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LTO BATTERY USEFUL LIFE PREDICTION FOR ALWAYS ON EDGE AIoT BASED STRUCTURAL HEALTH MONITORING
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| Author(s) |
Ivan ARAKISTAIN, Diego ZAMORA, David GARCIA-SANCHEZ, Alberto ARMIJO |
| Abstract |
Although many high-sampling sensor systems tend to be power-hungry, critical monitoring applications require reliable battery-powered sensor nodes that can retrieve and compute data on the edge for years. With the advent of Tiny Machine Learning (TinyML), it is becoming increasingly feasible to deploy always-on inference Machine Learning models on constrained battery-powered microcontroller-based nodes. However, owing to unpredictable and dynamic energy harvesting availability conditions and the limitations of battery technology, long-term operation is still challenging. In this paper, we present a hardware and software solution for long term continuous solar operation of power-hungry wireless sensor nodes with Lithium titanate oxide (LTO) batteries. |
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| Filename: | 0264-epe2025-full-15461073.pdf |
| Filesize: | 639.5 KB |
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| Type |
Members Only |
| Date |
Last modified 2025-08-31 by System |
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