In order to maintain high precision of parameter curve fitting on the basis of compressed data， a high-precision Non-uniform Rational B Spline （NURBS） curve fitting method was proposed based on feature point extraction， least square approximation and particle swarm optimization algorithm solving optimal control points. Firstly， feature points were extracted from all discrete data points based on the inflection point and curvature extreme points. Then， the characteristic points were approximated by the least square method， and the initial control points were calculated according to the obtained linear system of equations. Finally， the initial population of particles was constructed by the position coordinates of the initial control points， and a fitness function was established to measure the errors between the discrete data point and the fitting curve. The positions of the initial control points were iteratively optimized by the particle swarm optimization algorithm until the maximum number of iterations was reached. The results of experimental verification on blade and butterfly section prototypes show that the amount of data to be fitted is compressed to the 25/117 and 120/283 respectively of the original one by using the proposed method. Compared with the method of adding auxiliary control points with high accuracy as advantage， the proposed method has the fitting accuracy 57.1% and 22.9% higher， indicating strong competitiveness of the method in the existing curve fitting research methods.

The problem of the previous research about collaborative ranking is that it does not make full use of the information in the dataset, either focusing on explicit feedback data, or focusing on implicit feedback data. Until now, nobody researches collaborative ranking algorithm by explicit and implicit feedback fusion. In order to overcome the defects of prior research, a new collaborative ranking algorithm by explicit and implicit feedback fusion namedMERR_SVD++ was proposed to optimize Expected Reciprocal Rank (ERR) based on the newest Extended Collaborative Less-is-More Filtering (xCLiMF) model and Singular Value Decomposition++ (SVD++) algorithm. The experimental results on practical datasets show that, the values of Normalized Discounted Cumulative Gain (NDCG) and ERR for MERR_SVD++ are increased by 25.9% compared with xCLiMF, Cofi Ranking (CofiRank), PopRec and Random collaborative ranking algorithms, and the running time of MERR_SVD++ showed a linear correlation with the number of ratings. Because of the high precision and the good expansibility, MERR_SVD++ is suitable for processing big data, and has wide application prospect in the field of Internet information recommendation.

Concerning the problem that the impact of illegal nodes (including the node unable to locate) on the localization process in DV-Hop localization algorithm has not been taken into consideration, this paper proposed a secure localization mechanism based on DV-Hop. In other words, the character of message exchange between the nodes was introduced to detect the wormhole attacks in this paper. Time property and space property were used to define the valid beacon nodes, along with encryption and authentication mechanisms to resist against the node-tampering attack in the communication process. Finally, the nodes were located securely. The simulation results show that, in hostile environment, the proposed mechanism has a high probability to detect the wormhole attacks, and the relative localization error can be reduced by 63% or so.