Address-based crowdsourcing radio map construction for Wi-Fi positioning systems

The rapid growth of mobile communication and the proliferation of smart phones have drawn significant attention to location-based services (LBS). The Wi-Fi positioning system (WPS) is a newly attractive method as a widely applicable positioning technique in LBS. In WPS, the received signal strength indication (RSSI) data of all Wi-Fi access points (APs) are measured, and stored in a huge database, as a form of radio fingerprint map. Because of the millions of APs in urban areas, radio fingerprint data are seriously contaminated. Therefore, we present a coherent filtering method for radio fingerprint data. All fingerprints used in the developed test bed are harvested from actual radio fingerprint measurements taken throughout Seoul, Korea. This demonstrates the practical usefulness of the proposed methodology.

Localization of users in indoor spaces is a common issue in many applications. Among various technologies, a Wi-Fi fingerprinting based localization solution has attracted much attention, since it can be easily deployed using the existing off-the-shelf mobile devices and wireless networks. However, the collection of the Wi-Fi radio map is quite labor-intensive, which limits its potential for large-scale application. In this paper, a visual-based approach is proposed for the construction of a radio map in anonymous indoor environments. This approach collects multi-sensor data, e.g., Wi-Fi signals, video frames, inertial readings, when people are walking in indoor environments with smartphones in their hands. Then, it spatially recovers the trajectories of people by using both visual and inertial information. Finally, it estimates the location of fingerprints from the trajectories and constructs a Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m. A weighted k-nearest neighbor method is also used to evaluate the constructed radio map. The average localization error is about 3.2 m, indicating that the quality of the constructed radio map is at the same level as those constructed by site surveying. However, this approach can greatly reduce the human labor cost, which increases the potential for applying it to large indoor environments.

A radio map captures the spatial distribution of wireless channel parameters, such as the strength of the signal received, across a geographic area. The problem of fine-grained three-dimensional (3D) radio map construction involves inferring a high-resolution radio map for the two-dimensional (2D) area at an arbitrary target height within a 3D region of interest, using radio samples collected by sensors sparsely distributed in that 3D region. Solutions to the problem are crucial for efficient spectrum management in 3D spaces, particularly for drones in the rapidly developing low-altitude economy. However, this problem is challenging due to ultra-sparse sampling, where the number of collected radio samples is far fewer than the desired resolution of the radio map to be estimated. In this paper, we design RadioLAM, a fine-grained 3D radio map construction paradigm built on generative Large Artificial Intelligence Models (LAMs). RadioLAM employs the creative power and the strong generalization capability of LAM to address the ultra-sparse sampling challenge. It consists of three key blocks: 1) an augmentation block, using the radio propagation model to project the radio samples collected at different heights to the 2D area at the target height; 2) a generation block, leveraging a diffusion-based LAM under an Mixture of Experts (MoE) architecture to generate a candidate set of fine-grained radio maps for the target 2D area; and 3) an election block, utilizing the radio propagation model as a guide to find the best map from the candidate set. Extensive simulations show that RadioLAM is able to solve the fine-grained 3D radio map construction problem efficiently from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art (SOTA). Furthermore, real-world experiments also confirm that RadioLAM achieves superior performance compared to SOTA.

The construction and update of a radio map are usually referred as the main drawbacks of WiFi fingerprinting, a very popular method in indoor localization research. For radio map update, some studies suggest taking new measurements at some random locations, usually from the ones used in the radio map construction. In this paper, we argue that the locations should not be random, and propose how to determine them. Given the set locations where the measurements used for the initial radio map construction were taken, a subset of locations for the update measurements is chosen through optimization so that the remaining locations found in the initial measurements are best approximated through regression. The regression method is Support Vector Regression (SVR) and the optimization is achieved using a genetic algorithm approach. We tested our approach using a database of WiFi measurements collected at a relatively dense set of locations during ten months in a university library setting. The experiments results show that, if no dramatic event occurs (e.g., relevant WiFi networks are changed), our approach outperforms other strategies for determining the collection locations for periodic updates. We also present a clear guide on how to conduct the radio map updates.

Accurate radio maps will be very much needed to provide environmental awareness and effectively manage future wireless networks. Most of the research so far has focused on developing power mapping algorithms for single and omnidirectional antenna systems. In this letter, we investigate the construction of crowdsourcing-based radio maps for 5G cellular systems with massive directional antenna arrays (spatial multiplexing), proposing an original technique based on semi-parametric Gaussian regression. The proposed method is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model-free</i> and provides highly accurate estimates of the radio maps, outperforming fully parametric and non-parametric solutions.

We have proposed a method of the radio map construction using clustering algorithm in our conventional work. The method enables us to accurately predict the radio environment while reducing the registered data size. However, this clustering algorithm has been only applied to the wireless system with fixed transmitter location. Thus, this paper considers the radio maps construction based on the clustering for the autonomous distributed networks that both transmitter and receiver dynamically move. The proposed method classifies the similar average received signal power samples using k-means++. The emulation results clarify that the proposed method can estimate the radio environment with high accuracy while reducing the registered data size compared to the conventional radio map.

