A polygon model for wireless sensor network deployment with directional sensing areas.

Sensors (Basel)

Department of Computer Science, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan; E-Mail:

Published: September 2012

The modeling of the sensing area of a sensor node is essential for the deployment algorithm of wireless sensor networks (WSNs). In this paper, a polygon model is proposed for the sensor node with directional sensing area. In addition, a WSN deployment algorithm is presented with topology control and scoring mechanisms to maintain network connectivity and improve sensing coverage rate. To evaluate the proposed polygon model and WSN deployment algorithm, a simulation is conducted. The simulation results show that the proposed polygon model outperforms the existed disk model and circular sector model in terms of the maximum sensing coverage rate.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3267207PMC
http://dx.doi.org/10.3390/s91209998DOI Listing

Publication Analysis

Top Keywords

polygon model
16
deployment algorithm
12
wireless sensor
8
directional sensing
8
sensing area
8
sensor node
8
wsn deployment
8
sensing coverage
8
coverage rate
8
proposed polygon
8

Similar Publications

RNA nanoparticles, derived from the packaging RNA three-way junction motif (pRNA-3WJ) of the bacteriophage phi29 DNA packaging motor, have been demonstrated to be thermodynamically and chemically stable, with promise as a nanodelivery system. : A previous study showed that RNA nanoparticles with antiangiogenic aptamers (anti-vascular endothelial growth factor (VEGF) and anti-angiopoietin-2 (Ang2) aptamers) inhibited cell proliferation via WST-1 assay. To further investigate the antiangiogenic potential of these RNA nanoparticles, a modified three-dimensional (3D) spheroid sprouting assay model of human umbilical vein endothelial cells was utilized in the present study.

View Article and Find Full Text PDF

Studying genetic variability through the phenotypic performance of genotypes is crucial in the breeding program. Therefore, evaluating both yield performance and stability across diverse environments is essential in yield trials to identify high-yield potential and stable cultivars. In this study, we employed 12 univariate and 10 multivariate stability models to analyze how genotype (G), environment (E), and their interaction (G × E) affect the yield performance of 32 barley genotypes across 10 environments.

View Article and Find Full Text PDF

YOLOSeg with applications to wafer die particle defect segmentation.

Sci Rep

January 2025

Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City, 243, Taiwan.

This study develops the you only look once segmentation (YOLOSeg), an end-to-end instance segmentation model, with applications to segment small particle defects embedded on a wafer die. YOLOSeg uses YOLOv5s as the basis and extends a UNet-like structure to form the segmentation head. YOLOSeg can predict not only bounding boxes of particle defects but also the corresponding bounding polygons.

View Article and Find Full Text PDF

The comprehensive benefit evaluation of LID based on multi-criteria decision-making methods faces technical issues such as the uncertainties and vagueness in hybrid information sources, which can affect the overall evaluation results and ranking of alternatives. This study introduces a multi-indicator fuzzy comprehensive benefit evaluation approach for the selection of LID measures, aiming to provide a robust and holistic framework for evaluating their benefits at the community level. The proposed methodology integrates quantitative environmental and economic indicators with qualitative social benefit indicators, combining the use of the Storm Water Management Model (SWMM) and ArcGIS for scenario-based analysis, and the use of hesitant fuzzy language sets and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for decision-making.

View Article and Find Full Text PDF

Deep learning-based morphometric analysis of zebrafish is widely utilized for non-destructively identifying abnormalities and diagnosing diseases. However, obtaining discriminative and continuous organ category decision boundaries poses a significant challenge by directly observing zebrafish larvae from the outside. To address this issue, this study simplifies the organ areas to polygons and focuses solely on the endpoint positioning.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!