The Simultaneous Localization And Mapping (SLAM) is an essential skill for mobile robots exploring in unknown environments without external referencing systems. As the sparse map constructed by feature-based Visual SLAM (VSLAM) algorithm is not suitable for robot application, an efficient and compact map construction algorithm based on octree structure was proposed. First, according to the pose and depth data of the keyframes, the point cloud map of the scene corresponding to the image was constructed, and then the map was processed by the octree map technique, and a map suitable for the application of the robot was constructed. Comparing the proposed algorithm with RGB-Depth SLAM (RGB-D SLAM) algorithm, ElasticFusion algorithm and Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) algorithm on publicly available benchmark datasets, the results show that the proposed algorithm has high validity, accuracy and robustness. Finally, the autonomous mobile robot was built, and the improved VSLAM system was applied to the mobile robot. It can complete autonomous obstacle avoidance and 3D map construction in real-time, and solve the problem that the sparse map cannot be used for obstacle avoidance and navigation.

Priority Belief Propagation (priority-BP) algorithm cannot satisfy real-time requirement, and there is much room to improve its computational efficiency. As for its application in image inpainting, the main improvements of priority-BP algorithm focused on information transmission and tag searching. In information transmission step, sparse representation of image was wielded into the first iteration and the initial image information of target area was rapidly updated to provide more accurate prior information for the first iteration, which accelerated information transmission and improved the accuracy of tag trimming and information transmission. In tag searching step, the global searching was integrated with local searching instead of just only global searching so as to improve the construction efficiency of tag set. The improved algorithm was verified by examples. The results show that it has obvious advantage in inpainting images with large size; even with the small size of 120×126, it still improves the Peak Signal-to-Noise Ratio (PSNR) by 1.1 dB compared with original priority-BP algorithm, and reduces time consumption up to 1.2 seconds compared with original priority-BP algorithm. The experimental results indicate that the propsed algorithm can effectively improve the inpainting accuracy and efficiency.

In Wireless Sensor Network (WSN) clustering routing algorithm, sensors energy consumption imbalance will result in "energy hole" phenomenon, and it will affect the network lifetime. For this problem, an energy-balanced unequal clustering routing protocol based on game theory named GBUC was put forward. In clustering stage, WSNs were divided into clusters of different sizes, the cluster radius was determined by the distance from cluster head to sink node and the residual energy. By adjusting the cluster head in the energy consumption of communication within the cluster and forwarding data to achieve energy balance. In inter-cluster communication phase, a game model was established by using the residual energy efficiency and link reliability as the benefit functions, using its Nash equilibrium solution to get joint energy balancing, optimal transmission path of link reliability, thereby improving network performance. The simulation results show that, compared with Energy-Efficient Uneven Clustering (EEUC) algorithm and Unequal Clustering Energy-Economical Routing (UCEER) algorithm, the GBUC algorithm has significantly improved the performance in balancing node energy consumption and prolonging the network lifetime.

In the real-time large data applications of Peer-to-Peer (P2P), to avoid free-riding behavior in the Video on Demand (VOD) system, a new incentive scheme of auction algorithm based on the discriminatory second price was proposed. The nodes obtained the video data block they needed using distributed dynamic auction between nodes. In auction, the bidding node firstly determined whether the budget was enough to bid based on discrimination rule, and set the upload bandwidth according to the number of bidding nodes. Secondly, the winner node was determined by the bid price. Finally, the bidding node paid the auction node according to the second highest price after it got the data block as its income. Analysis of the revenue of nodes, the bandwidth utilization and the proportion of selfless or selfish nodes indicate that the proposed scheme can effectively motivate nodes to take active part in sharing of video data blocks, and make efficient use of the upload bandwidth at the same time.