COMPUTATIONAL ANALYSIS OF DEEP REINFORCEMENT  LEARNING-BASED TASK SCHEDULING FOR LARGE-SCALE  INTERNET OF THINGS NETWORKS

Kumbala Pradeep Reddy,B. Narendra Kumar,S Jagadeesh,Palabatla Anish

COMPUTATIONAL ANALYSIS OF DEEP REINFORCEMENT LEARNING-BASED TASK SCHEDULING FOR LARGE-SCALE INTERNET OF THINGS NETWORKS

Authors

Kumbala Pradeep Reddy,B. Narendra Kumar,S Jagadeesh,Palabatla Anish

Keywords:

Internet of Things, Deep Reinforcement Learning, Task Scheduling, Edge Computing, Resource Management, Network Optimization, Intelligent Scheduling.

Abstract

The rapid expansion of the Internet of Things (IoT) has resulted in the deployment of billions ofinterconnected devices that continuously generate large volumes of data and computational tasks. Efficient task scheduling has become a critical requirement in large-scale IoT networks because scheduling decisions directlyinfluence system latency, resource utilization, energy consumption

References

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed., MIT Press, 2018.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Human-Level Control Through Deep Reinforcement Learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.

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Published

2025-07-15

How to Cite

Kumbala Pradeep Reddy,B. Narendra Kumar,S Jagadeesh,Palabatla Anish. (2025). COMPUTATIONAL ANALYSIS OF DEEP REINFORCEMENT LEARNING-BASED TASK SCHEDULING FOR LARGE-SCALE INTERNET OF THINGS NETWORKS . Journal of Computational Analysis and Applications (JoCAAA), 34(7), 462–470. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5552