The aging population is growing rapidly, and so is the danger of falls in older adults. A major cause of injury is falling, and detection in time can greatly save medical expenses and recovery time. However, to provide timely intervention and avoid unnecessary alarms, detection systems must be effective and reliable while addressing privacy concerns regarding the user. In this work, we propose a framework for detecting falls using several complementary systems: a semi-supervised federated learning-based fall detection system (SF2D), an indoor localization and navigation system, and a vision-based human fall recognition system. A wearable device and an edge device identify a fall scenario in the first system. On top of that, the second system uses an indoor localization technique first to localize the fall location and then navigate a robot to inspect the scenario. A vision-based detection system running on an edge device with a mounted camera on a robot is used to recognize fallen people. Each of the systems of this proposed framework achieves different accuracy rates. Specifically, the SF2D has a 0.81% failure rate equivalent to 99.19% accuracy, while the vision-based fallen people detection achieves 96.3% accuracy. However, when we combine the accuracy of these two systems with the accuracy of the navigation system (95% success rate), our proposed framework creates a highly reliable performance for fall detection, with an overall accuracy of 99.99%. Not only is the proposed framework safe for older adults, but it is also a privacy-preserving solution for detecting falls.
Location information serves as the fundamental element for numerous Internet of Things (IoT) applications. Traditional indoor localization techniques often produce significant errors and raise privacy concerns due to centralized data collection. In response, Machine Learning (ML) techniques offer promising solutions by capturing indoor environment variations. However, they typically require central data aggregation, leading to privacy, bandwidth, and server reliability issues. To overcome these challenges, in this paper, we propose a Federated Learning (FL)-based approach for dynamic indoor localization using a Deep Neural Network (DNN) model. Experimental results show that FL has the nearby performance to Centralized Model (CL) while keeping the data privacy, bandwidth efficiency and server reliability. This research demonstrates that our proposed FL approach provides a viable solution for privacy-enhanced indoor localization, paving the way for advancements in secure and efficient indoor localization systems.
Device-free Wi-Fi sensing has numerous benefits in practical settings, as it eliminates the requirement for dedicated sensing devices and can be accomplished using current low-cost Wi-Fi devices. With the development of Wi-Fi standards, millimeter wave Wi-Fi devices with 60GHz operating frequency and up to 4GHz bandwidth have become commercially available. Although millimeter wave Wi-Fi presents great promise for Device-Free Wi-Fi sensing with increased bandwidth and beam-forming ability, there still lacks a method for localization using millimeter wave Wi-Fi. Here, we present two major contributions: First, we provide a comprehensive multi-sensor dataset that synchronously captures human movement data from millimeter wave Wi-Fi, 2.4GHz Wi-Fi, and millimeter wave radar sensors. This dataset enables direct performance comparisons across different sensing modalities and facilitates reproducible researches in indoor localization. Second, we introduce MMWiLoc, a novel localization method that achieves centimeter-level precision with low computational cost. MMWiLoc incorporates two components: beam pattern calibration using Expectation Maximization and target localization through Multi-Scale Compression Sensing. The system processes beam Signal-to-Noise Ratio (beamSNR) information from the beam-forming process to determine target Angle of Arrival (AoA), which is then fused across devices for localization. Our extensive evaluation demonstrates that MMWiLoc achieves centimeter-level precision, outperforming 2.4GHz Wi-Fi systems while maintaining competitive performance with high-precision radar systems. The dataset and examples processing code will be released after this paper is accepted at https://github.com/wowoyoho/MMWiLoc.
State estimation or filtering serves as a fundamental task to enable intelligent decision-making in applications such as autonomous vehicles, robotics, healthcare monitoring, smart grids, intelligent transportation, and predictive maintenance. Standard filtering assumes prior knowledge of noise statistics to extract latent system states from noisy sensor data. However, real-world scenarios involve abnormalities like outliers, biases, drifts, and missing observations with unknown or partially known statistics, limiting conventional approaches. This thesis presents novel robust nonlinear filtering methods to mitigate these challenges. Based on insights from our filtering proposals, we extend the formulations to offline estimation/learning setups and propose smoothing extensions. Our methods leverage Bayesian inference frameworks, employing both deterministic and stochastic approximation techniques including Variational Inference (VI) and Particle Filters/Sequential Monte Carlo (SMC). We also study theoretical estimation limits using Bayesian Cram\'er-Rao bounds (BCRBs) in the context of measurement abnormalities. To validate the performance gains of the proposed methods, we perform simulations and experiments in scenarios including target tracking, indoor localization, 3D point cloud registration, mesh registration, and pose graph optimization. The fundamental nature of the work makes it useful in diverse applications, with possible future extensions toward developing outlier-robust machine learning pipelines, learning system dynamics from anomalous data, and addressing challenges in generative AI where standard diffusion models struggle with outliers, imbalanced datasets, and mode collapse.
