Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (235)

Search Parameters:
Keywords = Wi-Fi indoor localization

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
22 pages, 3429 KiB  
Article
Indoor Positioning and Tracking System in a Multi-Level Residential Building Using WiFi
by Elmer Magsino, Joshua Kenichi Sim, Rica Rizabel Tagabuhin and Jan Jayson Tirados
Information 2025, 16(8), 633; https://doi.org/10.3390/info16080633 - 24 Jul 2025
Viewed by 301
Abstract
The implementation of an Indoor Positioning System (IPS) in a three-storey residential building employing WiFi signals that can also be used to track indoor movements is presented in this study. The movement of inhabitants is monitored through an Android smartphone by detecting the [...] Read more.
The implementation of an Indoor Positioning System (IPS) in a three-storey residential building employing WiFi signals that can also be used to track indoor movements is presented in this study. The movement of inhabitants is monitored through an Android smartphone by detecting the Received Signal Strength Indicator (RSSI) signals from WiFi Anchor Points (APs).Indoor movement is detected through a successive estimation of a target’s multiple positions. Using the K-Nearest Neighbors (KNN) and Particle Swarm Optimization (PSO) algorithms, these RSSI measurements are trained for estimating the position of an indoor target. Additionally, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) has been integrated into the PSO method for removing RSSI-estimated position outliers of the mobile device to further improve indoor position detection and monitoring accuracy. We also employed Time Reversal Resonating Strength (TRRS) as a correlation technique as the third method of localization. Our extensive and rigorous experimentation covers the influence of various weather conditions in indoor detection. Our proposed localization methods have maximum accuracies of 92%, 80%, and 75% for TRRS, KNN, and PSO + DBSCAN, respectively. Each method also has an approximate one-meter deviation, which is a short distance from our targets. Full article
Show Figures

Graphical abstract

19 pages, 684 KiB  
Article
A Wi-Fi Fingerprinting Indoor Localization Framework Using Feature-Level Augmentation via Variational Graph Auto-Encoder
by Dongdeok Kim, Jae-Hyeon Park and Young-Joo Suh
Electronics 2025, 14(14), 2807; https://doi.org/10.3390/electronics14142807 - 12 Jul 2025
Viewed by 339
Abstract
Wi-Fi fingerprinting is a widely adopted technique for indoor localization in location-based services (LBS) due to its cost-effectiveness and ease of deployment using existing infrastructure. However, the performance of these systems often suffers due to missing received signal strength indicator (RSSI) measurements, which [...] Read more.
Wi-Fi fingerprinting is a widely adopted technique for indoor localization in location-based services (LBS) due to its cost-effectiveness and ease of deployment using existing infrastructure. However, the performance of these systems often suffers due to missing received signal strength indicator (RSSI) measurements, which can arise from complex indoor structures, device limitations, or user mobility, leading to incomplete and unreliable fingerprint data. To address this critical issue, we propose Feature-level Augmentation for Localization (FALoc), a novel framework that enhances Wi-Fi fingerprinting-based localization through targeted feature-level data augmentation. FALoc uniquely models the observation probabilities of RSSI signals by constructing a bipartite graph between reference points and access points, which is then processed by a variational graph auto-encoder (VGAE). Based on these learned probabilities, FALoc intelligently imputes likely missing RSSI values or removes unreliable ones, effectively enriching the training data. We evaluated FALoc using an MLP (Multi-Layer Perceptron)-based localization model on the UJIIndoorLoc and UTSIndoorLoc datasets. The experimental results demonstrate that FALoc significantly improves localization accuracy, achieving mean localization errors of 7.137 m on UJIIndoorLoc and 7.138 m on UTSIndoorLoc, which represent improvements of approximately 12.9% and 8.6% over the respective MLP baselines (8.191 m and 7.808 m), highlighting the efficacy of our approach in handling missing data. Full article
(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)
Show Figures

