scholarly journals Using Smart Virtual-Sensor Nodes to Improve the Robustness of Indoor Localization Systems

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3912
Author(s):  
Guilherme Pedrollo ◽  
Andréa Aparecida Konzen ◽  
Wagner Ourique de Morais ◽  
Edison Pignaton de Freitas
Keyword(s):  
Health Systems ◽  
Indoor Localization ◽  
Sensor Nodes ◽  
Mobile Nodes ◽  
Virtual Sensors ◽  
Healthcare Facilities ◽  
Security Monitoring ◽  
Single Sensor ◽  
Essential Function ◽  
Localization Systems

Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization is an essential function in such e-health systems, and it typically relies on wireless sensor networks (WSN) composed of fixed and mobile nodes. Nodes in the network can become permanently or momentarily unavailable due to, for example, power failures, being out of range, and wrong placement. Consequently, unavailable sensors not providing data can compromise the system’s overall function. One approach to overcome the problem is to employ virtual sensors as replacements for unavailable sensors and generate synthetic but still realistic data. This paper investigated the viability of modelling and artificially reproducing the path of a monitored target tracked by a WSN with unavailable sensors. Particularly, the case with just a single sensor was explored. Based on the coordinates of the last measured positions by the unavailable node, a neural network was trained with 4 min of not very linear data to reproduce the behavior of a sensor that become unavailable for about 2 min. Such an approach provided reasonably successful results, especially for areas close to the room’s entrances and exits, which are critical for the security monitoring of patients in healthcare facilities.

scholarly journals Self-calibration and Collaborative Localization for UWB Positioning Systems

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Matteo Ridolfi ◽  
Abdil Kaya ◽  
Rafael Berkvens ◽  
Maarten Weyn ◽  
Wout Joseph ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Mobile Nodes ◽  
Positioning Systems ◽  
Self Calibration ◽  
Significant Research ◽  
Anchor Nodes ◽  
Collaborative Localization ◽  
The Cost ◽  
Localization Systems

Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

scholarly journals ‘The nurse did not even greet me’: how informed versus non-informed patients evaluate health systems responsiveness in South Africa

2021 ◽  
Vol 6 (4) ◽  
pp. e004360
Author(s):  
Dumisani MacDonald Hompashe ◽  
Ulf-G Gerdtham ◽  
Carmen S Christian ◽  
Anja Smith ◽  
Ronelle Burger
Keyword(s):  
Health Systems ◽  
Primary Healthcare ◽  
Healthcare Workers ◽  
Quality Care ◽  
Public Healthcare ◽  
Health Coverage ◽  
Access To Health Services ◽  
Healthcare Provision ◽  
Healthcare Facilities ◽  
Patients Experience

Introduction Universal Health Coverage is not only about access to health services but also about access to high-quality care, since poor experiences may deter patients from accessing care. Evidence shows that quality of care drives health outcomes, yet little is known about non-clinical dimensions of care, and patients’ experience thereof relative to satisfaction with visits. This paper investigates the role of non-clinical dimensions of care in patient satisfaction. Methods Our study describes the interactions of informed and non-informed patients with primary healthcare workers at 39 public healthcare facilities in two metropolitan centres in two South African provinces. Our analysis included 1357 interactions using standardised patients (for informed patients) and patients’ exit interviews (for non-informed patients). The data were combined for three types of visits: contraception, hypertension and tuberculosis. We describe how satisfaction with care was related to patients’ experiences of non-clinical dimensions. Results We show that when real patients (RPs) reported being satisfied (vs dissatisfied) with a visit, it was associated with a 30% increase in the probability that a patient is greeted at the facilities. Likewise, when the RPs reported being satisfied (vs dissatisfied) with the visit, it was correlated with a 15% increase in the prospect that patients are pleased with healthcare workers’ explanations of health conditions. Conclusion Informed patients are better equipped to assess health-systems responsiveness in healthcare provision. Insights into responsiveness could guide broader efforts aimed at targeted education and empowerment of primary healthcare users to strengthen health systems and shape expectations for appropriate care and conduct.

