Reinforcement Q-Learning-Based Adaptive Encryption Model for Cyberthreat Mitigation in Wireless Sensor Networks

Reinforcement Q-Learning-Based Adaptive Encryption Model for Cyberthreat Mitigation in Wireless Sensor Networks

The increasing prevalence of cyber threats in wireless sensor networks (WSNs) necessitates adaptive and efficient security mechanisms to ensure robust data transmission while addressing resource constraints. This paper proposes a reinforcement learning-based adaptive encryption framework that dynami...

Full description

Saved in:
Bibliographic Details
Main Authors: Sreeja Balachandran Nair Premakumari, Gopikrishnan Sundaram, Marco Rivera, Patrick Wheeler, Ricardo E. Pérez Guzmán
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Sensors
Subjects:
adaptive encryption
energy efficiency
Q-learning
real-time threat detection
reinforcement learning
resource-constrained networks
Online Access:https://www.mdpi.com/1424-8220/25/7/2056
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items