Robust Data-Aware Sensing Based on Half Quadratic Hashing for the Visual Internet of Things

Abstract

In the Visual Internet of Things (VIoT) such as drones over flying ad-hoc networks (FANETs), terminal nodes need to preliminarily identify sensing data. Thus, data can be rapidly dispatched to dedicated edge nodes for further processing. Nonetheless, terminals may collect partially observed visual data, which cause biased labeling. In view of such, this study proposes half quadratic supervised discrete hashing (HQSDH) that can conquer the biased similarity estimation for partially observed images, especially when large continuous occlusion, missing values, or sample-specific outliers exist. The proposed method can automatically and adaptively adjust itself via HQ auxiliary variables to avoid oversensitivity to large errors caused by partially observed data. In this study, to solve HQ discrete optimization problems in HQSDH, HQ discrete cyclic coordinate descent (HQDCCD) is developed. Second, partially observed images may cause inconsistent binary codes and increase the Hamming distances. Thus, this study devises HQ balancing decorrelation constraints along with normalization regularization to accommodate the problems. Third, a systematic and generic solution that allows various HQ functions to jointly work in the same framework is modeled to provide versatility. Experiments on open image data sets were carried out for evaluation. The results showed that the proposed method provided more resilience against continuous occlusion, missing values, and sample-specific outliers than the baselines when dealing with partially observed images.