Survey-based indoor positioning systems are expensive to establish and maintain. We describe how a crowdsourced WiFi radio map building problem can be framed as a trajectory alignment problem and further solved by the existing graph-based Simultaneous Localisation and Mapping (SLAM) framework. Specifically, we show how to exploit crowd-sourced WiFi signals and construct opportunistic constraints that satisfy the WiFi signal consistency modelled by Gaussian Processes (GP). We repurpose GraphSLAM to optimise these WiFi constraints collectively to approximate an optimised global signal consistency. We implement an automatic pipeline we call GPSLAM to produce radio maps based only on crowdsourced mobile data. Evaluation based on realistically crowdsourced mobile data generated naturally and passively by common visitors of multiple shopping malls demonstrates that GPSLAM can build radio maps that achieve comparable results to laborious manual surveys while the human intervention involved is minimised.

A Wi-Fi fingerprint consists of received signal strength values at a particular location along with the location information. A Wi-Fi radio map is constructed by maintaining a database of Wi-Fi fingerprints at various points in a building. A city radio map is a collection of radio maps for most buildings in a city. A highly precise citywide indoor positioning service is possible, if an accurate city radio map is available. However, tremendous time and effort are required to construct a city radio map through manual calibration. This paper proposes a method for constructing a city radio map through the crowdsourcing of Wi-Fi fingerprints from numerous smartphones. The proposed method classifies the buildings in a city into three categories: buildings in residential areas, commercial areas, and public areas. Then, it develops location-labeling techniques appropriate for the collected fingerprints from buildings in each category. Experiments conducted in the cities of Daejeon and Seoul revealed that the proposed method can construct a precise city radio map with minimal cost in a short period of time. Once city radio maps are constructed for most cities around the world, the global indoor positioning system will be completed.

Due to the heavy workload of RSS collection, the instability of WLAN signal strength and the disappearance of signals caused by complex indoor environments, the construction of radio map for wireless local area network (WLAN) fingerprint-based indoor positioning system is time-consuming and laborious. In order to rapidly deploy indoor WLAN positioning system, the bidirectional encoder representation from transformers (BERT) model is used to fill the missing signal in radio map and quickly build radio map. The radio map is imported into the BERT model in the form of natural language text, and the missing signal is filled by the BERT model. Since the number of input data in BERT model cannot exceed 512 words, the structure of BERT model is not suitable for WLAN signals with large data volume. Therefore, we redefine the model structure based on the original BERT model and fill in the missing signals in the radio map in parallel. In addition, the loss function is redefined. Except that each segment has a loss function, the weighted average value of all segment loss functions is defined as the total loss function. The experimental results show that the BERT model is better than the traditional linear interpolation method, compressed sensing algorithm and matrix completion algorithm in filling the missing signals in the fingerprint database, and the probability of error within 2 m reaches about 94%.

Fingerprint based Wi-Fi localisation system often takes a lot of human efforts to measure the received signal strength (RSS) of dense grid in an indoor environment. In this paper, we propose a novel fingerprint database construction method with reduced human effort to obtain optimal length of RSS time series and grid division. We verify the chaotic characteristics of RSS time series, and use phase space reconstruction algorithm to calculate the optimal length of RSS data to be collected at each reference point. Then Gaussian process regression (GPR) for fingerprinting based indoor localisation is used to construct the database with information of limited reference points. The hyper-parameters of GPR is calculated by conjugate gradient descent algorithm. The performance of the proposed radio map construction framework is validated in real indoor environment, and with using Bayesian positioning method, the localisation error mean can be 1.5 m while the construction time of radio map is greatly reduced with ensuring accuracy.

Introduction:An important and complicated problem related to the multilateration of Wi-Fi or Bluetooth Low Energy signals as well as Wi-Fi fingerprinting is the procedure of infrastructure adjustment which includes Wi-Fi radio map construction and Wi-Fi or Bluetooth Low Energy radio signal path loss model calibration.Purpose:Developing a method for navigation and Wi-Fi radio map construction, which would provide user’s indoor navigation, Bluetooth Low Energy path loss model calibration and Wi-Fi radio map collaborative semi-automatic construction.Results:The paper presents a collaborative semi-automatic Wi-Fi radio map construction method based on the combination of Bluetooth Low Energy multilateration, Wi-Fi fingerprinting, Wi-Fi radio map collaborative semiautomatic construction procedure and semi-automatic Bluetooth Low Energy path loss model calibration. For the semi-automatic calibration procedure of the Bluetooth Low Energy signal propagation model and for the method of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation, a calibration algorithm and an algorithm of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation were proposed, respectively. A mobile application has been developed which implements the proposed algorithms in order to test them and analyze the results.Practical relevance:The proposed method allows you to avoid time-consuming procedures of constructing a map of Wi-Fi radio signals and semi-automatic calibration of Bluetooth Low Energy signal propagation in the offline phase.