Figure 1

32 pages, 2945 KiB  
Article
SelfLoc: Robust Self-Supervised Indoor Localization with IEEE 802.11az Wi-Fi for Smart Environments
by Hamada Rizk and Ahmed Elmogy
Electronics 2025, 14(13), 2675; https://doi.org/10.3390/electronics14132675 - 2 Jul 2025
Viewed by 518
Abstract
Accurate and scalable indoor localization is a key enabler of intelligent automation in smart environments and industrial systems. In this paper, we present SelfLoc, a self-supervised indoor localization system that combines IEEE 802.11az Round Trip Time (RTT) and Received Signal Strength Indicator [...] Read more.
Accurate and scalable indoor localization is a key enabler of intelligent automation in smart environments and industrial systems. In this paper, we present SelfLoc, a self-supervised indoor localization system that combines IEEE 802.11az Round Trip Time (RTT) and Received Signal Strength Indicator (RSSI) data to achieve fine-grained positioning using commodity Wi-Fi infrastructure. Unlike conventional methods that depend heavily on labeled data, SelfLoc adopts a contrastive learning framework to extract spatially discriminative and temporally consistent representations from unlabeled wireless measurements. The system integrates a dual-contrastive strategy: temporal contrasting captures sequential signal dynamics essential for tracking mobile agents, while contextual contrasting promotes spatial separability by ensuring that signal representations from distinct locations remain well-differentiated, even under similar signal conditions or environmental symmetry. To this end, we design signal-specific augmentation techniques for the physical properties of RTT and RSSI, enabling the model to generalize across environments. SelfLoc also adapts effectively to new deployment scenarios with minimal labeled data, making it suitable for dynamic and collaborative industrial applications. We validate the effectiveness of SelfLoc through experiments conducted in two realistic indoor testbeds using commercial Android devices and seven Wi-Fi access points. The results demonstrate that SelfLoc achieves high localization precision, with a median error of only 0.55 m, and surpasses state-of-the-art baselines by at least 63.3% with limited supervision. These findings affirm the potential of SelfLoc to support spatial intelligence and collaborative automation, aligning with the goals of Industry 4.0 and Society 5.0, where seamless human–machine interactions and intelligent infrastructure are key enablers of next-generation smart environments. Full article
(This article belongs to the Special Issue Collaborative Intelligent Automation System for Smart Industry)
Show Figures

Figure 1

23 pages, 678 KiB  
Article
Unified Probabilistic and Similarity-Based Position Estimation from Radio Observations
by Max Werner, Markus Bullmann, Toni Fetzer and Frank Deinzer
Sensors 2025, 25(13), 4092; https://doi.org/10.3390/s25134092 - 30 Jun 2025
Viewed by 260
Abstract
We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just [...] Read more.
We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations. Full article
Show Figures

Figure 1

21 pages, 1204 KiB  
Article
Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems
by Maria Camila Molina, Iness Ahriz, Lounis Zerioul and Michel Terré
Sensors 2025, 25(13), 4095; https://doi.org/10.3390/s25134095 - 30 Jun 2025
Viewed by 389
Abstract
In contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation into a single framework. We present [...] Read more.
In contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation into a single framework. We present a multi-task neural network architecture capable of simultaneously estimating channels from multiple base stations in a blind manner while estimating user terminal coordinates in given indoor environments. This approach exploits the relationship between channel characteristics and spatial information, using the same channel state information (CSI) data to perform both tasks with a single model. We evaluate the proposed solution, assessing its effectiveness across differing antenna spacing configurations and indoor test environments using both WiFi and 5G orthogonal frequency-division multiplexing (OFDM) systems. The results show performance benefits, achieving comparable channel estimation results to other studies while simultaneously providing a localization estimate, resulting in reduced model overhead while leveraging spatial context. The presented system demonstrates potential to improve the efficiency of communication systems in real-world applications, aligning with the goals of emerging integrated sensing and communication (ISAC) systems. Results based on experimental data using the proposed solution show a 50th percentile localization error of 1.62 m for 3-tap channels and 0.89 m for 10-tap channels. Full article
Show Figures

Figure 1

19 pages, 6328 KiB  
Article
Seamless Indoor–Outdoor Localization Through Transition Detection
by Jaehyun Yoo
Electronics 2025, 14(13), 2598; https://doi.org/10.3390/electronics14132598 - 27 Jun 2025
Viewed by 256
Abstract
Indoor localization techniques operate independently of Global Navigation Satellite Systems (GNSSs), which are primarily designed for outdoor environments. However, integrating indoor and outdoor positioning often leads to inconsistent and delayed location estimates, especially at transition zones such as building entrances. This paper develops [...] Read more.
Indoor localization techniques operate independently of Global Navigation Satellite Systems (GNSSs), which are primarily designed for outdoor environments. However, integrating indoor and outdoor positioning often leads to inconsistent and delayed location estimates, especially at transition zones such as building entrances. This paper develops a probabilistic transition detection algorithm to identify indoor, outdoor, and transition zones, aiming to enhance the continuity and accuracy of positioning. The algorithm leverages multi-source sensor data, including WiFi Received Signal Strength Indicator (RSSI), Bluetooth Low-Energy (BLE) RSSI, and GNSS metrics such as carrier-to-noise ratio. During transitions, the system incorporates Inertial Measurement Unit (IMU)-based tracking to ensure smooth switching between positioning engines. The outdoor engine utilizes a Kalman Filter (KF) to fuse IMU and GNSS data, while the indoor engine employs fingerprinting techniques using WiFi and BLE. This paper presents experimental results using three distinct devices across three separate buildings, demonstrating superior performance compared to both Google’s Fused Location Provider (FLP) algorithm and a GPS. Full article
(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)
Show Figures

