Edge/Cloud-Assisted Feature Extraction in IoT Devices

Chuntao Ding, Yidong Li, Shangguang Wang

Abstract: The explosion of Internet of Things (IoT) devices will generate massive amounts of data. Due to the limited resources of IoT devices, they usually upload the collected data to edge/cloud servers for processing. To reduce the amount of data uploaded to the edge/cloud server, extracting the features of data on IoT devices has attracted increasing attention. However, most existing related works suffer from two major limitations: 1) difficulty meeting user needs: the extractors they generate are difficult to extract effective features from data on IoT devices and 2) consume a lot of storage resources: they deploy multiple extractors to adapt to the dynamically changing resources of IoT devices. To this end, we propose a nonredundant discriminative feature extraction (DFE) framework, which consists of similarity-based DFE (SDFE) and 2RNestE algorithms. SDFE is first proposed to generate an extractor E that can extract effective discriminative features by rationally exploring the structural information of the data set on the edge/cloud server. Then, 2RNestE is proposed, which takes E as input, and outputs a nonredundant multifunctional extractor by removing redundant subextractors and nesting the remaining nonredundant subextractors together. Finally, the edge/cloud server sends the generated extractor to the IoT device. Experimental results show that the proposed framework reduces memory footprint by about 82.6% and switching overhead by about 84.6% compared with state-of-the-art works.