Optimized Received Signal Strength-Based Radio Map Interpolation for Indoor Positioning Systems

The combination of extreme weather conditions and economic activities has increased the number of corrosive agents in the air. This phenomenon will lead to rust formation in various assets, such as vehicles and steel properties. A Corrosion Hazard Map of Peninsular Malaysia was produced by considering parameters from climate and wet deposition information that influenced atmospheric corrosion: wetness, rainfall, chloride, nitrate, and sulphate. The inverse distance weighting interpolation technique was applied to generate the map. The study discovered that the corrosion rate at the southern and central part of Peninsular Malaysia is at level five, while the highland area is at level one. The Corrosion Hazard Map produced from this study would become as additional information that possibly will benefit various sectors such as metal industries, construction, land development, automotive coating technology, and others.

Incorporation of principal component analysis, geostatistical interpolation approaches and frequency-space-based models for portraying the Cu-Au geochemical prospects in the Feizabad district, NW Iran

<p>The assessment of nutrient potential is crucial for enhancing tea production and achieving sustainable agricultural goals. Thus, the present study was carried out to assess and map the soil nutrient potential for tea plants through laboratory and geostatistical methods. A total of 74 random soil samples with 3 replications were collected from 0 to 9 cm (topsoil) and 9 to 18 cm (subsoil) depths from Oodaleah Tea Garden (OTG) which belongs to Fatikchhari Upazila of Chattogram District in Bangladesh. All the laboratory analyses were done following standard procedures and maps of individual soil indices were prepared through the Inverse Distance Weighted (IDW) interpolation technique using ArcGIS 10.7 software. The integration of maps for nutrient potential was performed following the Analytic Hierarchy Process (AHP). The mean concentrations of organic matter (OM), total nitrogen (TN), available phosphorus (AvP), available potassium (AvK), and available calcium (AvCa) were found higher at 0 to 9 cm compared to 9 to 18 cm soil depth with significant (<em>p</em> < 0.05) difference among the sampling sites. The maps of individual soil parameters revealed distinct spatial differences at 0 to 9 cm and 9 to 18 cm soil depths. The total study area covered 5.99 km<sup>2</sup>. Integrated nutrient potential mapping showed that moderately suitable (44.15%) accounted for the largest proportion, followed by highly suitable (26.03%), generally suitable (21.67%), marginally suitable (7.06%), and unsuitable area (1.08%) of the tea plantation suitability evaluation. The integrated map of nutrient potential will aid in better management of the tea soils.</p>

Six criteria pollutants were monitored in varied zones of Coimbatore City during a period of one year and correlated with the natural meteorological conditions such as temperature, relative humidity (RH), wind speed and precipitation using SPSS 23. The Spatial distribution analysis was determined by Inverse Distance Weighted (IDW) Interpolation Technique using ArcGIS 10.5. The interpolated values of the particulate matter (PM) i.e., PM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</inf> (102-117 $\mu g/m^{3}$) and PM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</inf> (66-75 $\mu g/m^{3}$) showed a higher concentration in comparison with the permissible levels in 3 locations and Carbon monoxide (CO) in all the 5 locations. The Air Quality Index (AQI) in the different zones ranged between 77.04 to 151.01 indicating unhealthy condition. A significant impact of meteorological parameters in the air pollution distribution, showing positive correlation between the concentration of air pollutants and temperature (0.6- 0.8), wind speed and turbulence (0.1- 0.4) and a negative correlation with precipitation (-0.1 - -0.4) was found.

The degradation of air quality is the most concerned issue of our society due to its harmful impacts on human health, especially in cities with rapid urbanization and population growth like Hanoi, the capital of Vietnam. This study aims at developing a new approach that combines data-driven models and interpolation technique to develop the PM10 concentration maps from meteorological factors for the central area of Hanoi. Data-driven models that relate the PM10 concentration with the meteorological factors at the air quality monitoring stations in the study area were developed using the Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) algorithms. Models’ performance comparison showed that ANN models yielded better goodness-of-fit indices than MLR models at all stations in the study area with average coefficient of correlation (r) and Nash–Sutcliffe Efficiency Index (NSE) of 0.51 and 0.34 for the former, and 0.7 and 0.49 for the latter. These indices indicates that the ANN-based data-driven models outperformed the MLR-based models. Thus, the ANN-based models and the Inverse Distance Weighting (IDW) interpolation technique were then combined for mapping the monthly PM10 concentration with a spatial resolution of 1 km from global meteorological data. With this combination, the PM10 concentration maps account for both local PM10 concentration and impacts of spatio-temporal variations of meteorological factors on the PM10 concentration. This study provides a promising method to predict the PM concentration with a high spatio-temporal resolution from meteorological data.

