Sebastian Biedron

MODIFIED, COMPLEMENTED TAXONOMY OF FAULTS IN FAULT-TOLERANT REAL-TIME SYSTEMS

This paper presents the main definitions relating to dependability. Basic definitions including reliability, security, maintainability, etc. are described first. They are then supplemented by additional definitions, which address to the threats of dependability (faults, errors, failures). Overlapping dependability standards, renumbering and integration can cause uncertainty when using of a certain definition. For this purpose, authors present complemented fault taxonomy for fault-tolerant real-time systems to eliminate inconsistencies and to unify existing fault taxonomies.

THE APPLICATION OF REDUNDANCY IN LEACH PROTOCOL

Nowadays wireless sensor networks have gained much popularity due to their flexibility. This progress has enabled the use of sensor nodes on an unprecedented scale and opened new opportunities for the so-called ubiquitous computerization. The total freedom of nodes distribution within the wireless sensor network (WSN), where the wireless characteristic is one of the greatest advantages of WSN, implies its greatest weaknesses. To overcome this challenge specialized routing protocols, such as many different version of LEACH, were ushered in so as to make effective use of the energy of the nodes themselves. This article aims to show the functioning of a new author’s solution, called IIS-LEACH, which uses redundancy as a basic element in increasing the reliability of WSN networks.

ALTERNATIVE TERMINATION CRITERION FOR K-SPECIFIED CRISP DATA CLUSTERING ALGORITHMS

In this paper the analysis of k-specified (namely k-means) crisp data partitioning pre-clustering algorithm’s termination criterion performance is described. The results have been analyzed using the clustering validity indices. Termination criterion allows analyzing data with any number of clusters. Moreover, introduced criterion in contrast to the known validity indices enables to analyze data that make up one cluster.

TESTING FOR REVEALING OF DATA STRUCTURE BASED ON THE HYBRID APPROACH

In this paper testing for revealing data structure based on a hybrid approach has been presented. The hybrid approach used during the testing suggests defining a pre-clustering hypothesis, defining a pre-clustering statistic and assuming the homogeneity of the data under pre-defined hypothesis, applying the same clustering procedure for a data set of interest, and comparing results obtained under the pre-clustering statistic with the results from the data set of interest. The pros and cons of the hybrid approach have been also considered.

Analysis of a new model of low energy adaptive clustering hierarchy protocol in the wireless sensor network

Wireless sensor networks constitute a special group of distributed measurement systems that are increasingly used to observe environmental factors for civilian or military purposes. These observations are not conducted by a single node, but rather by multiple nodes which together form ad-hoc cooperation networks. The event to be measured can be observed with the benefit of the very working principle of sensor networks, where information from each node is transmitted to the observer in the form of an induced periodic query or is induced by the event. With the technological advancements it is now possible to manufacture nodes whose size is in the order of centimeters. This enables the use of sensor nodes on an unprecedented scale and opens new opportunities for so-called ubiquitous computing. Initially, sensor networks were wired solutions. The development of wireless and data processing technologies has helped sensor nodes to take over a larger area of research. The authors of this article wish to raise the issue of sensor networks development and show the influence of various factors on the self-positioning of sensor networks.

ROBUST VERIFICATION AND ANALYSIS OF THE PRE-CLUSTERING ALGORITHM WITH A-PRIORI NON- SPECIFICATION OF THE NUMBER OF CLUSTERS

The range of the implementation of cluster analysis is wide, it extends from many technical applications to different branches of science, such as biology, medicine, computer sciences and psychology. The main purpose of the cluster analysis is dividing the investigated objects into homogeneous groups, or clusters, according to certain criteria and investigating the process of natural grouping of these objects. It means solving the task of grouping data and revealing in them a relevant structure. The unsupervised learning methods (clusterization) as opposed to the supervised learning methods (classification), marks of output objects, that is, determining each object belonging to the certain cluster, as well as the number of the clusters are not given from the very beginning of the process. The created clustering algorithm without a-priori information about the number of the clusters belongs to the group of pre-clustering algorithms. Pre-clustering is the procedure of checking the possibility of clustering the input data. Checking this possibility answers the question whether data can be divided into more than one cluster. The process of verification of the parameters of the pre-clustering algorithm concerns testing the rule of decision making for different types of input data. The selected cases of input data considered here are the input data with the normal distribution law having been grouped into one or two clusters.

