This paper attempts to solve the security problems in communication, consensus-making and authentication of nodes in the Internet of vehicles (IoV) for intelligent transport. Considering the defects of the central node and service complexity in the IoV, the blockchain was integrated with the IoV to create a decentralized mechanism for communication and consensus-making. In the architecture of the blockchain-based IoV, the Byzantine consensus algorithm based on time sequence and gossip protocol is used to complete information communication and consensus authentication, which not only ensures communication security, improves the consensus efficiency of nodes, but also improves the fault tolerance of the algorithm. The experimental results show that our algorithm outshined the traditional authentication method in information security and consensus efficiency of the IoV. The research findings provide a reference solution to the authentication problems in the IoV for intelligent transport.INDEX TERMS Blockchain, consensus algorithm, intelligent transport, Internet of vehicles (IoV).

The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Sentiment analysis, an important part of text mining, attempts to learn about the authors' opinion on a text through its content and structure. Such information is particularly valuable for determining the overall opinion of a large number of people. Examples of the usefulness of this are predicting box office sales or stock prices. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. In this study we seek to predict a sentiment value for stock related tweets on Twitter, and demonstrate a correlation between this sentiment and the movement of a company's stock price in a real time streaming environment. Both n-gram and "word2vec" textual representation techniques are used alongside a random forest classification algorithm to predict the sentiment of tweets. These values are then evaluated for correlation between stock prices and Twitter sentiment for that each company. There are significant correlations between price and sentiment for several individual companies. Some companies such as Microsoft and Walmart show strong positive correlation, while others such as Goldman Sachs and Cisco Systems show strong negative correlation. This suggests that consumer facing companies are affected differently than other companies. Overall this appears to be a promising field for future research.

In wireless LANs, partial packets are often received which usually contain only a few errors. According to the current 802.11 standard, such packets have to be retransmitted. Much effort has been invested recently in repairing such packets without retransmitting the entire packet, e.g., by using error correction (EC) code or retransmitting only the corrupted blocks. In this paper, we revisit this problem, and propose Unite, a framework for more efficient partial packet recovery. Unite is motivated by two key observations. First, the two repair methods, i.e., the EC-based and block-based, are not mutually exclusive and can be combined to achieve higher performance. Second, the recently introduced error estimators can be utilized to determine the optimal repair method depending on the condition of each partial packet. The combined method should achieve better performance than each individual method; in addition, it should remain low in complexity because the EC-based and block-based method are both simple in nature. Unite uses AMPS, a recently proposed estimator, for error estimation. We implement Unite on the Madwifi open source driver, and our experiments show that Unite outperforms other recovery schemes.

The proliferation of electric vehicles and active distribution network has brought many uncertainties to the power system. If the power system involves battery-swap stations of electric vehicles, it is difficult to ensure the data security during the distributed scheduling. To solve the problem, this paper sets up a collaborative optimization model for distributed scheduling based on blockchain consensus mechanism, considering the battery-swap stations. The power system was divided into three levels: the transmission network level, the distribution network level and the battery-swap station level. Next, the objective functions were constructed to minimize the generation cost and daily load variance on each level, and the optimal scheduling plan for the power system was solved through multi-level collaborative optimization. The blockchain consensus mechanism was adopted to verify the accuracy of the transaction data, and the production data of all entities were encoded by hash function before storage, such that the data are tamper resistant and traceable. The example analysis shows that our model can effectively reduce the generation cost, lower the daily load variance, and enhance system stability. The research findings shed new light on maintaining the optimization efficiency and data confidentiality of modern power network.

To overcome the high cost, high risk and poor efficiency of traditional centralized electric energy trading method, this paper proposes an efficient trading mechanism for energy power supply and demand network (EPSDN) based on blockchain smart contract, considering the opening of the sales side market in China. Specifically, the encourage-real-quotation (ERQ) rule was adopted to determine the clearing queue and price, thus smoothing the supply and demand interaction between the EPSDN node. Meanwhile, the blockchain smart contract was introduced into the transaction to form a sealed quotation function, which eliminates the centralization and high cost and solves the poor transparency and trust in traditional transaction. In addition, the transaction efficiency was improved through the construction of an efficient power trading system and a secure trading environment. A case study is given in the end of the paper. Case study shows that the blockchain-based smart contract trading system for the EPSDN can achieve desirable security and effectiveness, and effectively solve the problems of the traditional centralized trading method. The research findings lay solid theoretical and decision-making bases for small-scale transactions in the electric energy market.