AI Article Synopsis
- Commercial interests in indoor localization have grown significantly, driven by the demand for reliable indoor positioning solutions, with Wi-Fi RSS-based techniques being particularly popular due to the widespread availability of Wi-Fi access points.
- This paper introduces a new hierarchical classification-based method for indoor localization that utilizes an improved K-Means clustering algorithm to create overlapping zones, enhancing the accuracy of the positioning process.
- The method combines the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) for localization, achieving a position classification accuracy improvement of 1.4% to 3.2% and reducing average positioning error by 10% to 22% in real-world tests compared to existing methods.
Article Abstract
Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070398 | PMC |
| http://dx.doi.org/10.3390/s20041067 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Evaluating endogenous viral targets as potential treatment monitoring surrogates for onsite non-potable water reuse.
Environ Sci (Camb)
February 2024
U.S. Environmental Protection Agency, Office of Research and Development, 26 W. Martin Luther King Drive, Cincinnati, OH 45268, USA.
Onsite non-potable water reuse systems (ONWS) are decentralized systems that treat and repurpose locally collected waters ( greywater or combined wastewater) for uses such as irrigation and flushing toilets. To ensure that treatment is meeting risk benchmarks, it is necessary to monitor the efficacy of pathogen removal. However, accurate assessment of pathogen reduction is hampered by their sporadic and low occurrence rates in source waters and concentrations in treated water that are generally below measurement detection limits.
View Article and Find Full Text PDFPALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones.
Int Conf Indoor Position Indoor Navig
October 2024
Computer Science and Engineering, University of California, Santa Cruz, Santa Cruz, United States.
In this paper, we present PALMS, an innovative indoor global localization and relocalization system for mobile smartphones that utilizes publicly available floor plans. Unlike most vision-based methods that require constant visual input, our system adopts a dynamic form of localization that considers a single instantaneous observation and odometry data. The core contribution of this work is the introduction of a particle filter initialization method that leverages the Certainly Empty Space (CES) constraint along with principal orientation matching.
View Article and Find Full Text PDFEnhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models.
Sensors (Basel)
January 2025
Smart Diagnostic and Online Monitoring, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, Germany.
This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models.
View Article and Find Full Text PDFA Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features.
Sensors (Basel)
January 2025
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement.
View Article and Find Full Text PDFSafety and Efficacy of Incorporating Actellic 300 CS into Soil Wall Plaster for Control of Malaria Vectors in Rural Northeastern Uganda.
Trop Med Infect Dis
December 2024
Med Biotech Laboratories, Kampala P.O. Box 9364, Uganda.
Indoor residual spraying (IRS) and the use of insecticide-treated bednets for malaria vector control have contributed substantially to a reduction in malaria disease burden. However, these control tools have important shortcomings including being donor-dependent, expensive, and often failing because of insufficient uptake. We assessed the safety and efficacy of a user-friendly, locally tailored malaria vector control approach dubbed "Hut Decoration for Malaria Control" (HD4MC) based on the incorporation of a WHO-approved insecticide, Actellic 300 CS, into a customary hut decoration practice in rural Uganda where millions of the most vulnerable and malaria-prone populations live in mud-walled huts.
View Article and Find Full Text PDFWant 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!