An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms

Compressive Sensing: Analysis of Signals in Radio Astronomy

The compressive sensing (CS) theory says that for some kind of signals there is no need to keep or transfer all the data acquired accordingly to the Nyquist criterion. In this work we investigate if the CS approach is applicable for recording and analysis of radio astronomy (RA) signals. Since CS methods are applicable for the signals with sparse (and compressible) representations, the compressibility of RA signals is verified. As a result, we identify which RA signals can be processed using CS, find the parameters which can improve or degrade CS application to RA results, describe the optimum way how to perform signal filtering in CS applications. Also, a range of virtual LabVIEW instruments are created for the signal analysis with the CS theory

Lietuvos olimpinės slidinėjimo rinktinės narių aerobinio pajėgumo pokyčiai per metinį pasirengimo ciklą

Vytauto Didžiojo universitetasŠvietimo akademij

Daugelio agentų e. socialinės rūpybos paslaugų sistema išmaniojo ekologiško socialinio būsto gyventojams

The paper presents further development steps of recently proposed model of an adaptive multi-agent-based e-social care system by introducing in it an intelligent power controller to supporting people in a Smart Ecological and Social Apartment, the SESA. The schematic diagrams of the controller are proposed for adaptive control of motors and LED lights as well as for measurements of EDA, HR, and FT signals of inhabitancies in the SESA. The proposed in this paper multi-agent system consists of two groups of agents, first, the agents for human’s physiological data collection, analysis and emotional state detection, and, second, the agents for an intelligent power control which takes environmental and hardware parameters and, with the use of embedded agents, provides adaptive control of LED lights, temperature as well as of power consuming by motors used in the SESA in accordance with the human state defined by the first group of agents