Aiming at the issue of low efficiency existing in resource scheduling of cloud computing, a new resource scheduling algorithm of cloud computing based on Quality of Service (QoS) was proposed. Firstly, the quality function and convergence factor were used in Ant Colony Optimization (ACO) algorithm to ensure the efficiency of pheromone updating and the feedback factor was set to improve the selection of probability. Secondly, the local search efficiency of Shuffled Frog Leading Algorithm (SFLA) was improved by setting crossover factor and mutation factor in the SFLA. Finally, the local search and global search of the SFLA were introduced for updating in each iteration of ACO algorithm, which improved the efficiency of algorithm. The simulation experimental results of cloud computing show that, compared with the basic ACO algorithm, SFLA, Improved Particle Swarm Optimization (IPSO) algorithm and Improved Artificial Bee Colony algorithm (IABC), the proposed algorithm has advantages in four indexes of QoS:the least completion time, the lowest cost of consumption, the highest satisfaction and the lowest abnormal value. The proposed algorithm can be effectively used in resource scheduling of cloud computing.

Aiming at the poor positioning accuracy of traditional wireless indoor positioning technology, an indoor positioning system based on Ultra Wide Band (UWB) technology was designed and implemented. Firstly, to solve the problem of autonomous localization and navigation, the system architecture of real-time interaction between the location server and the mobile terminal APP was proposed. Secondly, a radio message was added to the Two-Way Ranging (TWR) algorithm to reduce the ranging error caused by clock drift, so as to improve the performance of the algorithm. Finally, the hyperboloid equation set achieved by Time Difference Of Arrival (TDOA) positioning algorithm was linearized and then solved by Jacobi iteration method, which avoids the cases that standard TDOA localization algorithm is difficult to directly solve. The test results show that the system based on this method can work stably and control the range of error within 30 cm in the corridor room scene. Compared with the positioning systems based on WiFi and Bluetooth, the positioning accuracy of this system is improved by about 10 times, which can meet the requirement of precise mobile positioning in complex indoor environment.

A density clustering method based on eXtended Classifier Systems (XCS) was proposed, which could be used to cluster the two-dimensional data sets with arbitrary shapes and noises. The proposed method was called Density XCS Clustering (DXCSc), which mainly included the following three processes:1) Based on the learning classification system, regular population of input data was generated and compressed. 2) The generated rules were regarded as two-dimensional data points, and then the two-dimensional data points were clustered based on idea of density clustering. 3) The regular population after density clustering was properly aggregated to generate the final regular population. In the first process, the learning classifier system framework was used to generate and compact the regular population. In the second process, the rule cluster centers were characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. In the third process, the relevant clusters were properly merged using the graph segmentation method. In the experiments, the proposed DXCSc was compared with K-means, Affinity Propagation (AP) and Voting-XCSc on a number of challenging data sets. The experimental results show that the proposed approach outperforms K-means and Voting-XCSc in precision.