Objectives: To determine the Soil Organic Carbon Density (SOCD) and to observe distribution of Soil Organic Carbon (SOC) in the Bhima River sub-watershed of Solapur. Methods/Analysis: The spatial distribution of Soil Organic Carbon and it density was shown by Inverse Distance Weighted (IDW) interpolation technique using ArcGIS and the subsequent map generated in the process account for the fertility across the watershed and its longitudinal section. Total 36 sampling points were used for soil sampling and are processed for the output map. The land cover type of barren land and agriculture field were selected for soil sampling. Walkley and Black method was used for analysis of soil organic carbon and organic matter was calculated as a derived parameter. Findings: It is found that average soil bulk density was 1.15 gm cm-3 and the difference in SOCD from the minimum to maximum was 2.85 kg/m2 with average of 1.87 kg/m2. The lower values carbon was at high elevation and higher values were recorded nearer to mouth of river. There seems to be insignificant correlation between bulk density and SOC. Applications/Improvements: The SOC comparison between different land cover, clearly shows that agricultural terrains were richer as compared to barren lands. The outcome of the research could be of importance for the policy makers and management of agriculture, as SOC is the major nutrient in terrestrial ecosystems.

Excessive exposure schoolchildren to air pollution can lead to long-lasting health problems, allergies and respiratory disease. It is well known that the major factors contributing to increase of air pollution are motor vehicles and industries. Thus, it is important to analyze the spatial temporal air pollutant concentrations and its relation with school location as the location of schools and its surrounding can increase their exposure. In this study, six schools in Johor were selected and the land use surrounding the schools were updated using ArcGIS. The Inverse Distance Weighting (IDW) interpolation technique was used to identify which schools’ area in Johor has a higher range of air pollutant concentration. There are four air pollution parameters obtained from the Department of Environment (DOE) which are PM2.5, CO, O3 and SO2. Hourly air pollutant concentration reading was obtained from the DOE in order to analyze air pollutant concentration during school period. The results obtained from the IDW technique showed that Sekolah Menengah Pasir Gudang (2) located in Pasir Gudang, Malaysia has reached a very unhealthy and hazardous level as compared to other schools in Johor. On the other hand, Sekolah Menengah Kebangsaan Tanjung Pengelih, Pengerang, Malaysia showed good to unhealthy range as compared to other schools in Johor. The spatial autocorrelation tool was used to analyze the relationship between the air pollution concentration and the school’s location in Johor. The results showed that the Moran’s Indices is positive showing a strong relationship that is clustering. It can be stated that there is a relationship between air pollutant concentrations with the school locations.

Industries in Delhi: Air pollution versus respiratory morbidities

Total volatile organic compounds (TVOCs),respiratory suspended particulate matter (RSPM) i.e. (PM10,PM2.5) and fine particulate matter (FPM) i.e.PM1 have been found to exert negative impact on the women health, and may even lead cancer on prolonged exposure. Since women spend most of their time indoors, they are at greater risk of indoor pollution. This study was conducted to see the effect of indoor RSPM, FPM and TVOCs on women health predominantly on young women dwellers (specifically categorized into pre-teenagers i.e., 8–12 years, teenagers i.e., 13–19 years and post-teenagers i.e., 20–21 years). Indoor monitoring was conducted from November 2022 to February 2023 in six different urban households of Lucknow, capital city of most polluted and populated state of India. Envirotech APM 550 for RSPM, APM 577 for FPM and portable sensors (BR-SMART) were used to measure TVOCs. The highest average indoor concentrations was found to be 250.1 ± 14.11µg/m3 (PM10) at Rajajipuram, 140.62 ± 19.71µg/m3 (PM2.5) at Indranagar, 27.60 ± 1.87µg/m3 (PM1) and 934 ± 70.41 µg/m3 (TVOCs) at Kaiserbagh. To elucidate the spatial dynamics of these pollutants, the Inverse Distance Weighting (IDW) interpolation technique was employed. Additionally, site-specific analysis of PM mass ratios (PM2.5/PM10, PM1/PM2.5 and PM1/PM10) elucidated the particle size distribution and their sources, such as vehicle emissions and secondary aerosols, significantly enhancing the scientific understanding of aerosol dynamics in these urban settings. The study utilised sophisticated statistical methods in it’s source apportionment analysis to identify and measure the specific contributions of various indoor sources to the total pollution levels. Health risk assessment was also determined using average daily dose (ADD), excess lifetime cancer risk (ELCR) and hazard quotient (HQ) for carcinogenic and non-carcinogenic risk. ELCR values for PM1 and PM2.5 surpassed the permissible limit in every house, demonstrating a probable cancer-causing risk and HQ value also exceeded the minimum allowable value for 20–21 year age group at all of the locations, indicating substantial health risk from exposure. International Committee of Radiological Protection Model (ICRP) and Multiple Path Particle Dosimetry (MPPD) modeling were used to see the regional deposition of PMs on the young women dwellers. The ICRP results showed that deposition is higher for PM10 in head airways and results of MPPD revealed that the highest deposition in pulmonary region was tend to be in post teenagers, whereas, highest deposition in head region was in pre teenagers and teenagers. Hence, consociate to harmful pollutants at these stages might be tremendously hazardous which are often overlooked. According to the study, the results are very alarming and concerning, the indoor air exposure to PMs and TVOCs may raise the risk of various diseases in young women and may ultimate lead to cancers. High concentration exposure during young age is very harmful and may be a cause of various diseases during their motherhood and even cause concerning and problems in new borns.

