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Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks.

Liyazhou Hu^1,2, Chao Han³, Xiaojun Wang²

¹School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China.

Sensors (Basel, Switzerland)

|March 28, 2024

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This study introduces DeepNR, a novel deep reinforcement learning strategy to boost energy efficiency and security in wireless sensor networks (WSNs). DeepNR significantly extends network lifespan and data throughput while improving defenses against attacks.

Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

Wireless sensor networks (WSNs) face critical challenges in energy efficiency and security due to limited resources and broadcast communication.
Existing solutions struggle to balance the competing demands of conserving energy and maintaining robust security.

Purpose of the Study:

To propose and evaluate DeepNR, a deep reinforcement learning (DRL)-based strategy to simultaneously enhance energy efficiency and security in WSNs.
To address the limitations of current methods in adaptive network management and real-time threat response.

Main Methods:

Developed DeepNR, a strategy utilizing deep neural networks (DNNs) to approximate Q-values and adaptively learn network state information.
Implemented DRL-based multi-level decision-making for real-time optimization of data transmission paths.

Keywords:

deep neural network (DNN)deep reinforcement learning (DRL)energy efficiency security wireless sensor networks (WSNs)

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Integrated a real-time defense mechanism to detect and respond to network attacks.

Main Results:

DeepNR demonstrated a 30% improvement in network lifespan compared to conventional methods.
Achieved a 25% increase in network data throughput.
Showcased a 20% enhancement in overall security measures.

Conclusions:

The proposed DeepNR strategy effectively enhances both energy efficiency and security in WSNs.
DeepNR's adaptive learning capabilities allow it to cope with dynamic network environments and evolving attack patterns.
DeepNR offers a promising solution for improving the performance and reliability of wireless sensor networks.