Communication-Efficient Quantized Deep Compressed Sensing for Edge-Cloud Collaborative Industrial IoT Networks
Mingqiang Zhang, Haixia Zhang, Chuanting Zhang, Dongfeng Yuan
Abstract
Due to the limited energy, communication bandwidth and computing ability of edge devices in Industrial Internet of Things (IIoT) networks, it is incredibly challenging to compress and transmit those massive manufacturing data collected at the edge, thus greatly degrading the transmission and computing efficiency and finally results in long latency. To address this, we propose a quantized deep compressed sensing network (QDCS-Net) for both linear and nonlinear measurements to help better compress the industrial data to reduce the transmission volume of data and achieve good reconstruction performance. The joint design of customized quantization layers, dual-path structures, and swish activation function in QDCS-Net is adopted to achieve high-precision data reconstruction at high compression ratios. The latency is analyzed for different transmission deployment schemes to get a better edge-cloud collaboration strategy. We evaluate QDCS-Net by using real-world datasets collected from a vibration signal acquisition system. Experimental results demonstrate that the proposed QDCS-Net performs better in recovering industrial signals even at extremely low compression ratios of 1/128, thus can effectively improve data reconstruction accuracy and communication efficiency.