This article explores the novel application of the Google Earth Engine (GEE) platform using the inverse distance weighting (IDW) interpolation technique based on an improved version of the modified Shephard method for the development of specialized geotechnical soil maps (GSMs) for mega-district Lahore. The study area was assessed at multiple depths in terms of key geotechnical properties. The result depicts a predominance of fine-grained soil at a shallow depth of 3 m exhibiting low to moderate soil stiffness, consistency and activity along with a few zones of problematic soil with adverse geotechnical characteristics which require due attention. Whereas beyond 3 m the study area exhibits higher bearing values, low plasticity and non-active soil. Besides, the chemical content level was within permissible limits except for a few spots. The comprehensive statistical evaluation based on key performance indices indicates the efficient performance of the novel GEE-based IDW algorithm to yield significantly accurate maps.

A novel mesh deformation technique is developed based on the Delaunay graph mapping method and the inverse distance weighting (IDW) interpolation. The algorithm maintains the advantages of the efficiency of Delaunay graph mapping mesh deformation while it also possesses the ability of better controlling the near surface mesh quality. The Delaunay graph is used to divide the mesh domain into a number of sub-domains. On each sub-domain, the inverse distance weighting interpolation is applied, resulting in a similar efficiency as compared to the fast Delaunay graph mapping method. The paper will show how the near-wall mesh quality is controlled and improved by the new method

In order to implement the spatial Inverse Distance Weighting (IDW) interpolation in parallel, we use the quadtree approach to decompose the spatial domain and execute the IDW interpolation on these quads through the parametric tasks on Windows HPC platform. The whole implementation includes the executables of QuadDC, IDW, output gathering and data visualization which should be run serially. Compared with the traditional method to integrate each executable manually, the Job Manager of Windows HPC utilizes multi-task job to serialize all the executables as a workflow. The experiments test two cases of IDW0 and IDW6 with different domain clustering regions, and show that our implementation improves the functionality integration and efficiency of the spatial IDW interpolation.

A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations. The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths. By considering multipath signal propagations from different APs, the inherent noise from these signal paths can be alleviated. Firstly, locations of the signal data with standard deviations higher than the threshold are identified. The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints. The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance. Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44% compared to the conventional interpolation techniques such as the Inverse Distance Weighting, Kriging, and single Path Loss Model. As a result, we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.

This paper focuses on the implementation problems for indoor positioning system, whose main process includes building fingerprint database, ranging and matching online. During the construction of a fingerprint map, signal characteristics are collected at known positions or reference points in a building. While in the ranging and online phase, mobile device application scans access points around and selects two access points from which received signal strengths are stronger, then we convert the signal strengths into a rectangular region where the user is surely inside it. Meanwhile, the current fingerprint probability distributions are compared with the offline radio fingerprint map. The user's location is estimated by calculating the smallest Bhattacharyya distance in the above-mentioned rectangular region. Experiments show that our method by means of parameter optimizing reaches a median error of 2.82m, which is superior to the median error of 3.4m by traditional Wi-Fi fingerprint.

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.