Aiming at the problems that most feature selection algorithms do not fully consider class non-uniform distribution of data， the correlation between features and the influence of different parameters on the feature selection results， a feature selection method for imbalanced data based on neighborhood tolerance mutual information and Whale Optimization Algorithm （WOA） was proposed. Firstly， for the binary and multi-class datasets in incomplete neighborhood decision system， two kinds of feature importances of imbalanced data were defined on the basis of the upper and lower boundary regions. Then， to fully reflect the decision-making ability of features and the correlation between features， the neighborhood tolerance mutual information was developed. Finally， by integrating the feature importance of imbalanced data and the neighborhood tolerance mutual information， a Feature Selection for Imbalanced Data based on Neighborhood tolerance mutual information （FSIDN） algorithm was designed， where the optimal parameters of feature selection algorithm were obtained by using WOA， and the nonlinear convergence factor and adaptive inertia weight were introduced to improve WOA and avoid WOA from falling into the local optimum. Experiments were conducted on 8 benchmark functions， the results show that the improved WOA has good optimization performance； and the experimental results of feature selection on 13 binary and 4 multi-class imbalanced datasets show that the proposed algorithm can effectively select the feature subsets with good classification effect compared with the other related algorithms.

The openness and dynamics of Wireless Mesh Network (WMN) makes it used widely, however, there are some security problems. The traditional trust model could no longer meet the security requirements of WMN. Based on the trust principle of social network, a new trust model named TFTrust was proposed. In the TFTrust, multi-dimensional factor calculation method was defined, including node contribution value, node dependency value and interaction frequency value, also the calculation method of the direct trust value was established. The simulation results show that TFTrust model is better than Ad Hoc on-demand Distance Vector Routing (AODV) protocol and Beth model in safety, quality of service and reduces the cost of network communications, etc.

To solve the sensor node localization problem of Wireless Sensor and Actor Network (WSAN), a range-based localization algorithm with virtual force in WSAN was proposed in this paper, in which mobile actor nodes were used instead of Wireless Sensor Network (WSN) anchors for localization algorithm, and Time Of Arrival (TOA) was combined with virtual force. In this algorithm, the actor nodes were driven under the action of virtual force and made themself move close to the sensor node which sent location request, and node localization was completed by the calculation of the distance between nodes according to the signal transmission time. The simulation results show that the localization success rate of the proposed algorithm can be improved by 20% and the average localization time and cost are less than the traditional TOA algorithm. It can apply to real-time field with small number of actor nodes.

To solve the low efficiency problem of grid service discovery, based on ontology technology, the theory of decision table, and knowledge representation system of rough sets, the paper put forward an optimized service discovery algorithm that considered the weight of the service properties. By rule extraction of the service invocation history and the calculation of the service properties weight, two main phases of the service discovery algorithm: information pre-processing and rough set service matching could be achieved. This article also gave theoretical analysis and experimental verification on both precision rate and recall rate. The results show that the proposed algorithm can provide higher precision and recall rate; besides, the ranking results of the candidate services are more preferable.

With study on the structure and performance of SR-tree(Sphere/Rectangle-tree) and X-tree(eXtended node tree),the split algorithm was improved to make up for the shortage of the SR-tree algorithm.A new multi-dimensional indexing structure ESR-tree(Extended SR-tree) was designed by combining the advantages of the both.With the increase of data amount and dimensions,experiments show that the performance of ESR-tree is much better than that of SR-tree and X-tree.