Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing

TL;DR

This paper introduces a fast and efficient reinforcement learning-based method, AC-OSELM, for estimating optimal compression ratios in real-time on edge devices, significantly reducing communication costs in IoT sensor data transmission.

Contribution

The paper proposes AC-OSELM, an innovative online sequential learning algorithm tailored for edge devices to improve compression ratio estimation efficiency in compressed sensing.

Findings

AC-OSELM achieves faster estimation than existing RL methods.

The proposed method maintains or improves compression performance.

Experimental results validate the efficiency and effectiveness of AC-OSELM.

Abstract

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.