Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in Human Activity Recognition (HAR), due to the rapid development of wearable and mobile devices. In this study, we focus on the deep learning enhanced HAR in IoHT environments. A semi-supervised deep learning framework is designed and built for more accurate HAR, which efficiently use and analyze the weakly labeled sensor data to train the classifier learning model. To better solve the problem of inadequately labeled sample, an intelligent auto-labeling scheme based on Deep Q-Network (DQN) is developed with a newly designed distance-based reward rule, which can improve the learning efficiency in IoT environments. A multi-sensor based data fusion mechanism is then developed to seamlessly integrate the on-body sensor data, context sensor data, and personal profile data together, and a Long Short Term Memory (LSTM)-based classification method is proposed to identify fine-grained patterns according to the high-level features contextually extracted from sequential motion data. Finally, experiments and evaluations are conducted to demonstrate the usefulness and effectiveness of the proposed method using real world data.

The rapidly developed Health 2.0 technology has provide people with more opportunities to conduct online medical consultation than ever before. Understanding contexts within different online medical activities becomes a significant issue to facilitate patients' medical decision making process. As a subcategory of machine learning, neural networks have drawn increasing attentions in natural language processing applications. In this study, we focus on the modeling and analysis of patient-physician-generated data based on an integrated CNN-RNN framework. A CNN-based classifier is designed to extract textural features in the sentence-level, and a RNN-based model is constructed to learn the potential patterns and correlations in a dialog-level. An intelligent recommendation mechanism is then developed to provide patients with automatic clinic guide and pre-diagnosis suggestions in a data-driven way. Experiments based on the collected real world data demonstrate the effectiveness of our proposed model and method for online medical pre-diagnosis support.

In this study, to alleviate the inconsistency between dimensionality reduction and feature retention in anomaly detection, we propose a Variational Long Short-Term Memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. A compressed network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data. Three loss functions are defined and quantified to constrain the reconstructed hidden variable into a more explicit and meaningful way. A lightweight estimation network is fed with the refined feature representation to identify anomalies in IBD. Experiments using a public IBD dataset UNSW-NB15 demonstrate that the proposed VLSTM model can significantly improve F1, AUC, and FAR scores in anomaly detection.

Intelligent anomaly detection for identifying cyber/physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this study, we propose a few-shot learning model based on Siamese Convolution Neural Network (FS-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPSs. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FS-SCNN can significantly improve FAR and F1 scores when detecting intrusion signals for industrial CPS security protection.

Along with the rapid development of cloud computing, IoT, and AI technologies, cloud video surveillance (CVS) has become a hotly discussed topic, especially when facing the requirement of real-time analysis in smart applications. Object detection usually plays an important role for environment monitoring and activity tracking in surveillance system. The emerging edge-cloud computing paradigm provides us an opportunity to deal with the continuously generated huge amount of surveillance data in an on-site manner across IoT systems. However, the detection performance is still far away from satisfactions due to the complex surveilling environment. In this study, we focus on the multitarget detection for real-time surveillance in smart IoT systems. A newly designed deep neural network model called A-YONet, which is constructed by combining the advantages of YOLO and MTCNN, is proposed to be deployed in an end-edge-cloud surveillance system, in order to realize the lightweight training and feature learning with limited computing sources. An intelligent detection algorithm is then developed based on a preadjusting scheme of anchor box and a multilevel feature fusion mechanism. Experiments and evaluations using two data sets, including one public data set and one homemade data set obtained in a real surveillance system, demonstrate the effectiveness of our proposed method in enhancing training efficiency and detection precision, especially for multitarget detection in smart IoT application developments.

The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.