Accurate indoor positioning for unmanned aerial vehicles (UAVs) is critical for logistics, surveillance, and emergency response applications, particularly in GPS-denied environments. Existing indoor localization methods, including optical tracking, ultra-wideband, and Bluetooth-based systems, face cost, accuracy, and robustness trade-offs, limiting their practicality for UAV navigation. This paper proposes CiUAV, a novel 3D indoor localization system designed for UAVs, leveraging channel state information (CSI) obtained from low-cost ESP32 IoT-based sensors. The system incorporates a dynamic automatic gain control (AGC) compensation algorithm to mitigate noise and stabilize CSI signals, significantly enhancing the robustness of the measurement. Additionally, a multi-task 3D localization model, Sensor-in-Sample (SiS), is introduced to enhance system robustness by addressing challenges related to incomplete sensor data and limited training samples. SiS achieves this by joint training with varying sensor configurations and sample sizes, ensuring reliable performance even in resource-constrained scenarios. Experiment results demonstrate that CiUAV achieves a LMSE localization error of 0.2629 m in a 3D space, achieving good accuracy and robustness. The proposed system provides a cost-effective and scalable solution, demonstrating its usefulness for UAV applications in resource-constrained indoor environments.
Received signal strength (RSS)-based optical wireless positioning (OWP) systems are becoming popular for indoor localization because they are low-cost and accurate. However, few open-source datasets are available to test and analyze RSS-based OWP systems. In this paper, we collected RSS values at a sampling frequency of 27 Hz, inertial measurement unit (IMU) at a sampling frequency of 200 Hz and the ground truth at a sampling frequency of 160 Hz in two indoor environments. One environment has no obstacles, and the other has a metal column as an obstacle to represent a non-line-of-sight (NLOS) scenario. We recorded data with a vehicle at three different speeds (low, medium and high). The dataset includes over 110 k data points and covers more than 80 min. We also provide benchmark tests to show localization performance using only RSS-based OWP and improve accuracy by combining IMU data via extended kalman filter. The dataset OWP-IMU is open source1 to support further research on indoor localization methods.
In the era of Industry 4.0, precise indoor localization is vital for automation and efficiency in smart factories. Reconfigurable Intelligent Surfaces (RIS) are emerging as key enablers in 6G networks for joint sensing and communication. However, RIS faces significant challenges in Non-Line-of-Sight (NLOS) and multipath propagation, particularly in localization scenarios, where detecting NLOS conditions is crucial for ensuring not only reliable results and increased connectivity but also the safety of smart factory personnel. This study introduces an AI-assisted framework employing a Convolutional Neural Network (CNN) customized for accurate Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) classification to enhance RIS-based localization using measured, synthetic, mixed-measured, and mixed-synthetic experimental data, that is, original, augmented, slightly noisy, and highly noisy data, respectively. Validated through such data from three different environments, the proposed customized-CNN (cCNN) model achieves {95.0\%-99.0\%} accuracy, outperforming standard pre-trained models like Visual Geometry Group 16 (VGG-16) with an accuracy of {85.5\%-88.0\%}. By addressing RIS limitations in NLOS scenarios, this framework offers scalable and high-precision localization solutions for 6G-enabled smart factories.
Wireless Fidelity (WiFi) based indoor positioning is a widely researched area for determining the position of devices within a wireless network. Accurate indoor location has numerous applications, such as asset tracking and indoor navigation. Despite advances in WiFi localization techniques -- in particular approaches that leverage WiFi telemetry -- their adoption in practice remains limited due to several factors including environmental changes that cause signal fading, multipath effects, interference, which, in turn, impact positioning accuracy. In addition, telemetry data differs depending on the WiFi device vendor, offering distinct features and formats; use case requirements can also vary widely. Currently, there is no unified model to handle all these variations effectively. In this paper, we present WiFiGPT, a Generative Pretrained Transformer (GPT) based system that is able to handle these variations while achieving high localization accuracy. Our experiments with WiFiGPT demonstrate that GPTs, in particular Large Language Models (LLMs), can effectively capture subtle spatial patterns in noisy wireless telemetry, making them reliable regressors. Compared to existing state-of-the-art methods, our method matches and often surpasses conventional approaches for multiple types of telemetry. Achieving sub-meter accuracy for RSSI and FTM and centimeter-level precision for CSI demonstrates the potential of LLM-based localisation to outperform specialized techniques, all without handcrafted signal processing or calibration.
