Data-driven and machine learning-based approaches for mobile indoor positioning

Location is vital for numerous applications driven by uncountable mobile users and developers. The global navigation satellite system (GNSS) has been served for years to provide highprecision localization and relevant applications in outdoor scenarios. However, the low penetration of GNSS signal through walls and obstacles sharply decreases the positioning accuracy in the indoor environment. Consequently, various indoor positioning methods have been proposed in recent years to facilitate location-based services in indoor scenarios. In recent years, the widespread adoption of smartphones and other mobile devices has led users to generate an enormous amount of data that can be analyzed to extract valuable insights such as movement patterns, user behavior, and environmental conditions. As a result, people now have high expectations for the accuracy and reliability of these services. Advancements in sensor technologies such as Wi-Fi and Bluetooth have made it possible to collect precise data about indoor environments for positioning. Machine learning technologies have allowed for the identification of patterns and relationships that may not be immediately apparent to human analysts from sensor data. Deep learning, a subset of machine learning, can process large amounts of data and can be deployed to identify complex patterns and relationships that would be difficult for traditional machine learning algorithms to detect. Therefore, motivated by the increasing user requirements and advancements in sensor technologies, as well as the emergence of machine learning and deep learning, this thesis investigates the potential of data-driven approaches to deliver dependable and scalable solutions for indoor positioning systems. This thesis first analyzes and explores the features, challenges, and applications of two popular indoor environmental signals: magnetic field and WiFi. In numerous indoor positioning systems, the magnetometer in smartphones has played a crucial role in providing location information, including orientation, user trajectory construction, and magnetic field-based fingerprinting. However, the magnetometer measurements face challenges due to magnetic disturbance caused by metal infrastructures, electrical equipment, and other electronic devices in complex indoor environments. Consequently, this thesis presents a novel data-driven solution for detecting magnetic disturbance using unsupervised learning. The research focuses on extracting and analyzing statistical features from smartphone magnetometer measurements. Based on extensive experiments and analysis of the covariance and magnitude difference, two unsupervised learning-based methods are developed and evaluated in static and dynamic scenarios to demonstrate their reliability and robustness. Results consistently indicate that the proposed approach outperforms conventional methods across all experimental conditions. WiFi is also widely used for indoor positioning services on smartphones, but its accuracy is often affected by ranging errors caused by non-line-of-sight (NLOS) conditions. To address this issue, this thesis introduces a novel data-driven approach for real-time NLOS/LOS identification using WiFi Received Signal Strength (RSS) and WiFi Round-trip Time (RTT). Through extensive analysis of the dispersion characteristics of WiFi RSS and WiFi RTT, three machine learning algorithms are selected and developed to differentiate samples corresponding to NLOS/LOS conditions. The experiments are conducted using data collected from commercial smartphones and WiFi access points in actual experimental sites, without any prior infrastructure setup or reconfiguration. The proposed methods exhibit the highest identification accuracy while maintaining the lowest latency compared to contemporary solutions. Next, the thesis studies the commonly employed WiFi fingerprinting methods for indoor positioning systems. To solve one key issue of lacking pre-collected WiFi fingerprints and to reduce the burden of heavy human labor, a scalable WiFi fingerprint augmentation method is proposed. This method utilizes a multivariate Gaussian process regression (MGPR) model to estimate the collective distribution of all WiFi signals. It then predicts the signals at unsurveyed potential reference points computed by two new schemes, thereby generating additional fingerprints. The effectiveness of the proposed solution is evaluated using an open-source dataset obtained from a multi-floor building. The results demonstrate that the proposed solution significantly enhances positioning accuracy while maintaining lower computational complexity than conventional augmentation methods. Finally, the thesis explores data fusion for indoor positioning systems that utilize various modalities. A new data-driven method called Multimodal Graph Fingerprinting is proposed. The method constructs a multimodal graph at the location of the user’s smart terminal by integrating radio frequency signals, electromagnetic field (EMF) strength, and inertial sensor measurements. A hierarchical deep graph neural network is developed to learn the correlations between the multimodal graphs and their respective locations by capturing the features of the identities and topology information. The proposed method is evaluated using a real dataset collected from the university campus. The results show that by integrating various modalities, the proposed model achieves a median positioning error of 2.1m

