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Edge/Cloud-Assisted Feature Extraction in IoT Devices

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.