Hu Liyazhou, Han Chao, Wang Xiaojun, Zhu Han, Ouyang Jian
School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China.
Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Sensors (Basel). 2024 Mar 21;24(6):1993. doi: 10.3390/s24061993.
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.
由于能量资源有限以及无线通信的广播特性，能量效率和安全问题是无线传感器网络（WSNs）中的主要关注点。因此，如何在提高无线传感器网络能量效率的同时增强安全性能已引起广泛关注。为了解决这个问题，本文提出了一种基于深度强化学习（DRL）的新策略，即深度网络强化（DeepNR）策略，以提高无线传感器网络的能量效率和安全性能。具体而言，所提出的深度网络强化策略通过设计深度神经网络（DNN）来近似Q值，以自适应地学习状态信息。它还设计了基于深度强化学习的多级决策，以实时学习和优化数据传输路径，最终实现网络的准确预测和决策。为了进一步提高安全性能，深度网络强化策略包括一种防御机制，可实时响应检测到的攻击，以确保网络的正常运行。此外，深度网络强化通过深度学习模型自适应地调整其策略，以应对不断变化的网络环境和攻击模式。实验结果表明，所提出的深度网络强化策略优于传统方法，网络寿命显著提高30%，网络数据吞吐量增加25%，安全措施增强20%。