Figure 1

29 pages, 2186 KiB  
Article
WiPIHT: A WiFi-Based Position-Independent Passive Indoor Human Tracking System
by Xu Xu, Xilong Che, Xianqiu Meng, Long Li, Ziqi Liu and Shuai Shao
Sensors 2025, 25(13), 3936; https://doi.org/10.3390/s25133936 - 24 Jun 2025
Viewed by 434
Abstract
Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and [...] Read more.
Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost. Full article
(This article belongs to the Special Issue Recent Advances in Smart Mobile Sensing Technology)
Show Figures

Figure 1

23 pages, 1297 KiB  
Article
Multi-Granularity and Multi-Modal Feature Fusion for Indoor Positioning
by Lijuan Ye, Yi Wang, Shenglei Pei, Yu Wang, Hong Zhao and Shi Dong
Symmetry 2025, 17(4), 597; https://doi.org/10.3390/sym17040597 - 15 Apr 2025
Viewed by 470
Abstract
Despite the widespread adoption of indoor positioning technology, the existing solutions still face significant challenges. On one hand, Wi-Fi-based positioning struggles to balance accuracy and efficiency in complex indoor environments and architectural layouts formed by pre-existing access points (APs). On the other hand, [...] Read more.
Despite the widespread adoption of indoor positioning technology, the existing solutions still face significant challenges. On one hand, Wi-Fi-based positioning struggles to balance accuracy and efficiency in complex indoor environments and architectural layouts formed by pre-existing access points (APs). On the other hand, vision-based methods, while offering high-precision potential, are hindered by prohibitive costs associated with binocular camera systems required for depth image acquisition, limiting their large-scale deployment. Additionally, channel state information (CSI), containing multi-subcarrier data, maintains amplitude symmetry in ideal free-space conditions but becomes susceptible to periodic positioning errors in real environments due to multipath interference. Meanwhile, image-based positioning often suffers from spatial ambiguity in texture-repeated areas. To address these challenges, we propose a novel hybrid indoor positioning method that integrates multi-granularity and multi-modal features. By fusing CSI data with visual information, the system leverages spatial consistency constraints from images to mitigate CSI error fluctuations while utilizing CSI’s global stability to correct local ambiguities in image-based positioning. In the initial coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, CSI features from Wi-Fi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. The experimental results show that our proposed method synergistically combines the strengths of Wi-Fi fingerprints and visual positioning, resulting in a substantial enhancement in positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 m for 45% of positioning points and 0.8 m for 67% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology. Full article
(This article belongs to the Section Mathematics)
Show Figures

Figure 1

15 pages, 10968 KiB  
Article
An Experimental Evaluation of Indoor Localization in Autonomous Mobile Robots
by Mina Khoshrangbaf, Vahid Khalilpour Akram, Moharram Challenger and Orhan Dagdeviren
Sensors 2025, 25(7), 2209; https://doi.org/10.3390/s25072209 - 31 Mar 2025
Cited by 2 | Viewed by 1057
Abstract
High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex [...] Read more.
High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m2 area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future. Full article
(This article belongs to the Special Issue Multi‐sensors for Indoor Localization and Tracking: 2nd Edition)
Show Figures

