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Abstract：
Energy efficiency and security issues are the main concerns in wireless sensor networks (WSNs) because of limited energy resources and the broadcast nature of wireless communication. Therefore, how to improve the energy efficiency of WSNs while enhancing security performance has attracted widespread attention. In order to solve this problem, this paper proposes a new deep reinforcement learning (DRL)-based strategy, i.e., DeepNR strategy, to enhance the energy efficiency and security performance of WSN. Specifically, the proposed DeepNR strategy approximates the Q-value by designing a deep neural network (DNN) to adaptively learn the state information. It also designs DRL-based multi-level decision-making to learn and optimize the data transmission paths in real time, which eventually achieves accurate prediction and decision-making of the network. To further enhance security performance, the DeepNR strategy includes a defense mechanism that responds to detected attacks in real time to ensure the normal operation of the network. In addition, DeepNR adaptively adjusts its strategy to cope with changing network environments and attack patterns through deep learning models. Experimental results show that the proposed DeepNR outperforms the conventional methods, demonstrating a remarkable 30% improvement in network lifespan, a 25% increase in network data throughput, and a 20% enhancement in security measures.
摘要：
由于有限的能源和无线通信的广播性质，能源效率和安全问题是无线传感器网络（WSN）中的主要问题。因此,如何在提高无线传感器网络能效的同时增强其安全性能,引起了人们的广泛关注。为了解决这个问题,本文提出了一种新的基于深度强化学习(DRL)的策略，即，DeepNR战略，提高无线传感器网络的能效和安全性能。具体来说,所提出的DeepNR策略通过设计深度神经网络(DNN)来自适应地学习状态信息来逼近Q值。它还设计了基于DRL的多级决策，以实时学习和优化数据传输路径，最终实现对网络的准确预测和决策。为了进一步增强安全性能，DeepNR策略包括防御机制，实时响应检测到的攻击，以确保网络的正常运行。此外,DeepNR通过深度学习模型自适应调整策略，以应对不断变化的网络环境和攻击模式。实验结果表明，所提出的DeepNR优于常规方法，展示了网络寿命显著提高30%，网络数据吞吐量增加25%，安全措施提高了20%。
