With the continuous development of digital currency, the blockchain technology has attracted more and more attention, and the research on its key technology, consensus mechanism, is particularly important. The application of blockchain technology in the Internet of Things(IoT) is one of the hot issues. Consensus mechanism is one of the core technologies of blockchain, which has an important impact on IoT in terms of decentralization degree, transaction processing speed, transaction confirmation delay, security, and scalability.Firstly, the architecture characteristics of IoT and the lightweight problem caused by resource limitation were described, the problems faced in the implementation of the blockchain in IoT were briefly summarized, and the demands of blockchain in IoT were analyzed by combining the operation flow of bitcoin. Secondly, the consensus mechanisms were divided into proof class, Byzantine class and Directed Acyclic Graph(DAG) class, and the working principles of these various classes of consensus mechanisms were studied, their adaptabilities to IoT were analyzed in terms of communication complexity, their advantages and disadvantages were summarized, and the combination architectures of the existing consensus mechanisms and IoT were investigated and analyzed. Finally, the problems of IoT, such as high operating cost, poor scalability and security risks were deeply studied, the analysis results show that the Internet of Things Application(IOTA) and Byteball consensus mechanisms based on DAG technology have the advantages of fast transaction processing speed, good scalability and strong security in the case of having a large number of transactions, and they are the development directions of blockchain consensus mechanism in the field of IoT in the future.

Inferring work locations for users based on spatiotemporal data is important for real-world applications ranging from product recommendation, precise marketing, transportation scheduling to city planning. However, the problem of location inference based on urban surveillance data has not been explored. Therefore, a work location inference method was proposed for vehicle owners based on the data of traffic surveillance with sparse cameras. First, the urban traffic periphery data such as road networks and Point Of Interests (POIs) were collected, and the preprocessing method of road network matching was used to obtain a real road network with rich semantic information such as POIs and cameras. Second, the important parking areas, which mean the candidate work areas for the vehicles were obtained by clustering Origin-Destination (O-D) pairs extracted from vehicle trajectories. Third, using the constraint of the proposed in/out visiting time pattern, the most likely work area was selected from multiple area candidates. Finally, by using the obtained road network and the distribution of POIs in the road network, the vehicle's reachable POIs were extracted to further narrow the range of work location. The effectiveness of the proposed method was demonstrated by comprehensive experimental evaluations and case studies on a real-world traffic surveillance dataset of a provincial capital city.