An Ultra WideBand (UWB)/ Pedestrian Dead Reckoning (PDR) fusion positioning algorithm with adaptive coefficient adjustment based on UWB error prediction was proposed in order to improve the UWB performance and reduce the PDR accumulative errors in the indoor Non-Line-Of-Sight (NLOS) positioning scenes and solve the UWB performance degradation caused by environmental factors. On the basis of the creative proposal of predicting the UWB positioning errors in complex environment by Support Vector Machine (SVM) regression model, UWB/PDR fusion positioning performance was improved by adding adaptive adjusted parameters to the conventional Extended Kalman Filter (EKF) algorithm. The experimental results show that the proposed algorithm can effectively predict the current UWB positioning errors in the complex UWB environment, and increase the accuracy by adaptively adjusting the fusion parameters, which makes the positioning error reduced by 18.2% in general areas and reduced by 48.7% in the areas with poor UWB accuracy compared with those of the conventional EKF algorithm, so as to decrease the environmental impact on the UWB performance. In complex scenes of both Line-Of-Sight (LOS) and NLOS including UWB, the positioning error per 100 meters is reduced from meter scale to decimeter scale, which reduces the PDR errors in NLOS scenes.

For the the heavy complexity of scheduling algorithm and the misallocation of assignment occurring in the cloud computing environment, a load balancing technology based on naive Bayes algorithm was proposed. This technology made use of the heartbeat mechanism to gather every node's load information comprehensively, so as to classify the load state of all nodes based on naive Bayes algorithm. Then, according to the classification, it achieved reasonable dispatch of the task and resource for each node. The results of the experiments show that, this load balancing technology improves the efficiency of the allocation of tasks and avoids the frequent migration between nodes, so that it can achieve the purpose of balancing the load rapidly and effectively.