Wi-Fi ingerprint-based localization is currently the most promising approach to building metropolitan-scale localization systems for both indoor and outdoor environments. A Wi-Fi fingerprint localization system makes use of a radio map that contains Wi-Fi signals received at various locations, and uses the map for location estimation. Thus, the accuracy of a Wi-Fi fingerprint localization system greatly depends on the quality of its radio map. This paper explores various properties of a metropolitan radio map that may affect the accuracy of Wi-Fi fingerprint localization systems. In this study, metropolitan-scale radio maps are obtained using war walking and war driving. These maps contain hundreds of thousands of access points and signal samples. A detailed comparison analysis of selected radio map properties reveals how different map properties affect the difference between the positional accuracies of the driving and walking radio maps in Wi-Fi fingerprint-based localization.

Accurate radio environment map (REM) construction proves critical for efficient wireless spectrum management. Although conventional 2D REMs have demonstrated effectiveness in wireless network optimization, they inherently overlook vertical signal strength variations, which are vital for UAV operations, particularly in urban landscapes with skyscrapers or diverse terrain features. Current estimation approaches, including ground-based crowdsourcing, random sampling, and predetermined trajectory measurements, show limited capability in generating high-fidelity 3D REMs. This study proposes a joint optimization framework for UAV-enabled adaptive 3D radio mapping, integrating 3D REM construction with adaptive aerial sampling. At the heart of the construction module, a dual-branch encoder-decoder architecture fuses multi-scale features from sparse aerial measurements with building structural data, explicitly modeling obstruction effects through offline pre-training and online refinement to enhance generalization. For adaptive sampling, a diffusion-based trajectory planner dynamically optimizes UAV measurement paths by integrating environmental priors (e.g., building layouts), effectively overcoming the sparse-reward limitations inherent in reinforcement learning methods. Experimental validation demonstrates significant performance improvements across all evaluation metrics. Compared to 2D per-layer estimation methods, our 3D estimator achieves 49% superior structural similarity (SSIM) in construction accuracy, while the feature fusion module yields a 37% reduction in mean squared error (MSE). The diffusion-based planner outperforms reinforcement learning approaches by achieving 45% lower MSE and 18% higher SSIM in resultant map quality after 5,000 step iterations.

Radio fingerprinting positioning is widely used for smartphone location-based services and Internet of Things applications given its high availability and low cost. Radio fingerprinting-based algorithms, however, are subject to the forced matching problem and often yield estimated positions even when a user is actually located outside of the fingerprint region. A positioning solution in multistory buildings should be able to locate positions accurately on the current floor; but these methods may generate unreasonable positioning trajectories, such as irrationally passing through a wall, when fingerprinting positioning is fused the with inertial measurement unit to further improve the accuracy. A radio map must be surveyed dynamically on-the-move, as a dynamic fingerprint, to reduce the time costs and on-site workload. Unlike static fingerprint-based methods, dynamic fingerprinting samples are sparse. Thus, we propose an enhanced indoor positioning solution using spatial context knowledge, extracted from the sparse dynamic fingerprints. In the offline stage of radio map calculation, we extract the dynamic fingerprint features and store them in a spatial features database to reduce the computational time and storage space complexity. The proposed floor detection, region recognition, and path correction algorithms identify the online spatial contexts from the stored spatial features to improve positioning performance. This solution was applied on smartphones combining WiFi and Bluetooth low-energy radio signals in two typical scenarios. The experimental results show that the floor detection accuracy reached 99% while region recognition accuracy reached 90.75%. The positioning path correction method enhances the accuracy of smartphone indoor positioning from 3.27 to 2.56 m.

A non-contact interface for modular smartphones that can provide a data connection at a maximum MIPI rate of 6 Gb/s has been developed. A two-fold transmission line coupler, which is a small-size coupler that has a wide bandwidth, is proposed for modular smartphones, where the layout area is strictly limited. The coupler size is 6 mm2 for a 5 mm communication distance, which is 1/24 smaller than the conventional coupler. Since many wireless communication components, such as LTE, WiFi, and GPS transceivers, are assembled in a small module, the interference between the non-contact interface and the wireless transceivers should be suppressed. To improve noise immunity and reduce unwanted radiation from the coupler, an electromagnetic-compatibility robust pulse transceiver is proposed. A synchronous receiver using an edge counting clock recovery circuit improves noise immunity, and a bi-phase pulse transmitter reduces noise radiation in the GPS band. There is no EMS by LTE or WiFi signals on the data connection at BER ${ when the coupler is separated by a distance of 2 mm and no EMI on GPS signals at a separation of 10 mm. Compared with the state-of-the-art result, the highest energy efficiency (6 pJ/b) and space efficiency (1.2 $\text{mm}{^2}/1 \text{mm}$ distance) is achieved.