The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

中文翻译：

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鲁棒网络：物联网的动态拓扑演化学习

物联网 (IoT) 已广泛应用于智慧城市。然而，随着组网规模的不断扩大，网络中部分节点的故障严重影响了物联网应用的通信能力。因此，对于需要高质量服务（QoS）的应用程序，研究人员关注提高由网络故障引起的通信容量。此外，网络拓扑的鲁棒性是衡量网络通信能力和抵抗某些故障节点引起的网络攻击能力的重要指标。虽然已经提出了一些算法来增强物联网拓扑的鲁棒性，但它们的特点是计算开销大，并且缺乏轻量级的拓扑优化模型。为了解决这个问题，我们首先提出了一种使用进化学习（ROEL）和神经网络的新型鲁棒性优化。ROEL动态优化物联网拓扑，智能预测进化优化过程中的鲁棒度。实验结果表明，ROEL可以代表物联网拓扑的演化过程，网络鲁棒性的预测精度令人满意，误差率较小。与其他算法相比，我们的算法在抵抗随机攻击和恶意攻击方面具有更好的容忍能力。实验结果表明，ROEL可以代表物联网拓扑的演化过程，网络鲁棒性的预测精度令人满意，误差率较小。与其他算法相比，我们的算法在抵抗随机攻击和恶意攻击方面具有更好的容忍能力。实验结果表明，ROEL可以代表物联网拓扑的演化过程，网络鲁棒性的预测精度令人满意，误差率较小。与其他算法相比，我们的算法在抵抗随机攻击和恶意攻击方面具有更好的容忍能力。