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.

翻译：由于物联网的广泛普及，大量传感器信息正被实时采集。因此，边缘设备的数据通信成本不断增加。压缩感知（CS）作为一种可在边缘设备上使用的数据压缩方法，正因其降低通信成本的潜力而备受关注。在压缩感知中，估计合适的压缩比至关重要。现有方法可利用强化学习（RL）自适应估计采集数据的压缩比。然而，可用于边缘设备的现有强化学习方法通常计算成本较高。本研究提出了一种适用于边缘设备的高效强化学习方法——在线序贯极限学习机演员-评论家算法（AC-OSELM），并构建了一套利用AC-OSELM在边缘端估计合适压缩比进行数据压缩的系统。通过与其他边缘设备强化学习方法对比，评估了所提方法在压缩比估计中的性能。实验结果表明，AC-OSELM在压缩性能上与现有方法相当或更优，且压缩比估计速度更快。