Error Investigation on Wi-Fi RTT in Commercial Consumer Devices

Researchers have explored multiple Wi-Fi features to estimate user locations in indoor environments in the past decade, such as Received Signal Strength Indication (RSSI), Channel State Information (CSI), Time of Arrival (TOA), and Angle of Arrive (AoA). Fine Time Measurement (FTM) is a protocol standardized by IEEE 802.11-2016, which can estimate the distance between the initiator and the station using Wi-Fi Round-Trip Time (RTT). Promoted by Google, such a protocol has been explored in many mobile localization algorithms, which can provide meter-level positioning accuracy between Wi-Fi RTT-enabled smartphones and access points (APs). However, previous studies have shown that the Wi-Fi RTT measurements are sensitive to environmental changes, which leads to significant errors in the localization algorithms. Such an error usually varies according to different environments and settings. Therefore, this paper investigates the error in Wi-Fi RTT distance measurements by setting multiple experiments with different hardware, motion status, and signal path loss conditions. The experiment results show that four categories of errors are found in RTT distance measurements, including hardware-dependent bias, blocker-dependent bias, fluctuations, and outliers. Comparison and analysis are carried out to illustrate the impact of the different errors on Wi-Fi RTT distance

Description of three new species of Callyntrura (Japonphysa) (Collembola, Entomobryidae) from China with the aid of DNA barcoding

Callyntrura(s.l.) Börner, 1906 is the largest genus of the subfamily Salininae and contains 11 subgenera and 98 species from all over the world (mainly Asia), with eight species recorded from China. In the present paper, three new species of Callyntrura(s.l.) are described from China: C. (Japonphysa) xinjianensis sp. nov.; C. (J.) tongguensis sp. nov. and C. (J.) raoi sp. nov. Their differences in colour pattern, chaetotaxy and other characters are slight, however distances of COI mtDNA support their validation as three new distinct species. A key to the Chinese Callyntrura(s.l.) is provided

Error Investigation on Wi-Fi RTT in Commercial Consumer Devices

Researchers have explored multiple Wi-Fi features to estimate user locations in indoor environments in the past decade, such as Received Signal Strength Indication (RSSI), Channel State Information (CSI), Time of Arrival (TOA), and Angle of Arrive (AoA). Fine Time Measurement (FTM) is a protocol standardized by IEEE 802.11-2016, which can estimate the distance between the initiator and the station using Wi-Fi Round-Trip Time (RTT). Promoted by Google, such a protocol has been explored in many mobile localization algorithms, which can provide meter-level positioning accuracy between Wi-Fi RTT-enabled smartphones and access points (APs). However, previous studies have shown that the Wi-Fi RTT measurements are sensitive to environmental changes, which leads to significant errors in the localization algorithms. Such an error usually varies according to different environments and settings. Therefore, this paper investigates the error in Wi-Fi RTT distance measurements by setting multiple experiments with different hardware, motion status, and signal path loss conditions. The experiment results show that four categories of errors are found in RTT distance measurements, including hardware-dependent bias, blocker-dependent bias, fluctuations, and outliers. Comparison and analysis are carried out to illustrate the impact of the different errors on Wi-Fi RTT distance

Screening of Concentration and Antimicrobial Effectiveness of Antimicrobial Preservative in Betastatin Besylate Nasal Spray

