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, we consider the so-called structured low rank approximation (SLRA) problem as a problem of optimization on the set of either matrices or vectors. Briefly, SLRA is defined as follows. Given an initial matrix with a certain structure (for example, Hankel), the aim is to find a matrix of specified lower rank that approximates this initial matrix, whilst maintaining the initial structure. We demonstrate that the optimization problem arising is typically very difficult; in particular, the objective function is multiextremal even in simple cases. We also look at different methods of solving the SLRA problem. We show that some traditional methods do not even converge to a locally optimal matrix.

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.

We consider finite population slotted ALOHA where each of n terminals has its own transmission probability pi. Given the overall traffic load λ, the probabilities pi are determined in such a way as to maximize throughput. This is achieved by solving a constrained optimization problem. The results of Abramson (1970) are obtained as a special case. Our recent results are improved (Mathar and Žilinskas, 1993).