# Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing

arXiv: https://arxiv.org/abs/2211.04284 · HTML: https://mlsanity.com/paper/2211.04284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 8 Nov 2022.

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

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Adaptive Generalized ZEM-ZEV Feedback Guidance for Planetary Landing via a Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2003.02182/) (2020)
- [Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables](https://mlsanity.com/paper/1902.06913/) (2019)
- [An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning](https://mlsanity.com/paper/2005.04646/) (2020)
- [A Novel Update Mechanism for Q-Networks Based On Extreme Learning Machines](https://mlsanity.com/paper/2006.02986/) (2020)

## Abstract (arXiv)

Owing to the widespread adoption of the Internet of Things, a vast amount of sensor information is being acquired in real time. Accordingly, the communication cost of data from edge devices is increasing. Compressed sensing (CS), a data compression method that can be used on edge devices, has been attracting attention as a method to reduce communication costs. In CS, estimating the appropriate compression ratio is important. There is a method to adaptively estimate the compression ratio for the acquired data using reinforcement learning (RL). However, the computational costs associated with existing RL methods that can be utilized on edges are often high. In this study, we developed an efficient RL method for edge devices, referred to as the actor--critic online sequential extreme learning machine (AC-OSELM), and a system to compress data by estimating an appropriate compression ratio on the edge using AC-OSELM. The performance of the proposed method in estimating the compression ratio is evaluated by comparing it with other RL methods for edge devices. The experimental results indicate that AC-OSELM demonstrated the same or better compression performance and faster compression ratio estimation than the existing methods.