Objective. To explore the optimal concentration and antimicrobial effectiveness of antimicrobial preservative in betastatin besylate nasal spray. Methods. By using Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Candida albicans, and Aspergillus niger as test strains, the antimicrobial effectiveness of betastatin besylate nasal spray containing different concentrations of antimicrobial preservative (0.02%, 0.0125%, and 0.005% benzalkonium chloride, respectively) was determined by using bacteriostatic effect test (Chinese Pharmacopoeia, 2015 edition). Results. The antimicrobial effectiveness of betastatin besylate nasal spray containing 0.02% and 0.0125% benzalkonium chloride, respectively, complied with the regulations of Chinese Pharmacopoeia (2015 Edition) against five test strains. However, the antimicrobial effectiveness of betastatin besylate nasal spray containing 0.005% benzalkonium chloride against P. aeruginosa did not meet the requirements of Chinese Pharmacopoeia. Conclusion. Benzalkonium chloride at a concentration of 0.125% can be used as an added antimicrobial preservative in betastatin besylate nasal spray

Crowdsourced Indoor Positioning with Scalable WiFi Augmentation

In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, crowdsourced data is usually sensitive to crowd density. The positioning accuracy degrades in some areas due to a lack of FPs or visitors. To improve the positioning performance, this paper proposes a scalable WiFi FP augmentation method with two major modules: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM). A globally self-adaptive (GS) and a locally self-adaptive (LS) approach are proposed in VRPG to determine the potential unsurveyed RPs. A multivariate Gaussian process regression (MGPR) model is designed to estimate the joint distribution of all WiFi signals and predicts the signals on unsurveyed RPs to generate more FPs. Evaluations are conducted on an open-source crowdsourced WiFi FP dataset based on a multi-floor building. The results show that combining GS and MGPR can improve the positioning accuracy by 5% to 20% from the benchmark, but with halved computation complexity compared to the conventional augmentation approach. Moreover, combining LS and MGPR can sharply reduce 90% of the computation complexity against the conventional approach while still providing moderate improvement in positioning accuracy from the benchmark

Phylomitogenomic analyses on collembolan higher taxa with enhanced taxon sampling and discussion on method selection

Collembola are a basal group of Hexapoda renowned for both unique morphological characters and significant ecological roles. However, a robust and plausible phylogenetic relationship between its deeply divergent lineages has yet to be achieved. We carried out a mitophylogenomic study based on a so far the most comprehensive mitochondrial genome dataset. Our data matrix contained mitogenomes of 31 species from almost all major families of all four orders, with 16 mitogenomes newly sequenced and annotated. We compared the linear arrangements of genes along mitochondria across species. Then we conducted 13 analyses each under a different combination of character coding, partitioning scheme and heterotachy models, and assessed their performance in phylogenetic inference. Several hypothetical tree topologies were also tested. Mitogenomic structure comparison revealed that most species share the same gene order of putative ancestral pancrustacean pattern, while seven species from Onychiuridae, Poduridae and Symphypleona bear different levels of gene rearrangements, indicating phylogenetic signals. Tomoceroidea was robustly recovered for the first time in the presence of all its families and subfamilies. Monophyly of Onychiuroidea was supported using unpartitioned models alleviating LBA. Paronellidae was revealed polyphyletic with two subfamilies inserted independently into Entomobryidae. Although Entomobryomorpha has not been well supported, more than half of the analyses obtained convincing topologies by placing Tomoceroidea within or near remaining Entomobryomorpha. The relationship between elongate-shaped and spherical-shaped collembolans still remained ambiguous, but Neelipleona tend to occupy the basal position in most trees. This study showed that mitochondrial genomes could provide important information for reconstructing the relationships among Collembola when suitable analytical approaches are implemented. Of all the data refining and model selecting schemes used in this study, the combination of nucleotide sequences, partitioning model and exclusion of third codon positions performed better in generating more reliable tree topology and higher node supports than others.This work was supported by National Natural Science Foundation of China [grant numbers 41571052, 41971063, 41430857, 41811530086, 31861133006]; Alexander von Humboldt Foundation; and Youth Innovation Promotion Association, CAS