arXiv subjects

Biljana Stojkoska

Publications and source records attributed to Biljana Stojkoska.

4 recordsLinked to original sources

arXiv2017

Project Based Learning of Embedded Systems

Traditional teaching, usually based on lectures and tutorials fosters the idea of instruction-driven learning model where students are passive listeners. Besides this approach, Project Based Learning (PBL) as a different learning paradigm is standing behind constructivism learning theory, where learning from real-world situations is put on the first place. The purpose of this paper is to present our approach in learning embedded systems at our University. It is based on combination of traditional (face-to-face) learning and PBL. Our PBL represents an interdisciplinary project based on wireless sensor monitoring of real-world environment (greenhouse). The students use UML that was shown as an excellent tool for developing such a projects. From the student perspective, we found that this high level of interdisciplinary is very valuable from the point of view of facing the students with real-life problems.

cs.OH↗

arXiv2016

N-queens-based algorithm for moving object detection in distributed wireless sensor networks

The main constraint of wireless sensor networks (WSN) in enabling wireless image communication is the high energy requirement, which may exceed even the future capabilities of battery technologies. In this paper we have shown that this bottleneck can be overcome by developing local in-network image processing algorithm that offers optimal energy consumption. Our algorithm is very suitable for intruder detection applications. Each node is responsible for processing the image captured by the video sensor, which consists of NxN blocks. If an intruder is detected in the monitoring region, the node will transmit the image for further processing. Otherwise, the node takes no action. Results provided from our experiments show that our algorithm is better than the traditional moving object detection techniques by a factor of (N/2) in terms of energy savings.

cs.MM↗

arXiv2016

Wireless Sensor Networks Localization Methods: Multidimensional Scaling vs. Semidefinite Programming Approach

With the recent development of technology, wireless sensor networks are becoming an important part of many applications such as health and medical applications, military applications, agriculture monitoring, home and office applications, environmental monitoring, etc. Knowing the location of a sensor is important, but GPS receivers and ophisticated sensors are too expensive and require processing power. Therefore, the localization wireless sensor network problem is a growing field of interest. The aim of this paper is to give a comparison of wireless sensor network localization methods, and therefore, multidimensional scaling and semidefinite programming are chosen for this research. Multidimensional scaling is a simple mathematical technique widely-discussed that solves the wireless sensor networks localization problem. In contrast, semidefinite programming is a relatively new field of optimization with a growing use, although being more complex. In this paper, using extensive simulations, a detailed overview of these two approaches is given, regarding different network topologies, various network parameters and performance issues. The performances of both techniques are highly satisfactory and estimation errors are minimal

cs.DC↗

arXiv2016

Cluster-based MDS Algorithm for Nodes Localization in Wireless Sensor Networks with Irregular Topologies

Nodes localization in Wireless Sensor Networks (WSN) has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priori known nodes positions. One solution to the problem is by adding GPS receivers to each node. Since this is an expensive approach and inapplicable for indoor environments, we need to find an alternative intelligent mechanism for determining nodes location. In this paper, we propose our cluster-based approach of multidimensional scaling (MDS) technique. Our initial experiments show that our algorithm outperforms MDS-MAP[8], particularly for irregular topologies in terms of accuracy.

cs.DC↗