Informatica

In recent years, Wireless Sensor Networks (WSNs) received great attention because of their important applications in many areas. Consequently, a need for improving their performance and efficiency, especially in energy awareness, is of a great interest. Therefore, in this paper, we proposed a lifetime improvement fixed clustering energy awareness routing protocol for WSNs named Load Balancing Cluster Head (LBCH) protocol. LBCH mainly aims at reducing the energy consumption in the network and balancing the workload over all nodes within the network. A novel method for selecting initial cluster heads (CHs) is proposed. In addition, the network nodes are evenly distributed into clusters to build balanced size clusters. Finally, a novel scheme is proposed to circulate the role of CHs depending on the energy and location information of each node in each cluster. Multihop technique is used to minimize the communication distance between CHs and the base station (BS) thus saving nodes energy. In order to evaluate the performance of LBCH, a thorough simulation has been conducted and the results are compared with other related protocols (i.e. ACBEC-WSNs-CD, Adaptive LEACH-F, LEACH-F, and RRCH). The simulations showed that LBCH overcomes other related protocols for both continuous data and event-based data models at different network densities. LBCH achieved an average improvement in the range of 2–172%, 18–145.5%, 10.18–62%, 63–82.5% over the compared protocols in terms of number of alive nodes, first node died (FND), network throughput, and load balancing, respectively.

In this paper, a method for the study of cluster stability is purposed. We draw pairs of samples from the data, according to two sampling distributions. The first distribution corresponds to the high density zones of data-elements distribution. Thus it is associated with the clusters cores. The second one, associated with the cluster margins, is related to the low density zones. The samples are clustered and the two obtained partitions are compared. The partitions are considered to be consistent if the obtained clusters are similar. The resemblance is measured by the total number of edges, in the clusters minimal spanning trees, connecting points from different samples. We use the Friedman and Rafsky two sample test statistic. Under the homogeneity hypothesis, this statistic is normally distributed. Thus, it can be expected that the true number of clusters corresponds to the statistic empirical distribution which is closest to normal. Numerical experiments demonstrate the ability of the approach to detect the true number of clusters.

We investigate applicability of quantitative methods to discover the most fundamental structural properties of the most reliable political data in Lithuania. Namely, we analyze voting data of the Lithuanian Parliament. Two most widely used techniques of structural data analysis (clustering and multidimensional scaling) are compared. We draw some technical conclusions which can serve as recommendations in more purposeful application of these methods.

The paper deals with the minimization algorithms which enable us to economize the computing time during the coordinated calculation of the values of an objective function on the nodes of a rectangular lattice by storing and using quantities that are common for several nodes. The algorithm of a uniform search with clustering, the variable metric algorithm and the polytope algorithm are modified.

The problem multialternative recognition of non-stationary processes on the basis of dynamic models is investigated in the paper. The algorithms of pointwise and group classifications are compared. Clustering algorithms based on nonlinear mapping of the segments of random processes onto the plain are used to construct the classifiers.