Figure 1

21 pages, 3145 KiB  
Review
A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
by Xingyu Liu, Xin Meng, Hancong Duan, Ze Hu and Min Wang
Sensors 2025, 25(6), 1913; https://doi.org/10.3390/s25061913 - 19 Mar 2025
Cited by 1 | Viewed by 2184
Abstract
As a key enabling technology of the Internet of Thing (IoT), WiFi sensing has undergone noteworthy advancements and brought significant improvement to prevailing IoT systems and applications. The past few years have witnessed growing efforts in WiFi sensing, which is widely applied in [...] Read more.
As a key enabling technology of the Internet of Thing (IoT), WiFi sensing has undergone noteworthy advancements and brought significant improvement to prevailing IoT systems and applications. The past few years have witnessed growing efforts in WiFi sensing, which is widely applied in various applications, such as indoor localization, human activity recognition, physiological signal monitoring, and so on. However, these techniques are also maliciously used by attackers to eavesdrop on legitimate users and even tamper the sensing results. Fortunately, these attack techniques in turn promote the advancement of WiFi sensing techniques, especially defense techniques. In this study, we carried out a comprehensive survey to systematically summarize the works related to the topic of attacks and defenses on WiFi sensing technology. Firstly, we summarize the existing surveys in related areas and highlight our unique novelty. Then, we introduce the concept of the core topic of this survey and provide a taxonomy to distinguish different kinds of attack and defense techniques, respectively, that is, active and passive attack techniques as well as active and passive defense techniques. Furthermore, existing works in each category are grouped and introduced in detail, respectively. Full article
(This article belongs to the Section Internet of Things)
Show Figures

Figure 1

40 pages, 2727 KiB  
Review
Indoor Localization Methods for Smartphones with Multi-Source Sensors Fusion: Tasks, Challenges, Strategies, and Perspectives
by Jianhua Liu, Zhijie Yang, Sisi Zlatanova, Songnian Li and Bing Yu
Sensors 2025, 25(6), 1806; https://doi.org/10.3390/s25061806 - 14 Mar 2025
Cited by 3 | Viewed by 5364
Abstract
Positioning information greatly enhances the convenience of people’s lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone [...] Read more.
Positioning information greatly enhances the convenience of people’s lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone indoor positioning and navigation is a crucial technology for enabling indoor location services. Nevertheless, relying solely on a single positioning technique can hardly achieve accurate indoor localization. We reviewed several main methods for indoor positioning using smartphone sensors, including Wi-Fi, Bluetooth, cameras, microphones, inertial sensors, and others. Among these, wireless medium-based positioning methods are prone to interference from signals and obstacles in the indoor environment, while inertial sensors are limited by error accumulation. The fusion of multi-source sensors in complex indoor scenarios benefits from the complementary advantages of various sensors and has become a research hotspot in the field of pervasive indoor localization applications for smartphones. In this paper, we extensively review the current mainstream sensors and indoor positioning methods for smartphone multi-source sensor fusion. We summarize the recent research progress in this domain along with the characteristics of the relevant techniques and applicable scenarios. Finally, we collate and organize the key issues and technological outlooks of this field. Full article
Show Figures

Figure 1

19 pages, 3780 KiB  
Article
Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
by Houjin Lu, Shuzhi Liu and Seung-Hoon Hwang
Electronics 2025, 14(6), 1136; https://doi.org/10.3390/electronics14061136 - 13 Mar 2025
Cited by 1 | Viewed by 1019
Abstract
Indoor positioning systems have become increasingly important due to the limitations of GPS in indoor environments, such as non-line-of-sight conditions and weak signal strength. Among the various indoor positioning techniques, fingerprinting-based approaches utilizing WiFi signals are highly regarded for their accessibility and convenience. [...] Read more.
Indoor positioning systems have become increasingly important due to the limitations of GPS in indoor environments, such as non-line-of-sight conditions and weak signal strength. Among the various indoor positioning techniques, fingerprinting-based approaches utilizing WiFi signals are highly regarded for their accessibility and convenience. However, existing convolutional neural network (CNN) models for fingerprinting often struggle to maintain consistent performance under diverse environmental conditions. To address these challenges, this study proposes a local batch normalization (LBN)-aided CNN model for received signal strength indicator (RSSI)-based indoor positioning. The LBN technique is designed to overcome the limitations of traditional batch normalization (BN) and layer normalization (LN) in managing location-dependent RSSI variations, thereby improving positioning accuracy. The proposed approach consists of two phases: an offline phase, where RSSI data are collected at reference points to train the model, and an online phase, where real-time RSSI data are used to estimate the device’s location. Experimental results demonstrate that the proposed LBN-aided CNN model achieves an accuracy of 92.9%, outperforming existing CNN-based methods. These findings confirm the effectiveness of LBN in enhancing CNN performance for indoor positioning, particularly in challenging environments with significant signal variability. Full article
Show Figures

