks, in particular, have gained popularity due to their ability to operate in dynamic environments where traditional wired connections may be impractical or impossible. In order to effectively communicate within these networks, various protocols have been developed to manage network traffic and ensure reliable delivery of messages. One example of such a protocol is the Dynamic Source Routing (DSR) algorithm, which uses a combination of flooding and source routing techniques to efficiently route packets through the network.

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Published

24.03.2024

How to Cite

Pandiyan G., Saranya G., Kirubakaran S., Nithish V., K. K. S. P. . (2024). Network Selection over Heterogeneous Health System Using Deep Reinforcement Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 1391–1399. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5530

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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