Deep Recurrent Neural Network (DRNN) Based Model for Energy Prediction in Wireless Sensor Network

Authors

  • Mandar K Mokashi  Research Scholar, School of Engineering and Technology, D.Y.Patil University Pune-Ambi (India) and Assistant Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, Pune (India)
  • Dr. Ninad Nore  Associate Professor, Department of Computer Engineering, School of Engineering and Technology, D.Y.Patil University Pune-Ambi (India)
  • Dr. Vinayak G. Kottawar  Associate Professor, Department of Artificial Intelligence and Data Science, D.Y.Patil College of Engineering, Akurdi, Pune (India)

Keywords:

Sensor Nodes, Deep Learning, Feed-Forward Neural Network, Energy Prediction

Abstract

The short battery life of sensor nodes makes energy utilisation a crucial aspect of wireless sensor networks (WSNs), which are utilised in a range of applications. Energy consumption continues to be a limiting problem despite current research concentrating extensively on energy-conscious apps and operating systems. once the sensor nodes have been set up. Battery replacement and recharging can be difficult or even impossible, which can lead to inaccurate lifespan predictions for sensor networks that might cost a lot of money and put the network's intended purpose at danger. The models exhibit great accuracy in estimating energy usage, allowing for the creation of WSNs that are more effective and sustainable. This method's relevance is broadened to include IoT and cyber-physical systems, providing precise energy prediction.

References

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International Journal of Scientific Research in Science and Technology

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Published

2023-04-16

Issue

Volume 10, Issue 11, March-April-2023

Section

Research Articles

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Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Mandar K Mokashi, Dr. Ninad Nore, Dr. Vinayak G. Kottawar "Deep Recurrent Neural Network (DRNN) Based Model for Energy Prediction in Wireless Sensor Network" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 11, pp.296-301, March-April-2023.