An Introduction to the TOA measurement for UWB indoor localization Systems

2021 ◽  
Author(s):  
B Venkata Krishnaveni ◽  
K Suresh Reddy ◽  
P Ramana Reddy
Keyword(s):  
Indoor Localization ◽  
Localization Systems

A Novel Inter-device Calibration for Wi-Fi-aided Indoor Localization Systems

2019 ◽  
pp. 610-619
Author(s):  
Miguel Martínez del Horno ◽  
Cristina Romero-González ◽  
Luis Orozco-Barbosa ◽  
Ismael García-Varea
Keyword(s):  
Indoor Localization ◽  
Localization Systems

scholarly journals Using Mobile Elements as Dynamic Bridges in Sparse Wireless Sensor Networks

2014 ◽  
pp. 107
Author(s):  
Hoang Dang Hai ◽  
Thorsten Strufe ◽  
Pham Thieu Nga ◽  
Hoang Hong Ngoc ◽  
Nguyen Anh Son ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Exchange ◽  
Data Transfer ◽  
Mobile Elements ◽  
Sensor Nodes ◽  
Pollution Monitoring ◽  
Wireless Sensor ◽  
Mobile Nodes ◽  
Road Distance

Sparse  Wireless  Sensor  Networks  using several  mobile  nodes  and  a  small  number  of  static sensor  nodes  have  been  widely  used  for  many applications,  especially  for  traffic-generated  pollution monitoring.  This  paper  proposes  a  method  for  data collection and forwarding using Mobile Elements (MEs), which are moving on predefined trajectories in contrast to previous works that use a mixture of MEsand static nodes. In our method, MEscan be used as data collector as well as dynamic bridges for data transfer. We design the  trajectories  in  such  a  way,  that  they  completely cover  the  deployed  area  and  data  will  be  gradually forwarded  from  outermost  trajectories  to  the  center whenever  a  pair  of MEs contacts  each  other  on  an overlapping road distance of respective trajectories. The method  is based  on  direction-oriented  level  and  weight assignment.  We  analyze  the  contact  opportunity  for data  exchange  while MEs move.  The  method  has  been successfully tested for traffic pollution monitoring in an urban area.

scholarly journals DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chong Han ◽  
Wenjing Xun ◽  
Lijuan Sun ◽  
Zhaoxiao Lin ◽  
Jian Guo
Keyword(s):  
Indoor Localization ◽  
Wireless Sensing ◽  
Training Phase ◽  
Training Time ◽  
Localization Accuracy ◽  
Localization System ◽  
Complex Models ◽  
Indoor Scenarios ◽  
Trained Network ◽  
Localization Systems

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

scholarly journals Improved GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

2021 ◽  
pp. 242-249
Author(s):  
M.Shahkhir Mozamir ◽  
◽  
Rohani Binti Abu Bakar ◽  
Wan Isni Soffiah Wan Din ◽  
Zalili Binti Musa
Keyword(s):  
Particle Swarm Optimization ◽  
Indoor Localization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Computational Time ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Local Neighborhood

Localization is one of the important matters for Wireless Sensor Networks (WSN) because various applications are depending on exact sensor nodes position. The problem in localization is the gained low accuracy in estimation process. Thus, this research is intended to increase the accuracy by overcome the problem in the Global best Local Neighborhood Particle Swarm Optimization (GbLN-PSO) to gain high accuracy. To compass this problem, an Improved Global best Local Neighborhood Particle Swarm Optimization (IGbLN-PSO) algorithm has been proposed. In IGbLN-PSO algorithm, there are consists of two phases: Exploration phase and Exploitation phase. The neighbor particles population that scattered around the main particles, help in the searching process to estimate the node location more accurately and gained lesser computational time. Simulation results demonstrated that the proposed algorithm have competence result compared to PSO, GbLN-PSO and TLBO algorithms in terms of localization accuracy at 0.02%, 0.01% and 59.16%. Computational time result shows the proposed algorithm less computational time at 80.07%, 17.73% and 0.3% compared others.

Sign in / Sign up

Export Citation Format

Share Document