Abstract.With the scientific and technological progress, new-style three-dimensional (3D) mobile sensor networks draw the attention of many exciting applications in multifarious areas, where the former twodimensional (2D) assumptions do not make sense. Existing references were mostly restricted to 3D static sensor networks, featuring either random or deterministic, while basing on more realistic mobile conditions, the studies were rarely referred. In this paper, we, for the first time, apply the noted virtual force algorithm (VFA) to 3D space. Except for traditional virtual forces, central gravitation and equilibrium force are additionally introduced to get better sensor distribution. Then four groups of cases are presented to summarize various mobility patterns and two metrics are of particular interest to evaluate the performance of our proposed improvement, namely coverage ratio and homogeneous degree. The simulation witnesses the effectiveness of the improved mechanism in the end.

Since SVM is sensitive to noises and outliers of system call sequence data. A new fuzzy support vector machine algorithm based on SVDD is presented in this paper. In our algorithm, the noises and outliers are identified by a hypersphere with minimum volume while containing the maximum of the samples. The definition of fuzzy membership is considered by not only the relation between a sample and hyperplane, but also relation between samples. For each sample inside the hypersphere, the fuzzy membership function is a linear function of the distance between the sample and the hyperplane. The greater the distance, the greater the weight coefficient. For each sample outside the hypersphere, the membership function is an exponential function of the distance between the sample and the hyperplane. The greater the distance, the smaller the weight coefficient. Compared with the traditional fuzzy membership definition based on the relation between a sample and its cluster center, our method effectively distinguishes the noises or outlies from support vectors and assigns them appropriate weight coefficients even though they are distributed on the boundary between the positive and the negative classes. The experiments show that the fuzzy support vector proposed in this paper is more robust than the support vector machine and fuzzy support vector machines based on the distance of a sample and its cluster center.

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