Analytical Study of Task Offloading Techniques using Deep Learning

Authors

  • Mr Almelu
  • Dr. S. Veenadhari
  • Kamini Maheshwar

DOI:

https://doi.org/10.24113/ijoscience.v7i7.393

Keywords:

Internet of Things (IoT), Bandwidth, Task Offloading, Deep Learning.

Abstract

The Internet of Things (IoT) systems create a large amount of sensing information. The consistency of this information is an essential problem for ensuring the quality of IoT services. The IoT data, however, generally suffers due to a variety of factors such as collisions, unstable network communication, noise, manual system closure, incomplete values and equipment failure. Due to excessive latency, bandwidth limitations, and high communication costs, transferring all IoT data to the cloud to solve the missing data problem may have a detrimental impact on network performance and service quality. As a result, the issue of missing information should be addressed as soon as feasible by offloading duties like data prediction or estimations closer to the source. As a result, the issue of incomplete information must be addressed as soon as feasible by offloading duties such as predictions or assessment to the network’s edge devices. In this work, we show how deep learning may be used to offload tasks in IoT applications.

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Author Biographies

Mr Almelu

PhD Scholar

Computer Science & Engineering

Rabindranath Tagore University

Bhopal, India

Dr. S. Veenadhari

Associate Professor

Computer Science & Engineering

Rabindranath Tagore University

Bhopal, India

 

Kamini Maheshwar

PhD Scholar

Computer Science & Engineering

Rabindranath Tagore University

Bhopal, Madhya Pradesh, India

 

References

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Published

07/01/2021

How to Cite

Almelu, M., Veenadhari, D. S. ., & Maheshwar, K. . (2021). Analytical Study of Task Offloading Techniques using Deep Learning . SMART MOVES JOURNAL IJOSCIENCE, 7(7), 1–4. https://doi.org/10.24113/ijoscience.v7i7.393

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Articles