Indoor localization is critical for IoT applications, yet challenges such as non-Gaussian noise, environmental interference, and measurement outliers hinder the robustness of traditional methods. Existing approaches, including Kalman filtering and its variants, often rely on Gaussian assumptions or static thresholds, limiting adaptability in dynamic environments. This paper proposes a hierarchical robust framework integrating Variational Bayesian (VB) parameter learning, Huber M-estimation, and Conformal Outlier Detection (COD) to address these limitations. First, VB inference jointly estimates state and noise parameters, adapting to time-varying uncertainties. Second, Huber-based robust filtering suppresses mild outliers while preserving Gaussian efficiency. Third, COD provides statistical guarantees for outlier detection via dynamically calibrated thresholds, ensuring a user-controlled false alarm rate. Theoretically, we prove the Semi-positive Definiteness of Huber-based Kalman filtering covariance and the coverage of sliding window conformal prediction. Experiments on geomagnetic fingerprint datasets demonstrate significant improvements: fingerprint matching accuracy increases from 81.25% to 93.75%, and positioning errors decrease from 0.62-6.87 m to 0.03-0.35 m. Comparative studies further validate the framework's robustness, showing consistent performance gains under non-Gaussian noise and outlier conditions.
LoRaWAN technology's extensive coverage positions it as a strong contender for large-scale IoT deployments. However, achieving sub-10 m accuracy in indoor localization remains challenging due to complex environmental conditions, multipath fading, and transient obstructions. This paper proposes a lightweight but robust approach combining adaptive filtering with an extended log-distance, multi-wall path loss and shadowing (PLS) model. Our methodology augments conventional models with critical LoRaWAN parameters (received signal strength indicator (RSSI), frequency, and signal-to-noise ratio (SNR)) and dynamic environmental indicators (temperature, humidity, carbon dioxide, particulate matter, and barometric pressure). An adaptive Kalman filter reduces RSSI fluctuations, isolating persistent trends from momentary noise. Using a six-month dataset of 1,328,334 field measurements, we evaluate three models: the baseline COST 231 multi-wall model (MWM), the baseline model augmented with environmental parameters (MWM-EP), and a forward-only adaptive Kalman-filtered RSSI version of the latter (MWM-EP-KF). Results confirm that the MWM-EP-KF achieves a mean absolute error (MAE) of 5.81 m, outperforming both the MWM-EP (10.56 m) and the baseline MWM framework (17.98 m). Environmental augmentation reduces systematic errors by 41.22%, while Kalman filtering significantly enhances robustness under high RSSI volatility by 42.63%, on average across all devices. These findings present an interpretable, efficient solution for precise indoor LoRaWAN localization in dynamically changing environments.
In recent years, Channel State Information (CSI), recognized for its fine-grained spatial characteristics, has attracted increasing attention in WiFi-based indoor localization. However, despite its potential, CSI-based approaches have yet to achieve the same level of deployment scale and commercialization as those based on Received Signal Strength Indicator (RSSI). A key limitation lies in the fact that most existing CSI-based systems are developed and evaluated in controlled, small-scale environments, limiting their generalizability. To bridge this gap, we explore the deployment of a large-scale CSI-based localization system involving over 400 Access Points (APs) in a real-world building under the Integrated Sensing and Communication (ISAC) paradigm. We highlight two critical yet often overlooked factors: the underutilization of unlabeled data and the inherent heterogeneity of CSI measurements. To address these challenges, we propose a novel CSI-based learning framework for WiFi localization, tailored for large-scale ISAC deployments on the server side. Specifically, we employ a novel graph-based structure to model heterogeneous CSI data and reduce redundancy. We further design a pretext pretraining task that incorporates spatial and temporal priors to effectively leverage large-scale unlabeled CSI data. Complementarily, we introduce a confidence-aware fine-tuning strategy to enhance the robustness of localization results. In a leave-one-smartphone-out experiment spanning five floors and 25, 600 m2, we achieve a median localization error of 2.17 meters and a floor accuracy of 99.49%. This performance corresponds to an 18.7% reduction in mean absolute error (MAE) compared to the best-performing baseline.
