Aiming at the difficulty of constructing real tour routes from sparse refueling trajectories of self-driving tourists,a sparse trajectory clustering algorithm based on semantic representation was proposed to mine popular self-driving tour routes. Different from traditional trajectory clustering algorithms based on trajectory point matching,in this algorithm, the semantic relationships between different trajectory points were considered and the low-dimensional vector representation of the trajectory was learned. Firstly,the neural network language model was used to learn the distributed vector representation of the gas stations. Then,the average value of all the station vectors in each trajectory was taken as the vector representation of this trajectory. Finally,the classical k-means algorithm was used to cluster the trajectory vectors. The final visualization results show that the proposed algorithm mines two popular self-driving tour routes effectively.

Aiming at the problem of channel insecurity caused by wireless transmission in mobile Radio Frequency IDentification (RFID) system, a low-cost mobile mutual authentication protocol based on the Hash function was proposed by considering the complexity of the protocol and the implementation cost of the tag. In the protocol, the square operation was used to dynamically update the tag identifier. And the reader identifier, the pseudo random function and Hash function were used to enhance the identity authentication between the reader and the back-end server, which can improve the mobility of the system. Compared with the typical authentication protocols based on the Hash function and the tag ownership transfer protocol, this proposed protocol can resist tracking, impersonation, replay, man-in-the-middle, Denial of Service (DoS) attacks, etc., which can ensure the security of tag ownership transfer. The efficiency of calculation and storage was analyzed, and the results show that the calculation of the tag is reduced and the storage capacity is lower.