Distributed service environment (smart spaces) security model development

Access control mechanisms play a key role in many areas of computer science, however, for the information provided on the basis of semantic web and established solutions don't exist. This work focuses on the research in this area, in particular to ensure the information security in distributed service environments (smart spaces), which are the most promising application of standards and technologies of semantic web. The main focus of this paper will be devoted to the analysis and investigation solutions to develop security model and mechanisms for a smart space platform, as well as its comprehensive testing. As a test platform was chosen Smart-M3 platform, which has the highest degree of elaboration and maximum prospects for further applications

Scalable Algorithms for Simultaneous Mapping and Localization of Mobile Robot Swarms

The chapter is devoted to the development of scalable algorithms for multi-agent solution of the SLAM problem. These algorithms are applicable to robots with limited computational resources, having limited computational power and memory, small spatial size, and power from a portable battery. To simplify the description, only robots equipped with LIDAR are considered. The main focus is as follows: a scalable multi-agent SLAM algorithm based on Dempster-Shafer theory; an algorithm for filtering two-dimensional laser scans to free up computational resources; evaluation of the accuracy of the map and trajectory constructed by the multi-agent algorithm; performance evaluation on resource-limited computing devices

Geo2Tag performance evaluation

Today volume of the Internet traffic growths very fast. This trend affects Web-application, with the number of users and amount of traffic also increasing their workload. That's why developers need to achieve maximum performance on existing hardware. Software optimization allows solving this problem. Geo2Tag is an open source platform for location-based services (LBS), which provide web interfaces for them. Initially, it was developed as an educational project which goal was to give students experience in open source projects development. But now number of supported functions and number of users (users of LBS and developers) for platform is increasing, and in this situation platform performance is not enough. This paper describes Geo2Tag platform performance evaluation and optimization

Traffic prediction in wireless mesh networks using process mining algorithms

Prediction of the traffic flow in particular systems will expedite discovering of an optimal path for packet transmitting in dynamic wireless networks. The main goal is to predict traffic overload while changing a network topology. Machine learning techniques and process mining enables prediction of the traffic produced by several moving nodes. Several related approaches are observed. The idea of process mining approach is proposed

Gated recurrent unit decision model for device argumentation in ambient assisted living

The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%