In big data environment, the research on mobile crowdsourcing system has become a research hotspot in Mobile Social Network (MSN). However, the selfishness of individuals in networks may cause the distrust problem of mobile crowdsourcing system. In order to inspire individuals to select trustful strategy, an online incentive mechanism based on reputation for mobile crowdsourcing system named RMI was proposed. Combining evolutionary game theory and Wright-Fisher model in biology, the evolution trend of mobile crowdsourcing system was studied. To solve free-riding and false-reporting problems, the reputation updating methods were established. Based on the above researches, an online incentive mechanism was built, which can inspire workers and requesters to select trustful strategies. The simulation results verify the effectiveness and adaptability of the proposed incentive mechanism. Compared with the traditional social norm-based reputation updating method, RMI can improve the trust degree of mobile crowdsourcing system effectively.

In view of the high time complexity of pixel segmentation, a super pixel segmentation algorithm was proposed for high resolution image. Super pixels instead of the original pixels were used as the segmentation processing elements and the characteristics of Hadoop and the super pixels were combined. Firstly, a static and dynamic adaptive algorithm for multiple tasks was proposed which could reduce the coupling of the blocks in HDFS (Hadoop Distributed File System) and task arranging. Secondly, based on the constraint in the distance and gradient on the super pixel formed by the boundary of super pixel block, a parallel watershed segmentation algorithm was proposed in each Map node task. Meanwhile, two merging strategies were proposed and compared in the super pixel block merging in the Shuffle process. Finally, the combination of super pixels was optimized to complete the final segmentation in the Reduce node task. The experimental results show that the proposed algorithm is superior to the Simple Linear Iterative Cluster (SLIC) algorithm and Normalized cut (Ncut) algorithm in Boundary Recall ratio (BR) and Under segmentation Error (UE), and the segmentation time of the high-resolution image is remarkably decreased.

Most mesh simplification algorithms for three-dimensional (3D) may cause oversimplification and distortion in the processing of simplification. To address this problem, an efficient multi-feature fusion method for mesh simplification was proposed. The proposed method measured geometric feature information based on quadric error metric weighted by normal information firstly, then used torsion weighted by side ratio of triangle to measure visual feature information, at last proposed a multi-feature fusion metric to guide model simplification. The experimental results have been compared on execution time and visual quality with other edge contraction algorithms; the results show that the proposed method can guarantee computational efficiency, improve visual shape feature, and reduce oversimplification and distortion for simplified model.