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.

Accurate prediction of Remaining Useful Life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in Deep Learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with Deep Neural Network (DNN). Firstly, we present a multi-dimensional feature extraction method with autoencoder model to represent battery health degradation. Then, the RUL prediction model based DNN is trained for multi-battery remaining cycle life estimation. The proposed approach is applied to the real dataset of lithium-ion battery cycle life from NASA, and the experiment results show that the proposed approach can improve the accuracy of RUL prediction.

Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an emerging yet crucial issue to effectively and efficiently detect vulnerabilities in contracts. Existing detection methods like Oyente and Securify are mainly based on symbolic execution or analysis. These methods are very time-consuming, as the symbolic execution requires the exploration of all executable paths or the analysis of dependency graphs in a contract. In this work, we propose ContractWard to detect vulnerabilities in smart contracts with machine learning techniques. First, we extract bigram features from simplified operation codes of smart contracts. Second, we employ five machine learning algorithms and two sampling algorithms to build the models. ContractWard is evaluated with 49502 real-world smart contracts running on Ethereum. The experimental results demonstrate the effectiveness and efficiency of ContractWard. The predictive Micro-F1 and Macro-F1 of ContractWard are over 96% and the average detection time is 4 seconds on each smart contract when we use XGBoost for training the models and SMOTETomek for balancing the training sets.

Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

Cyber-physical-social system (CPSS) has drawn tremendous attention in industrial applications such as industrial Internet of Things (IIoT). As the fundamental component of IIoT, bearings play an increasingly important role in CPSS for IIoT. Better understanding of bearing working conditions and degradation patterns so as to more accurately predict the remaining useful life (RUL), becomes an urgent demand for industrial prognostics in IIoT. The data-driven approach has indicated good potential, but the prediction accuracy is still not satisfactory. This paper proposes a new method for the prediction of bearing RUL based on deep convolution neural network (CNN). A new feature extraction method is presented to obtain the eigenvector, named the spectrum-principal-energy-vector. The eigenvector is suitable for deep CNN. In the prediction phase, we propose a smoothing method to deal with the discontinuity problem found in the prediction results. To the best of our knowledge, we are the first to propose such a smoothing method for bearing RUL prediction. Experiments show that our method can significantly improve the prediction accuracy of bearing RUL.

Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area.