With the popularity of mobile smart devices, Mobile Crowd Sensing (MCS) has been widely used while facing serious privacy leaks. Focusing on the issue that the existing original data privacy protection scheme is unable to resist collusion attacks and reduce the perception data availability, a Data Privacy Protection algorithm based on Mobile Node (DPPMN) was proposed. Firstly, the node manager in DPPMN was used to establish an online node list and send it to the source node. An anonymous path for data transmission was built by the source node through the list. Then, the data was encrypted by using paillier encryption scheme, and the ciphertext was uploaded to the application server along the path. Finally, the required perception data was obtained by the server using ciphertext decryption. The data was encrypted and decrypted during transmission, making sure that the attacker was not able to wiretap the content of the perception data and trace the source of the data along the path. The DPPMN ensures that the application server can access the original data without the privacy invasion of the nodes. Theoretical analysis and experimental results show that DPPMN has higher data security with increasing appropriate communication, and can resist collusion attacks without affecting the availability of data.

Cyber Physical System (CPS) involves the integration and collaboration of various computing models. Concerning the problems of inconsistent CPS design methods, poor plasticity, high complexity and difficulty in collaborative modeling and verification, a structured and descriptive heterogeneous component model was proposed. Firstly, the model was constructed by a unified component modeling method to solve the problem that the model was not open. Then, eXtensible Markup Language (XML) was used to realize the standard description of all kinds of components to resolve the inconsistency and non-extensibility of different computing model description languages. Finally, the collaborative simulation verification method of multi-level open component model was used to realize the simulation verification to solve the non-collaboration problem of verification. The medical thermostat was modeled, described and simulated by the general component modeling method, the XML component standard description language and the verification tool platform XModel. The case of medical thermostat shows that, the proposed model-driven process of building reconfigurable heterogeneous components and confirming their design correctness supports the collaborative design of cyber physics and the correction while constructing, avoiding repeated modifications when problems are found in the process of system implementation.

A Quantum-inspired Migrating Birds Co-Optimization (QMBCO) algorithm was proposed for minimizing the makespan in Lot-streaming Flow shop Scheduling Problem (LFSP). Firstly, the quantum coding based on Bloch coordinates was applied to expand the solution space. Secondly, an initial solution improvement scheme based on Framinan-Leisten (FL) algorithm was used to makeup the shortage of traditional initial solution and construct the random initial population with high quality. Finally, Migrating Birds Optimization (MBO) and Variable Neighborhood Search (VNS) algorithm were applied for iteration to achieve the information exchange between the worse individuals and superior individuals in proposed algorithm to improve the global search ability. A set of instances with different scales were generated randomly, and QMBCO was compared with Discrete Particle Swarm Optimization (DPSO), MBO and Quantum-inspired Cuckoo Co-Search (QCCS) algorithms on them. Experimental results show that compared with DPSO, MBO and QCCS, QMBCO has the Average Relative Percentage Deviation (ARPD) averagely reduced by 65%, 34% and 24% respectively under two types of running time, verifying the effectiveness and efficiency of the proposed QMBCO algorithm.

Focusing on the issue of privacy disclosure between trajectory and non-sensitive information, a trajectory privacy preserving algorithm based on non-sensitive information analysis was proposed. Firstly, the correlation between trajectory and non-sensitive information was analyzed to build trajectory privacy disclosure decision model, and the Minimal Violating Sequence tuple (MVS) was gotten. Secondly, using common subsequences, the doublets with the minimal loss of trajectory data in MVS were selected as the suppression objects when removing the privacy risks caused by MVS, then the anonymized trajectory dataset with privacy and low data loss was obtained. In the comparison experiments with LKC-Local algorithm and Trad-Local algorithm, when the sequence length is 3, the average instance loss of the proposed algorithm is decreased by about 6% and 30% respectively, and the average MFS (Maximal Frequent Sequence) loss is decreased by about 7% and 60% respectively. The experimental results verify that the proposed algorithm can effectively improve the quality of recommend service.

A Novel Quantum Differential Evolutionary (NQDE) algorithm was proposed for the Blocking Flowshop Scheduling Problem (BFSP) to minimize the makespan. The NQDE algorithm combined Quantum Evolutionary Algorithm (QEA) with Differential Evolution (DE) algorithm, and a novel quantum rotating gate was designed to control the evolutionary trend and increase the diversity of population. An effective Quantum-inspired Evolutionary Algorithm-Variable Neighborhood Search (QEA-VNS) co-evolutionary strategy was also developed to enhance the global search ability of the algorithm and to further improve the solution quality. The proposed algorithm was tested on the Taillard's benchmark instances, and the results show that the number of optimal solutions obtained by NQDE is bigger than the current better heuristic algorithm-Improved Nawaz-Enscore-Ham Heuristic (INEH) evidently. Specifically, the optimal solutions of 64 instances in the 110 instances are improved by NQDE. Moreover, the performance of NQDE is superior to the valid meta-heuristic algorithm-New Modified Shuffled Frog Leaping Algorithm (NMSFLA) and Hybrid Quantum DE (HQDE), and the Average Relative Percentage Deviation (ARPD) of NQDE algorithm decreases by 6% contrasted with the latter ones. So it is proved that NQDE algorithm is suitable for the large scale BFSP.