Reinforcement Learning for Adaptive Cognitive Sensor Networks-Reference-Cited by-同舟云学术

Reinforcement Learning for Adaptive Cognitive Sensor Networks

Published:2024-06-08 Issue: Volume: Page:662-671
ISSN:2581-9429
Container-title:International Journal of Advanced Research in Science, Communication and Technology
language:en
Short-container-title:IJARSCT

Author:

Nazeer Shaik ¹,Abdul Subhahan Shaik ²

Affiliation:

1. Srinivasa Ramanujan Institute of Technology (Autonomous), Anantapur, India

2. Avanthi Institute of Engineering & Technology, Hyderabad, India

Abstract

In this paper, we propose an adaptive cognitive sensor network (CSN) system utilizing reinforcement learning (RL) to optimize network performance dynamically. The RL-based system adjusts key parameters such as transmission power, channel selection, and data scheduling based on real-time environmental feedback, thereby enhancing energy efficiency, spectrum utilization, and data accuracy. A Q-learning algorithm is employed to train the RL agent, which operates under an ϵ-greedy policy to balance exploration and exploitation. Comparative analysis with traditional static and rule-based systems demonstrates significant improvements across all key performance metrics. Future enhancements are suggested, including advanced RL techniques, transfer learning, and real-world deployments, highlighting the potential of RL in transforming CSNs into more intelligent, efficient, and resilient networks

Publisher

Naksh Solutions

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