Analysis of the modified alternative decision rule in the preclustering algorithm

The preclustering algorithm as opposed to other existed algorithms does not require a priori information about cluster location and about additional means of control. Preclustering algorithm is multipurpose and promising for a primary analysis of investigated input data. In this article the main part of the preclustering algorithm – the modified decision rule has been presented. The modification consisted to the replacement of the calculation of mean distances in a precluster (like in the classical decision rule) by the mean distances from the center of the precluster to all objects in the chosen precluster. The proposed decision rule determines the centre of the group as a local density maximum of the group of objects (before clustering) or of the precluster (after clustering). The results obtained during the testing of the decision rule were compared with the results obtained with the use of criteria of spherical resolution. Also, from the analysis, the advantages and disadvantages of the proposed decision rule have been identified.

COMPARISON OVERVIEW OF AN ACTIVE FINGERPRINTING APPLICATION OF THE SECOND AND THE THIRD LAYER OF TCP/IP STACK

Nowadays, computer networks have become as popular as telephone networks fifteen years ago and by the year they are playing ever more important functions in human life. Not only they have created huge opportunities in many areas of life, facilitating communications or obtaining information, but also have provided online entertainment as well. The invention, originally developed for military purposes in one of the darkest periods of human civilization, has become the greatest discovery of the twentieth century, connecting millions of people around the world into one big community. Whatever the differences in size or devices used, a computer network can be defined as a combination of terminal devices, transmission medium, intermediate communication devices and network software (network area). Currently, anyone can build their own network or join the largest of them - the Internet - via a local Internet provider. Unfortunately, the Internet is not only a virtually unlimited source of information, entertainment, communication, and work. Alongside its positive aspects and conveniences it poses numerous risks to uninformed users. This publication aims to cast light on the aspects of the method of sampling operating systems security of network computers, i.e. active fingerprinting of the data link layer and Internet TCP/IP stack layer.

THE APPLICATION OF SENSOR NETWORKS WITHIN MOBILE CELL NETWORKS

Mobile devices such as mobile phones are getting increasingly popular by the year. Currently, most users cannot imagine doing without these devices that support our daily lives. Using them, however, entails a certain drawback, namely having to conclude an agreement with the mobile network operator, where the user undertakes to operate the device in keeping with the set rules and pay fixed amounts for the communications service provided by the operator. The authors’ overarching idea is both to reduce the costs of using mobile phones and achieves total independence from the mobile operator. To achieve this goal they propose a communications system that omits the mobile network operator’s infrastructure using methods taken from sensor networks. With the technological advancements it is now possible to manufacture devices whose size is in the order of centimetres. This enables the use of sensor nodes on an unprecedented scale and opens new opportunities even for a private user. Initially, sensor networks were wired solutions. The development of wireless and data processing technologies has helped sensor devices to take over a larger area of research. The purpose of this paper is to present the concept proposal of a free and reliable mobile network

Development of a stopping rule of clustering performance by using the connected acyclic graph

In this article the technique of the analysis of a stopping rule for the data preclustering algorithm without the prior information about the number of clusters with the use of a connected acyclic graph is introduced. The connected acyclic graph (tree) makes it possible to represent the interconnection between the objects in input data. The stopping rule allows a halt at the some step assuming that further clusterization will not cause finding new clusters. The core of the analysis was the application of the preclustering algorithm and the stopping rule to the series of input data which were represented by sample cases of input data. Sample cases were input data with normal distribution law which belonged either to a single group or to many groups. The analysis has shown the advantages of the stopping rule for the data preclustering algorithm.