# Sensor-Aware Classifiers for Energy-Efficient Time Series Applications on IoT Devices

arXiv: https://arxiv.org/abs/2407.08715 · HTML: https://mlsanity.com/paper/2407.08715/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Classification of Time-Series Images Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1710.00886/) (2017)
- [Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference](https://mlsanity.com/paper/2206.04685/) (2022)
- [Online Human Activity Recognition using Low-Power Wearable Devices](https://mlsanity.com/paper/1808.08615/) (2018)

## Built on by (most cited first, 2 of 2)

- [MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices](https://mlsanity.com/paper/2505.10607/) (2025)
- [Adaptive Law-Based Transformation (ALT): A Lightweight Feature Representation for Time Series Classification](https://mlsanity.com/paper/2501.09217/) (2025)

## Abstract (arXiv)

Time-series data processing is an important component of many real-world applications, such as health monitoring, environmental monitoring, and digital agriculture. These applications collect distinct windows of sensor data (e.g., few seconds) and process them to assess the environment. Machine learning (ML) models are being employed in time-series applications due to their generalization abilities for classification. State-of-the-art time-series applications wait for entire sensor data window to become available before processing the data using ML algorithms, resulting in high sensor energy consumption. However, not all situations require processing full sensor window to make accurate inference. For instance, in activity recognition, sitting and standing activities can be inferred with partial windows. Using this insight, we propose to employ early exit classifiers with partial sensor windows to minimize energy consumption while maintaining accuracy. Specifically, we first utilize multiple early exits with successively increasing amount of data as they become available in a window. If early exits provide inference with high confidence, we return the label and enter low power mode for sensors. The proposed approach has potential to enable significant energy savings in time series applications. We utilize neural networks and random forest classifiers to evaluate our approach. Our evaluations with six datasets show that the proposed approach enables up to 50-60% energy savings on average without any impact on accuracy. The energy savings can enable time-series applications in remote locations with limited energy availability.