Summary of Sensor-aware Classifiers For Energy-efficient Time Series Applications on Iot Devices, by Dina Hussein et al.
Sensor-Aware Classifiers for Energy-Efficient Time Series Applications on IoT Devices
by Dina Hussein, Lubah Nelson, Ganapati Bhat
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models are being used to analyze time-series data, such as sensor readings from health monitoring systems or environmental sensors. However, processing entire windows of data at once can be energy-intensive and may not be necessary for all tasks. For example, in activity recognition, it’s possible to infer whether someone is sitting or standing using only partial data. This paper proposes a new approach that uses “early exit classifiers” to make predictions with partial data, reducing the need for full sensor windows and minimizing energy consumption. The authors use neural networks and random forest classifiers to evaluate their approach on six datasets, achieving an average of 50-60% energy savings without sacrificing accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is helping us analyze important data from sensors that track things like our health or the environment. This data comes in small chunks, but we often process it all at once to get answers. But sometimes, we don’t need to look at everything to figure out what’s going on. For example, if we want to know if someone is sitting or standing, we can do that with just a little bit of data. This new approach uses this idea to save energy and still get good results. It looks like it could be very useful for things like tracking health in places where there isn’t much power. |
Keywords
» Artificial intelligence » Activity recognition » Machine learning » Random forest » Time series » Tracking
