# OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework

arXiv: https://arxiv.org/abs/2308.05757 · HTML: https://mlsanity.com/paper/2308.05757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 5 Aug 2023.

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

- [Compressed Sensing using Generative Models](https://mlsanity.com/paper/1703.03208/) (2017)
- [Deep Spectral Clustering using Dual Autoencoder Network](https://mlsanity.com/paper/1904.13113/) (2019)
- [Adversarial Examples: Opportunities and Challenges](https://mlsanity.com/paper/1809.04790/) (2018)
- [Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting](https://mlsanity.com/paper/2101.10460/) (2021)
- [Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion](https://mlsanity.com/paper/1904.11960/) (2019)
- [Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges](https://mlsanity.com/paper/1907.08965/) (2019)
- [Machine learning and data analytics for the IoT](https://mlsanity.com/paper/2007.04093/) (2020)
- [Learning Common Representation from RGB and Depth Images](https://mlsanity.com/paper/1812.06873/) (2018)
- [Novel Light Weight Compressed Data Aggregation Using Sparse Measurements for IoT Networks](https://mlsanity.com/paper/1806.04874/) (2018)

## Abstract (arXiv)

Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.