Graphical abstract

25 pages, 1280 KiB  
Article
Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
by Isil Karabey Aksakalli and Levent Bayındır
Appl. Sci. 2025, 15(4), 1985; https://doi.org/10.3390/app15041985 - 14 Feb 2025
Viewed by 799
Abstract
With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among [...] Read more.
With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among various fingerprint-based systems—such as Bluetooth, ZigBee, or FM radio—Wi-Fi-based indoor positioning stands out as a practical solution, due to the pervasiveness of Wi-Fi infrastructure in public indoor spaces. This study introduces an ESP32-based data-collection tool designed to minimize offline training time for Wi-Fi fingerprinting, and it presents a novel dataset incorporating room-to-room transition time, which represents the time taken to move between rooms, alongside Wi-Fi signal strength data. The proposed approach focuses on room-level localization, leveraging Machine Learning (ML) models to predict the most likely room rather than precise (x, y) coordinates. To assess the effectiveness of this feature, three datasets were collected from different residential environments by three different individuals, enabling a comprehensive evaluation across multiple spatial layouts and movement patterns. The experimental results demonstrate that incorporating room-to-room transition time consistently enhanced localization performance across all the datasets, with accuracy improvements ranging from 1.17% to 12.47%, depending on the model and dataset. Notably, the Wide Neural Network model exhibited the highest improvement, achieving an accuracy increase from 82.37% to 94.77%, while the Ensemble-based methods such as Ensemble Bagged Trees also benefited significantly, reaching up to 93.17% accuracy. Despite varying gains across the datasets, the results confirm that integrating room-to-room transition time improves Wi-Fi-based indoor positioning by leveraging temporal movement patterns to enhance classification. Full article
(This article belongs to the Special Issue Current Research in Indoor Positioning and Localization)
Show Figures

Figure 1

37 pages, 9349 KiB  
Review
A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
by Toufiq Aziz and Insoo Koo
Appl. Sci. 2025, 15(3), 1544; https://doi.org/10.3390/app15031544 - 3 Feb 2025
Cited by 3 | Viewed by 3292
Abstract
The field of indoor localization is fast developing and has important ramifications for a number of areas, such as smart infrastructure development, healthcare settings, industrial automation, and military operations. Advances in a range of technologies, each suited to certain use cases and objectives, [...] Read more.
The field of indoor localization is fast developing and has important ramifications for a number of areas, such as smart infrastructure development, healthcare settings, industrial automation, and military operations. Advances in a range of technologies, each suited to certain use cases and objectives, have been fueled by the capacity to precisely locate objects or people inside places. Prominent indoor localization technologies like Bluetooth, Wi-Fi, ultra-wideband (UWB), ZigBee, and RFID-based systems are examined in this review, along with hybrid solutions that combine several technologies to get around their individual drawbacks and enhance system performance. The field still faces several obstacles in spite of these developments. Widespread acceptance is hampered by persistent problems such as signal interference, high energy consumption, and restricted scalability. The deployment of these systems is further complicated by elements like cost-effectiveness, privacy issues, and compatibility in a variety of situations. This study also examines potential avenues for future research to improve the precision, dependability, and versatility of indoor localization technology in order to overcome these obstacles. Designing systems with increased resilience to environmental changes, utilizing edge computing for real-time processing, and integrating artificial intelligence for predictive modeling are all promising areas of emphasis. This study attempts to help academics and practitioners navigate the changing terrain of indoor localization by offering a comprehensive picture of the field’s present status and future directions. Full article
Show Figures

Figure 1

13 pages, 1543 KiB  
Article
SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate
by Rahul Mundlamuri, Devasena Inupakutika and David Akopian
Sensors 2025, 25(3), 823; https://doi.org/10.3390/s25030823 - 30 Jan 2025
Cited by 1 | Viewed by 838
Abstract
The Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints, and machine learning techniques for position estimation. In [...] Read more.
The Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints, and machine learning techniques for position estimation. In particular, received signal strength indicators (RSSIs) and Channel State Information (CSI) in Wireless Local Area Networks (WLANs or Wi-Fi) have gained popularity and have been addressed in the literature. While RSSI signatures are easy to collect, the fluctuation of wireless signals resulting from environmental uncertainties leads to considerable variations in RSSIs, which poses a challenge to accurate localization on a single floor, not to mention multi-floor or even three-dimensional (3D) indoor localization. Considering recent E911 mandate attention to vertical location accuracy, this study aimed to investigate CSI from Wi-Fi signals to produce baseline Z-axis location data, which has not been thoroughly addressed. To that end, we utilized CSI measurements and two representative machine learning methods, an artificial neural network (ANN) and convolutional neural network (CNN), to estimate both 3D and vertical-axis positioning feasibility to achieve E911 accuracy compliance. Full article
(This article belongs to the Section Navigation and Positioning)
Show Figures

Figure 1

Back to TopTop