IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation

IGADA-IoT：由自动数据增强驱动的无线传感器网络中物联网传感器能量优化

Abstract: In wireless sensor networks (WSNs), data augmentation is a novel method to improve sampling-frequency decision performance, thereby enabling energy optimization for IoT (Internet of Things) sensors. However, existing methods rely on a single generator and empirically determined quantities, failing to establish a mapping between dynamic information gaps and multiple generators, and overlooking the heterogeneity of generated samples. Moreover, an evaluation and a closed-loop method that jointly considers the information gap and the model performance are lacking.

摘要： 在无线传感器网络（WSNs）中，数据增强是一种提高采样频率决策性能的新颖方法，从而实现物联网（IoT）传感器的能量优化。然而，现有方法依赖于单一生成器和经验确定的数量，未能建立动态信息差距与多个生成器之间的映射，且忽视了生成样本的异构性。此外，目前还缺乏一种综合考虑信息差距和模型性能的评估及闭环方法。

To address these issues, we propose an information gap-guided IoT sensor automatic data augmentation framework (IGADA-IoT) with hierarchical multi-generator collaboration and scheduling over multiple rounds. Capabilities of different generators are jointly utilized to reduce the information gaps. In the IGADA-IoT, a hierarchical multi-generator collaboration and scheduling strategy (HMGCS) is proposed to enhance the targetedness and rationality of generated sample allocation. An information gap-model performance joint evaluation and closed-loop method (IGMP-EC) is proposed to enhance the accuracy of augmentation decisions, and to mitigate the risks of under-augmentation and over-augmentation.

为了解决这些问题，我们提出了一种信息差距引导的物联网传感器自动数据增强框架（IGADA-IoT），该框架通过多轮分层多生成器协作与调度来实现。不同生成器的能力被联合利用以缩小信息差距。在 IGADA-IoT 中，提出了一种分层多生成器协作与调度策略（HMGCS），以增强生成样本分配的针对性和合理性。同时，提出了一种信息差距-模型性能联合评估与闭环方法（IGMP-EC），以提高增强决策的准确性，并降低欠增强和过增强的风险。

Experimental results show that the IGADA-IoT improves the average accuracy of multiple downstream models by 7.27%. Compared with advanced data augmentation methods, the average accuracy is improved by 8.67%. Compared with the individual generators, the average accuracy is improved by 7.24%. Furthermore, public IoT sensor datasets from the UCR Archive and real-world deployments demonstrate the accuracy and generalizability of the proposed method.

实验结果表明，IGADA-IoT 将多个下游模型的平均准确率提高了 7.27%。与先进的数据增强方法相比，平均准确率提高了 8.67%。与单一生成器相比，平均准确率提高了 7.24%。此外，来自 UCR 档案的公共物联网传感器数据集和实际部署验证了该方法的准确性和泛化能力。