Biljana Risteska Stojkoska

Application of Wireless Sensor Networks for Indoor Temperature Regulation

Wireless sensor networks take a major part in our everyday lives by enhancing systems for home automation, healthcare, temperature control, energy consumption monitoring, and so forth. In this paper we focus on a system used for temperature regulation for residential, educational, industrial, and commercial premises, and so forth. We propose a framework for indoor temperature regulation and optimization using wireless sensor networks based on ZigBee platform. This paper considers architectural design of the system, as well as implementation guidelines. The proposed system favors methods that provide energy savings by reducing the amount of data transmissions through the network. Furthermore, the framework explores techniques for localization, such that the location of the nodes can be used by algorithms that regulate temperature settings.

Improved MDS-based algorithm for nodes localization in wireless sensor networks

With the recent development of technology, wireless sensor networks (WSN) are becoming an important part of many applications. Knowing the exact location of each sensor in the network is very important issue. Therefore, the localization problem is a growing field of interest. Adding GPS receivers to each sensor node is costly solution and inapplicable on nodes with limited resources. Additionally, it is not suitable for indoor environments. In this paper, we propose an algorithm for nodes localization in WNS based on multidimensional scaling (MDS) technique. Our approach improves MDS by distance matrix refinement. Using extensive simulations we investigated in details our approach regarding different network topologies, various network parameters and performance issues. The results from simulations show that our improved MDS (IMDS) algorithm outperforms well known MDS-MAP algorithm [1] in terms of accuracy.

Web Interface for Habitat Monitoring Using Wireless Sensor Network

In the near future, the wireless sensor networks (WSN) consisting of hundreds or thousands of small sensor devices are expected to become increasingly popular due to their low cost and easy use. As a distributed system, they are usually deployed in unattended environments and aim to collect useful information from the sensed area. In this paper, we present a web interface model for habitat monitoring using wireless sensor network, which can be used for wide range of applications. The interface we developed provides a userfriendly environment and a set of functionalities that easies the interaction between the end-users and the WSN. Our web interface is characterized with modularity, which makes applications easily extensible. Keywords-Wireless Sensor Networks, Web interface, Habitat Monitoring, Mica motes

Nodes Localization in 3D Wireless Sensor Networks Based on Multidimensional Scaling Algorithm

In the recent years, there has been a huge advancement in wireless sensor computing technology. Today, wireless sensor network (WSN) has become a key technology for different types of smart environment. Nodes localization in WSN has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priory known nodes positions. Adding GPS receivers to each node is an expensive solution and inapplicable for indoor environments. In this paper, we implemented and evaluated an algorithm based on multidimensional scaling (MDS) technique for three-dimensional (3D) nodes localization in WSN using improved heuristic method for distance calculation. Using extensive simulations we investigated our approach regarding various network parameters. We compared the results from the simulations with other approaches for 3D-WSN localization and showed that our approach outperforms other techniques in terms of accuracy.In the recent years, there has been a huge advancement in wireless sensor computing technology. Today, wireless sensor network (WSN) has become a key technology for different types of smart environment. Nodes localization in WSN has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priory known nodes positions. Adding GPS receivers to each node is an expensive solution and inapplicable for indoor environments. In this paper, we implemented and evaluated an algorithm based on multidimensional scaling (MDS) technique for three-dimensional (3D) nodes localization in WSN using improved heuristic method for distance calculation. Using extensive simulations we investigated our approach regarding various network parameters. We compared the results from the simulations with other approaches for 3D-WSN localization and showed that our approach outperforms other techniques in terms of accuracy.

Internet of Things Framework for Home Care Systems

The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency.

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.

Robust localisation algorithm for large scale 3D wireless sensor networks

Nodes positioning has recently been of great interest in wireless networks owing to its crucial role in many applications. In wireless sensor networks WSNs, the task of localising sensor nodes with unknown position is important for efficient network configuration and operation. This challenge has stimulated research of various localisation algorithms. In this paper we propose robust localisation algorithm for large scale three-dimensional 3D WSNs based on multidimensional scaling MDS. Our approach has two main improvements over classical MDS algorithm. Firstly, it uses heuristic approach for distance matrix calculation, and secondly, it applies Levenberg-Marquardt LM method for absolute map refinement using received signal strength RSS measurements. Furthermore, the performance of the proposed approach is compared to other 3D WSN localisation techniques and it is shown that the proposed approach outperforms other techniques for 3D localisation.

A taxonomy of localization techniques based on multidimensional scaling

Localization in Wireless Sensor Networks (WSNs) has been a challenging problem in the last decade. The most explored approaches for this purpose are based on multidimensional scaling (MDS) technique. The first algorithm that introduced MDS for nodes localization in sensor networks is well known as MDS-MAP. Since its appearance in 2003, many variations of MDS-MAP have been proposed in the literature. This paper aims to provide a comprehensive survey of the localization techniques that are based on MDS. We classify MDS-based algorithms according to different taxonomy features and different evaluation metrics.

SHOPIN: Semantic Homogeneity Optimization in Protein Interaction Networks

Protein interaction networks (PINs) are argued to be the richest source of hidden knowledge of the intrinsic physical and/or functional meanings of the involved proteins. We propose a novel method for computational protein function prediction based on semantic homogeneity optimization in PIN (SHOPIN). The SHOPIN method creates graph representations of the PIN augmented by inclusion of the semantics of the proteins and their interacting contexts. Network wide semantic relationships, modeled using random walks, are used to map the augmented PIN graphs in a new semantic metric space. The method produces a hierarchical partitioning of the PIN optimal in terms of semantic homogeneity by iterative optimization of the ratio of between clusters dissimilarities and within clusters similarities in the new semantic metric space. Function prediction is done using cluster wide-hierarchy high function enrichment. Results validate the rationale of the SHOPIN method placing it right next to state-of-the-art approaches performance wise.

Intelligent analysis of economic parameters of European countries

Engineers today are often bound to work with complex data, with valuable knowledge to be extracted and models to be built for predicting future behavior. With a given set of economic parameters for the European countries, the goal of this paper is to analyze if they can form meaningful community structure. To this end, we propose a network science approach to economic time series analysis. The first step is the creation of corresponding graphs as the most adequate data representation that can capture multiple relations and dependencies among entities of interest. The preprocessing steps for creating a graph include calculating correlation coefficients for the parameters, which are used for weighing the graph. The weighted graphs are then analyzed for their underlying structure using clustering algorithms which produce various communities depending on the settings employed. Experiments are performed using different correlation calculations for graph weighing and different settings for cluster extraction and the produced communities are analyzed in terms of their quality, both in view of modularity score and economic meaning. Results show that the proposed approach successfully captures meaningful economic relationships between European countries and can be a solid base on which future, more complex analysis can be build.