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The paper applies generative data augmentation, coordinating multiple generators over several rounds, to improve sampling-frequency decisions that aim to save energy on IoT sensors. The reported gains (about 7.3% average accuracy over downstream models, 8.9% over prior augmentation methods) come from benchmark datasets like the UCR Archive plus some real-world deployments, so readers should check how well they transfer beyond those settings. The core contribution is the method for allocating synthetic samples across generators and deciding when to stop augmenting, which may matter more to ML practitioners than the energy framing.
IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation
arXiv:2605.27397v2 Announce Type: replace 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 consider the information gap and the model performance are lacking. 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. Experimental results show that the IGADA-IoT improves the average accuracy of multiple downstream models by 7.28%. Compared with advanced data augmentation methods, the average accuracy is improved by 8.90%. Compared with the individual generators, the average accuracy is improved by 7.63%. Furthermore, public IoT sensor datasets from the UCR Archive and real-world deployments demonstrate the accuracy and deployment feasibility of the proposed method.
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 consider the information gap and the model performance are lacking. 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. Experimental results show that the IGADA-IoT improves the average accuracy of multiple downstream models by 7.28%. Compared with advanced data augmentation methods, the average accuracy is improved by 8.90%. Compared with the individual generators, the average accuracy is improved by 7.63%. Furthermore, public IoT sensor datasets from the UCR Archive and real-world deployments demonstrate the accuracy and deployment feasibility of the proposed method.
Machine Learning (cs.LG)
arXiv:2605.27397 [cs.LG]
(or arXiv:2605.27397v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.27397 Focus to learn more arXiv-issued DOI via DataCite
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