# QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices

arXiv: https://arxiv.org/abs/2104.07521 · HTML: https://mlsanity.com/paper/2104.07521/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 63 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition](https://mlsanity.com/paper/1509.08971/) (2015)
- [FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems](https://mlsanity.com/paper/1804.11147/) (2018)
- [Deeper Insights into Weight Sharing in Neural Architecture Search](https://mlsanity.com/paper/2001.01431/) (2020)
- [Improving BLE Beacon Proximity Estimation Accuracy through Bayesian Filtering](https://mlsanity.com/paper/2001.02396/) (2020)

## Built on by (most cited first, 5 of 5)

- [Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization](https://mlsanity.com/paper/2205.08069/) (2022)
- [Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2112.00654/) (2021)
- [VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization](https://mlsanity.com/paper/2302.09443/) (2023)
- [AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems](https://mlsanity.com/paper/2408.04797/) (2024)
- [A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks](https://mlsanity.com/paper/2205.08068/) (2022)

## Abstract (arXiv)

Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future. Such services are poised to reinvent the process of navigation and tracking of people and assets in a variety of indoor and subterranean environments. The growing ownership of computationally capable smartphones has laid the foundations of portable fingerprinting-based indoor localization through deep learning. However, as the demand for accurate localization increases, the computational complexity of the associated deep learning models increases as well. We present an approach for reducing the computational requirements of a deep learning-based indoor localization framework while maintaining localization accuracy targets. Our proposed methodology is deployed and validated across multiple smartphones and is shown to deliver up to 42% reduction in prediction latency and 45% reduction in prediction energy as compared to the best-known baseline deep learning-based indoor localization model.