IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation
arXiv:2605.27397v1 Announce Type: new 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 performanc
The proliferation of IoT devices and the increasing demand for sustainable energy solutions in wireless sensor networks drive the need for advanced optimization techniques.
Optimizing energy consumption in IoT sensors is crucial for extending device lifespans and making large-scale IoT deployments more economically and environmentally viable.
This research introduces a refined approach to data augmentation for IoT sensor energy optimization, potentially leading to more efficient and reliable wireless sensor networks.
- · IoT device manufacturers
- · Smart city developers
- · Logistics and supply chain companies
- · Agriculture technology sector
- · Companies reliant on frequent battery replacements
- · Inefficient IoT networking solutions
Improved energy efficiency for IoT sensors in wireless networks due to better data augmentation techniques.
Reduced operational costs and maintenance for large-scale IoT deployments across various industries.
Accelerated adoption of IoT in remote or resource-constrained environments, fostering new applications and services.
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Read at arXiv cs.LG