Trajectory privacy protection is critical to the development of Internet of Vehicles (IoV), which makes it important to summarize and analyze existing research methods. Existing IoV trajectory privacy protection methods can be divided into three categories: trajectory obfuscation, pseudonym change and trajectory encryption. Trajectory obfuscation can be achieved based on users' real trajectory or dummy trajectory. Pseudonym change can be achieved based on mix-zone or path confusion. Trajectory encryption can be achieved based on Private Information Retrieval (PIR) protocol or spatial transformation. Firstly, the research background and common attacks were introduced and summarized in this paper. Secondly, existing IoV trajectory privacy protection methods were surveyed from the aspects of methodology, scientific problem and method evolution. The problems need to be further studied also were elaborated. Furthermore, the performances of representative schemes were summarized, such as privacy protection, attack resistance and complexity. Finally, the future research directions of IoV trajectory privacy protection was prospected.

Since the existing crowdsourcing model could not detect the malicious behavior in the crowdsourcing system quickly and efficiently, a reputation model based on Dempster-Shafer theory, called DS_CQC (Dempster/Shafer Crowdsoucing Quality Control), was proposed to apply to the crowdsourcing quality control. Firstly, the sustainable credible evidence sequence and sustained incredible evidence sequence based on time-window were obtained. Secondly, the original D-S evidence theory was improved through three aspects including importance of evidence, relationship of evidence and reliability of witness, and the new basic probability function was acquired. Finally, evidence sequence was fused by using the improved D-S evidence theory and then the direct reputation, indirect reputation and comprehensive reputation were computed. The incentive mechanism based on reputation was used to encourage people to participate in crowdsourcing actively and submit a higher quality crowd, while the malicious workers were suppressed. Experiments on simulation and real crowd data were conducted, and compared to the trust model of probability, the detection of malicious behavior in the crowdsourcing system of DS_CQC increased by 50% in speed and 3.1% in efficiency at least. The result proves that the DS_CQC has the high anti-attacking capability.

There are the errors that memory data is not updated, the loop exits early and the loop exits late in the program loop control. In order to ensure the correctness of the program execution in the safety critical system, a new error detection algorithm of program loop control based on ANBD-code (arithmetic-code with signature and timestamp) was proposed. Through ANBD-code, the program variables were encoded as a signed code word by the proposed algorithm. And the errors in the loop control were detected by verifying code signature, the error of memory data being not updated could be detected by using the time label of ANBD-code. In addition, on the basis of the ANBD-code, the errors of the loop exiting early and the loop exiting late could be detected by using the online statement block signature allocation algorithm, the block signature function and the variable signature compensation function. The occurrence probability of an undetected error was 1/ A in theory, where A was coding prime. The primes were selected between 97 and 10993 to test occurrence probability of an undetected error and the Normalized Mean Square Error (NMSE) of theoretical model and test result was about-30 dB. The test results show that the proposed algorithm can effectively detect all kinds of errors in the loop control and the occurrence probability of an undetected error is up to 10 ^-9 when the prime A is close to 2 ³². The proposed algorithm can satisfy the requirements of safety critical system.

To solve the problem of location verification caused by collusion attack in Vehicular Ad Hoc NETworks (VANET), a multi-round vote location verification based on weight and difference was proposed. In the mechanism, a static frame was introduced and the Beacon messages format was redesigned to alleviate the time delay of location verification. By setting malicious vehicles filtering process, the position of the specific region was voted by the neighbors with different degrees of trust, which could obtain credible position verification. The experimental results illustrate that in the case of collusion attack, the scheme achieves a higher accuracy of 93.4% compared to Minimum Mean Square Estimation (MMSE) based location verification mechanism.

The concept and architecture of the Internet of Things (IoT) were introduced. Key characteristics and technologies, which included the ubiquity of the IoT, intelligent identification and sensing technologies, uncertainty of data, representation methods of data, information propagation towards massive data, security and privacy were discussed in detail, and related open issues were presented. Based on the future ubiquitous applications, the research directions of the IoT were put forward.