Long Sequence Decoder Network for Mobile Sensing
Jiazhong Mei, J. Nathan Kutz

TL;DR
This paper introduces a robust deep learning model combining structured state space sequence (S4D) with a novel Butterworth filter initialization to improve reconstruction of long, noisy spatio-temporal sensor data from mobile sensors.
Contribution
The paper proposes a new robust S4D model with a filtering initialization for better long-sequence and noise handling in mobile sensing data reconstruction.
Findings
Outperforms previous methods in long-sequence reconstruction accuracy
Enhanced robustness to noisy mobile sensor measurements
Effective in capturing complex spatio-temporal patterns
Abstract
The reconstruction and estimation of spatio-temporal patterns poses significant challenges when sensor measurements are limited. The use of mobile sensors adds additional complexity due to the change in sensor locations over time. In such cases, historical measurement and sensor information are useful for better performance, including models such as Kalman filters, recurrent neural networks (RNNs) or transformer models. However, many of these approaches often fail to efficiently handle long sequences of data in such scenarios and are sensitive to noise. In this paper, we consider a model-free approach using the {\em structured state space sequence} (S4D) model as a deep learning layer in traditional sequence models to learn a better representation of historical sensor data. Specifically, it is integrated with a shallow decoder network for reconstruction of the high-dimensional state…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks · Advanced Data Compression Techniques · Advanced Wireless Communication Techniques
