IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation
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Computer Science > Machine Learning
arXiv:2605.27397 (cs)
[Submitted on 22 Apr 2026]
Title:IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation
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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. 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 7.27%. 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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.27397 [cs.LG] |
| (or arXiv:2605.27397v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27397
arXiv-issued DOI via DataCite
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