Modeling path loss in indoor LoRaWAN technology deployments is inherently challenging due to structural obstructions, occupant density and activities, and fluctuating environmental conditions. This study proposes a two-stage approach to capture and analyze these complexities using an extensive dataset of 1,328,334 field measurements collected over six months in a single-floor office at the University of Siegen's Hoelderlinstrasse Campus, Germany. First, we implement a multiple linear regression model that includes traditional propagation metrics (distance, structural walls) and an extension with proposed environmental variables (relative humidity, temperature, carbon dioxide, particulate matter, and barometric pressure). Using analysis of variance, we demonstrate that adding these environmental factors can reduce unexplained variance by 42.32 percent. Secondly, we examine residual distributions by fitting five candidate probability distributions: Normal, Skew-Normal, Cauchy, Student's t, and Gaussian Mixture Models (GMMs) with 2 to 5 components. Our results show that a four-component Gaussian Mixture Model captures the residual heterogeneity of indoor signal propagation most accurately, significantly outperforming single-distribution approaches. Given the push toward ultra-reliable, context-aware communications in 6G networks, our analysis shows that environment-aware modeling can substantially improve LoRaWAN network design in dynamic indoor IoT deployments.
The standard approach for visual place recognition is to use global image descriptors to retrieve the most similar database images for a given query image. The results can then be further improved with re-ranking methods that re-order the top scoring images. However, existing methods focus on re-ranking based on the same image descriptors that were used for the initial retrieval, which we argue provides limited additional signal. In this work we propose Generalized Contextual Similarity Aggregation (GCSA), which is a graph neural network-based re-ranking method that, in addition to the visual descriptors, can leverage other types of available side information. This can for example be other sensor data (such as signal strength of nearby WiFi or BlueTooth endpoints) or geometric properties such as camera poses for database images. In many applications this information is already present or can be acquired with low effort. Our architecture leverages the concept of affinity vectors to allow for a shared encoding of the heterogeneous multi-modal input. Two large-scale datasets, covering both outdoor and indoor localization scenarios, are utilized for training and evaluation. In experiments we show significant improvement not only on image retrieval metrics, but also for the downstream visual localization task.
Fingerprint-based indoor localization is often labor-intensive due to the need for dense grids and repeated measurements across time and space. Maintaining high localization accuracy with extremely sparse fingerprints remains a persistent challenge. Existing benchmark methods primarily rely on the measured fingerprints, while neglecting valuable spatial and environmental characteristics. In this paper, we propose a systematic integration of an Attentional Graph Neural Network (AGNN) model, capable of learning spatial adjacency relationships and aggregating information from neighboring fingerprints, and a meta-learning framework that utilizes datasets with similar environmental characteristics to enhance model training. To minimize the labor required for fingerprint collection, we introduce two novel data augmentation strategies: 1) unlabeled fingerprint augmentation using moving platforms, which enables the semi-supervised AGNN model to incorporate information from unlabeled fingerprints, and 2) synthetic labeled fingerprint augmentation through environmental digital twins, which enhances the meta-learning framework through a practical distribution alignment, which can minimize the feature discrepancy between synthetic and real-world fingerprints effectively. By integrating these novel modules, we propose the Attentional Graph Meta-Learning (AGML) model. This novel model combines the strengths of the AGNN model and the meta-learning framework to address the challenges posed by extremely sparse fingerprints. To validate our approach, we collected multiple datasets from both consumer-grade WiFi devices and professional equipment across diverse environments. Extensive experiments conducted on both synthetic and real-world datasets demonstrate that the AGML model-based localization method consistently outperforms all baseline methods using sparse fingerprints across all evaluated metrics.
