Internet of Things (IoT) equipment is usually in a harsh environment, and its security has always been a widely concerned issue. Node identity authentication is an important means to ensure its security. Traditional IoT identity authentication protocols usually rely on trusted third parties. However, many IoT environments do not allow such conditions, and are prone to single point failure. Blockchain technology with decentralization features provides a new solution for distributed IoT system. In this paper, a blockchain based multi-WSN authentication scheme for Internet of Things is proposed. The nodes of IoT are divided into base stations, cluster head nodes and ordinary nodes according to their capability differences, which are formed to a hierarchical network. A blockchain network is constructed among different types of nodes to form a hybrid blockchain model including local chain and public chain. In this hybrid model, nodes identity mutual authentication in various communication scenarios are realized, ordinary node identity authentication operation is accomplished by local blockchain, and cluster head node identity authentication is realized in public blockchain. The analysis of security and performance shows that the scheme has comprehensive security and better performance.

With accelerated ensemble of the Internet of Things technology and automotive industry, vehicular network has been established as powerful tools. However, it is a significant challenge for dynamic and heterogeneous vehicular network to meet high requirements of the sixth-generation (6G) network such as high reliability and high security. To address this challenge, we design a novel weight-based ensemble machine learning algorithm (WBELA) to identify abnormal messages of vehicular Controller Area Network (CAN) bus network. Then, we establish a model based on many-objective optimization for intrusion detection of CAN bus network. To support this model, a many-objective optimization algorithm based on balance convergence and diversity (MaOEA-BCD) is designed. Open-source CAN bus message data sets and tamper attack scenarios are used to evaluate the effectiveness of proposed algorithm for different ID data frames. Experimental results revealed that proposed methods significantly enhance precision, reduce the false positive rate and have better performance than other methods so as to enhance security of vehicular networks in 6G.

In intelligent unmanned warehouse goods-to-man systems, the allocation of tasks has an important influence on the efficiency because of the dynamic performance of AGV robots and orders. The paper presents a hierarchical Soft Actor-Critic algorithm to solve the dynamic scheduling problem of orders picking. The method proposed is based on the classic Soft Actor-Critic and hierarchical reinforcement learning algorithm. In this paper, the model is trained at different time scales by introducing sub-goals, with the top-level learning a policy and the bottom level learning a policy to achieve the sub-goals. The actor of the controller aims to maximize expected intrinsic reward while also maximizing entropy. That is, to succeed at the sub-goals while moving as randomly as possible. Finally, experimental results for simulation experiments in different scenes show that the method can make multi-logistics AGV robots work together and improves the reward in sparse environments about 2.61 times compared to the SAC algorithm.

Graph Convolutional Network (GCN) is extensively used in text classification tasks and performs well in the process of the non-euclidean structure data. Usually, GCN is implemented with the spatial-based method, such as Graph Attention Network (GAT). However, the current GCN-based methods still lack a more reasonable mechanism to account for the problems of contextual dependency and lexical polysemy. Therefore, an improved GCN (IGCN) is proposed to address the above problems, which introduces the Bidirectional Long Short-Term Memory (BiLSTM) Network, the Part-of-Speech (POS) information, and the dependency relationship. From a theoretical point of view, the innovation of IGCN is generalizable and straightforward: use the short-range contextual dependency and the long-range contextual dependency captured by the dependency relationship together to address the problem of contextual dependency and use a more comprehensive semantic information provided by the BiLSTM and the POS information to address the problem of lexical polysemy. What is worth mentioning, the dependency relationship is daringly transplanted from relation extraction tasks to text classification tasks to provide the graph required by IGCN. Experiments on three benchmarking datasets show that IGCN achieves competitive results compared with the other seven baseline models.

In the text sentiment classification task, some words are seemingly unrelated to the classification task, but they have a direct impact on the performance of classification model. For example, in the sentences “I have terminal cancer” and “Cancer is a very common disease”, it can be clearly found that the word “cancer” has two different sentiment tendencies in the daily life domain and the medical domain. In the daily life domain, the word “cancer” shows an extremely negative sentiment tendency. While in the medical domain, the word “cancer” is just a simple term with a relatively neutral sentiment tendency. Although current deep learning models have already achieved good performance through their powerful feature learning capabilities, there are serious deficiencies in dealing with the above problem. Therefore, from a new perspective, this paper proposes the Graph Domain Adversarial Transfer Network (GDATN) based on the idea of adversarial learning, which uses the labeled source domain data to predict the sentiment label of unlabeled target domain data. Firstly, GDATN extracts feature representations through the Bidirectional Long Short-Term Memory (BiLSTM) Network and Graph Attention Network (GAT) successively. Then, GDATN introduces the domain classifier to capture the domain-shared text feature representation with the Gradient Reversal Layer (GRL). In addition, an auxiliary task named the projection mechanism is constructed to further capture the domain-specific text feature representation in response to the text domain problem. Extensive experimental results on two benchmark datasets show that GDATN proposed in this paper outperforms the other six benchmark sentiment classification models, and GDATN has a better stability on different cross-domain pairs.