Developing languages and tools which depend on Domain-Specific Modeling (DSM) methodology, represent a hot topic nowadays, but building a set of graphical tool is often very complex process which takes a lot of time and it often highly depends on developer’s knowledge and reliable frameworks. Today, a leading role of Integrated Development Environment (IDE) tools on market has an open source – Eclipse platform, which became de-facto standard and primary choice for developing a DSM environment. The choice of user interface framework, to solve the traditional problems of custom modeling in an elegant fashion, strongly influences the development process and system’s lifetime costs spent on maintenance. In order to realize which of the open source frameworks based on Eclipse is better choice, allowing developers to establish a very flexible graphical environment for editing the models, a comparative analysis of Graphiti and Sirius frameworks for developing a DSM Graphical Editor is done with aim to define pros and cons of each of them.

This paper presents a survey of applications of fuzzy logic in real Wireless Sensor Networks (WSNs). The use of fuzzy logic in WSNs is shown to be a promising technique since it allows combining and evaluating diverse parameters in an efficient manner. Fuzzy logic is a good approach due to the execution requirements can be easily supported by sensor nodes, while it is able to improve the overall network performance. The potential of fuzzy logic goes beyond traditional systems and can be used in many research fields (statistics, quality control, optimization techniques . . . ), allowing multidisciplinary approaches and performance improvements. In WSNs, fuzzy logic has been used to improve decision-making, reduce resource consumption, and generally increase performance through efficient deployment, localization, clustering and cluster head election, routing, data aggregation, security, etc.

Wireless sensor networks (WSN) are a rapidly growing area for research and commercial development with very wide range of applications. Using WSNs many critical events like fire can be detected earlier to prevent loosing human lives and heavy structural damages. Integration of soft computing techniques on sensor nodes, like fuzzy logic, neural networks and data mining, can significantly lead to improvements of critical events detection possibility. Using data mining techniques in process of patterns discovery in large data sets it’s not often so easy. A several algorithms must be applied to application before a suitable algorithm for selected data types can be found. Therefore, the selection of a correct data mining algorithm depends on not only the goal of an application, but also on the compatibility of the data set. This paper focuses on comparative analysis of various data mining techniques and algorithms and in that purpose three different experiments on WSN fire detection data are proposed and performed. The primary goal was to see which of them has the best classification accuracy of fuzzy logic generated data and is the most appropriate for a particular application of fire detection.

Considering that some systems have limitation in memory and processing power, storing a full fuzzy rule base might be a drawback. Large rule base might considerably slow down the whole system and significantly affect performance. Thus, the purpose of rule reduction method implementation is simplifying the decision process and making the rule base traversal faster. In this paper several methods for rule reduction are presented and one of them - FURIA is applied to system for fire possibility determining. Applying FURIA, rule base is significantly reduced and tested by simulation of temperature rises in a several cases for high and low temperatures. A data analysis for this measurement shows that decreased rule base has slightly lower accuracy in contrast to a system with full rule base, which means that, by reducing a number of rules, system's energy and memory consumption can be decreased, transmission costs can be reduced and critical event detection made faster.

. Extreme events like fire can cause massive damage to indoor areas and life threatening conditions. Early residential fire detection is important for life preservation, prompt extinguishing and reducing damage. To detect fire, one or a combination of sensors (heat detectors, smoke detectors, flame detectors) and a detection algorithm are needed. The sensors might be a part of a wireless sensor network (WSN) or work independently. One of the most frequently used heat detectors is the rate-of-rise heat detector. In this paper some of the data mining algorithms on simulation data of the rate-of-rise heat detector are applied. Data mining seems to be an effective technique for discovering useful knowledge from a large amount of data observed by many sensors. Prediction in sensor networks can be performed in the way that each sensor learns a local predictive model for the global target classes, using only its local input data. Only the predicted target class for each reading is then transmitted to the gateway or to the base station. One important class of such algorithms are predictors, which use the sensor inputs to predict some output function of interest. The purpose of the paper is to analyze different classification algorithms in the case of rate-of-rise heat detector to see which of the applied techniques led to higher accuracy and fewer errors.

As information systems are becoming more distributed, with an emphasis on service oriented technologies, it is necessary to implement security on access and communication with them. On market vendors offer different software solutions in a form of frameworks in a domain of service application security, and different concepts post certain specific constraints on design of security subsystem of such systems. In this paper current methods in RESTful service security will be depicted, and extension elaborated in detail for Jersey RESTful framework for the purpose of efficiency enhancement.

The paper describes the application of project management tools used for educational purposes. We first look at the current state of software engineering courses in institutions of higher education. We then offer improvements and solutions to existing approaches through the introduction of software tools; we show that Project Management software packages used in the role of electronic support systems can reduce issues related to management and control in educational software projects for students in software engineering courses.