Fingerprinting-based indoor localization methods typically require labor-intensive site surveys to collect signal measurements at known reference locations and frequent recalibration, which limits their scalability. This paper addresses these challenges by presenting a novel approach for indoor localization that utilizes crowdsourced data without location labels. We leverage the statistical information of crowdsourced data and propose a cumulative distribution function (CDF) based distance estimation method that maps received signal strength (RSS) to distances from access points. This approach overcomes the limitations of conventional distance estimation based on the empirical path loss model by efficiently capturing the impacts of shadow fading and multipath. Compared to fingerprinting, our unsupervised statistical approach eliminates the need for signal measurements at known reference locations. The estimated distances are then integrated into a three-step framework to determine the target location. The localization performance of our proposed method is evaluated using RSS data generated from ray-tracing simulations. Our results demonstrate significant improvements in localization accuracy compared to methods based on the empirical path loss model. Furthermore, our statistical approach, which relies on unlabeled data, achieves localization accuracy comparable to that of the supervised approach, the $k$-Nearest Neighbor ($k$NN) algorithm, which requires fingerprints with location labels. For reproducibility and future research, we make the ray-tracing dataset publicly available at [2].
Various deep learning models have been developed for indoor localization based on radio-frequency identification (RFID) tags. However, they often require adaptation to ensure accurate tracking in new target operational domains. To address this challenge, unsupervised domain adaptation (UDA) methods have been proposed to align pre-trained models with data from target environments. However, they rely on large annotated datasets from the initial domain (source). Source data access is limited by privacy, storage, computational, and transfer constraints. Although many source-free domain adaptation (SFDA) methods address these constraints in classification, applying them to regression models for localization remains challenging. Indeed, target datasets for indoor localization are typically small, with few features and samples, and are noisy. Adapting regression models requires high-confidence target pseudo-annotation to avoid over-training. In this paper, a specialized mean-teacher method called MTLoc is proposed for SFDA. MTLoc updates the student network using noisy data and teacher-generated pseudo-labels. The teacher network maintains stability through exponential moving averages. To further ensure robustness, the teacher's pseudo-labels are refined using k-nearest neighbor correction. MTLoc allows for self-supervised learning on target data, facilitating effective adaptation to dynamic and noisy indoor environments. Validated using real-world data from our experimental setup with INLAN Inc., our results show that MTLoc achieves high localization accuracy under challenging conditions, significantly reducing localization error compared to baselines, including the state-of-the-art adversarial UDA approach with access to source data.
Wi-Fi sensing has emerged as a transformative technology that leverages ubiquitous wireless signals to enable a variety of applications ranging from activity and gesture recognition to indoor localization and health monitoring. However, the inherent dependency of Wi-Fi signals on environmental conditions introduces significant generalization challenges,variations in surroundings, human positions, and orientations often lead to inconsistent signal features, impeding robust action recognition. In this survey, we review over 200 studies on Wi-Fi sensing generalization, categorizing them along the entire sensing pipeline: device deployment, signal processing, feature learning, and model deployment. We systematically analyze state-of-the-art techniques, which are employed to mitigate the adverse effects of environmental variability. Moreover, we provide a comprehensive overview of open-source datasets such as Widar3.0, XRF55, and XRFv2, highlighting their unique characteristics and applicability for multimodal fusion and cross-modal tasks. Finally, we discuss emerging research directions, such as multimodal approaches and the integration of large language models,to inspire future advancements in this rapidly evolving field. Our survey aims to serve as a valuable resource for researchers, offering insights into current methodologies, available datasets, and promising avenues for further investigation.
Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulation in the inertial navigation process limits the feasible exploratory phases of these systems. Advances in magnetometer array processing have demonstrated that odometry information, i.e., displacement and rotation information, can be extracted from local magnetic field variations and used to create magnetic-field odometry-aided inertial navigation systems. The error growth rate of these systems is significantly lower than that of standalone inertial navigation systems. This study seeks an answer to whether a magnetic-field SLAM system fed with measurements from a magnetometer array can indirectly extract odometry information -- without requiring algorithmic modifications -- and thus sustain longer exploratory phases. The theoretical analysis and simulation results show that such a system can extract odometry information and indirectly create a magnetic field odometry-aided inertial navigation system during the exploration phases. However, practical challenges related to map resolution and computational complexity remain significant.
Indoor positioning systems (IPSs) have gained attention as outdoor navigation becomes prevalent in everyday life. Research is being actively conducted on how indoor smartphone navigation can be accomplished and improved using received signal strength indication (RSSI) and machine learning (ML). IPSs have more use cases that need further exploration, and we aim to explore using IPSs for the indoor navigation of an autonomous robot. We collected a dataset and trained models to test on a robot. We also developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions. After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time. The findings of this paper indicate that using IPSs for autonomous robots is a promising area of future research.
As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes.