Wi-Fi Received Signal Strength Research Articles

Multitarget Device-Free Localization via Cross-Domain Wi-Fi RSS Training Data and Attentional Prior Fusion

Device-free localization (DFL) using easily-obtained Wi-Fi received signal strength (RSS) has wide real-world applications for not requiring people to carry trackable devices. However, accurate multitarget DFL remains challenging due to the unknown number of targets, multipath interference (MPI), especially between nearby targets, and limited real-world data. In this study, we pioneeringly propose a transformer-based learning method with Wi-Fi RSS as input, and an attentional prior fusion module, to simultaneously locate an unknown number of people at random positions. To overcome the multitarget data collection challenges, we contribute a large-scale cross-domain real-simulation-augmentation training dataset with one and two real-world nearby non-person objects at limited positions and up to five simulated and augmented randomly distributed targets. Experimental results demonstrate our method's improved accuracy, generalization ability, and robustness with fewer Wi-Fi nodes than previous methods.

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

Automatic Fingerprint Data Labeling Using WiFi Signal and Smartphone Camera for Indoor Positioning

WiFi fingerprinting has been one of the most practical approaches for implementing an indoor positioning system. However, the need to measure location labels for fingerprint data has hindered the deployment of WiFi fingerprint‐based positioning systems. To reduce the cost of location labeling, several methods for automatically labeling locations or bypassing location labeling have been proposed. However, these methods still need to measure location labels for partial fingerprint data, or the estimated location labels are inaccurate. In this paper, we propose Heimdall, an automatic fingerprint data labeling method using a smartphone camera and WiFi signal for an indoor positioning system. Since Heimdall utilizes camera geometry for accurate automatic labeling, it aims to find the location labels of unlabeled fingerprint data composed of WiFi received signal strengths (RSSs) and photos collected using smartphones in multiple reference points (RPs). Using structure from motion (SfM) with all photos in the unlabeled fingerprint dataset, the location and orientation of the cameras can be recovered on an arbitrary world coordinate system. However, the world coordinate system derived by SfM is warped. In other words, it is different from the real‐world coordinate system where we express the location labels since SfM has similarity ambiguity, including scale, rotation, and translation ambiguity. Heimdall finds the location labels by estimating and applying an unwarping transformation that removes those ambiguities. We use two types of geometric constraints for estimating the unwarping transformation. First, several geometric entities in the warped‐world coordinates and real‐world coordinates are extracted from the photos and prior information about the indoor layout. The unwarping transformation transforms the geometric entities in warped coordinates to those in real‐world coordinates. Second, we utilize the log‐distance pathloss model with RSSs to derive the constraint about distances from the location label to the access points. We construct the optimization problem subject to those constraints to estimate the unwarping transformation. To evaluate the performance of Heimdall, we performed several experiments. Heimdall yields a mean location labeling error of 0.12 m and a standard deviation of location labeling error of 0.06 m for the best case in the experiment. These results demonstrate that a fingerprint‐based indoor positioning system can use the location labels estimated by Heimdall without performance degradation.

Selection of Signal Sources Influence at Indoor Positioning System

The indoor positioning task is an exciting problem because of the practical applications and the scientific challenges. This work emphasises the impact of signal source selection on the effectiveness of positioning models relying on Wi-Fi received signal strength data by eliminating unstable signal sources such as mobile access points. Two localisation algorithms, based on k-Nearest Neighbour and Random Forest, were applied to data from three academic buildings to test the effectiveness of various access points (APs) selection filters. Additionally, new hierarchical selection models were proposed. The methods divide building into zones to improve the characteristics of the positioning error by a local selection of APs. Hierarchical methods reduced the median number of AP to 67 in comparison to over 100 selected by existing methods, obtaining a statistically similar median error of 6.01 m.

Wi-Fi Received Signal Strength Indicator (RSSI) Factors in Influencing Indoor Signal: A Review

The Received Signal Strength Indicator (RSSI) is a crucial metric for assessing the quality of Wi-Fi signals, measured in decibels (dBm). The RSSI value is affected by several factors, such as distance from the access point, obstructions in the signal path, and interference from other electronic devices. Although these factors are commonly influencing RSSI, there is a lack of study to reveal the significance factors that influence indoor signal. This review article explores the various factors that influence the strength of Wi-Fi signals indoors, with a specific focus on the RSSI. This article discusses these factors in detail and their impact on the RSSI and overall quality of the Wi-Fi signal from literature. By understanding these factors, network administrators and users can take steps to optimize their Wi-Fi networks and improve the reliability and performance of their devices.

Device-Free Human Motion Detection Using Single Link WiFi Channel Measurements for Building Energy Management

Human motion is a vital parameter signalling indoor occupancy in occupant centric building energy management systems. In this letter, a WiFi based device free sensing model employing ESP32-a low cost embedded IoT device, is proposed as a suitable retrofit for human motion sensing in new and existing buildings. A low complexity feature set derived from WiFi received signal strength indicator and channel state information samples collected by ESP32 is utilized for training ensemble machine learning models. The proposed model shows an accuracy of up to 99.4% in classifying human motion in 3 different rooms of a residential building, with a single WiFi access point. The proposed model can be employed for motion driven smart load control and for a typical residential occupancy scenario, this approach exhibits a potential monthly electricity savings of up to 66.26% and a three times reduction in CO2 emissions.

MapLoc: LSTM-Based Location Estimation Using Uncertainty Radio Maps

With the growing demand for location-based services, fingerprint has become a hot topic in the area of Internet of Things (IoT). However, the performance of fingerprinting-based indoor localization systems is usually affected by the quality and granularity of fingerprints. In this paper, we present MapLoc, a Long Short-term Memory (LSTM)-based indoor localization system that takes advantage of the continuous indoor uncertainty maps created using both earth magnetic field readings and WiFi received signal strengths (RSS). A Deep Gaussian Process (DGP) model is trained to create indoor radio maps with confidence intervals, which are referred as uncertainty maps. Utilizing the uncertainty maps, an LSTM based location prediction model is pre-trained with artificial trajectory data sampled from the uncertainty maps, and then fine-tuned with the signal measurements collected in the field. In the training process, auxiliary outputs are implemented to overcome overfitting and improve the robustness of the system. Our extensive experiments demonstrate the outstanding performance of the proposed MapLoc system.

Combining Wi-Fi Fingerprinting and Pedestrian Dead Reckoning to Mitigate External Factors for a Sustainable Indoor Positioning System

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

Two-stage clustering for improve indoor positioning accuracy

High accurate positioning, as a key factor for the most resource management algorithms, has attracted a lot of attention due to its crucial role in some new applications such as smart factories, IoT, and autonomous cars. Indoor positioning, as another major category of positioning in addition to outdoor positioning, is a very complicated process and achieving high accuracy is a tough process. This paper proposes a two-stage clustering-based approach (TSCA) for indoor positioning to deal with highly complex data sets which was collected from a large-scale indoor radio system. In the first stage, a new clustering algorithm, called group matching method is developed, which divides the dataset into several groups (sub-datasets) according to the different reference signal received power (RSRP) values of the real-world dataset. In the second stage, KNN and its variants, are used to match and evaluate the location of each device in one of sub-datasets instead of the entire dataset, which can increase the accuracy of the positioning. This method can perfectly solve the problem of uneven distribution of reference point data in the process of data acquisition, which is a popular challenge for most real scene data acquisition. The proposed method is compared with several state-of-the-art ML methods such as Support Vector Regression (SVR), and clustering methods such as K-means. The results indicate a high positioning accuracy improvement of more than 55% compared to a modified KNN method use our own RSRP fingerprint dataset, and a 2D accuracy improvement of 13.36% and a 3D accuracy improvement of 10.3% compared to a traditional KNN method use a Wi-Fi Received Signal Strength fingerprint.

Cramér–Rao Lower Bound Analysis of Differential Signal Strength Fingerprinting for Crowdsourced IoT Localization

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced Indoor Positioning System (IPS) has to handle diverse devices and the inherent heterogeneity in Received Signal Strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this paper, we focus on Mean Differential Fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all APs from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér-Rao Lower Bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS datasets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.

A genetic based indoor positioning algorithm using Wi-Fi received signal strength and motion data

<span lang="EN-GB">The recent trend in location-based services has led to a proliferation of studies in indoor positioning technology. Wi-Fi</span><span lang="VI"> received signal strength </span><span lang="EN-US">indicator</span><span lang="VI"> (RSSI) </span><span lang="EN-GB">Fingerprinting and pedestrian dead reckoning (PDR) are the two best representatives from both approaches. This</span><span lang="VI"> research </span><span lang="EN-GB">proposed a genetic algorithm to combine Wi-Fi Fingerprinting and PDR. By taking advantage of PDR</span><span lang="VI"> and genetic algorithm</span><span lang="EN-GB">, we only need to collect a limited</span><span lang="VI"> number of</span><span lang="EN-GB"> points for the fingerprint dataset with known coordinates, then target trajectories' position can be estimated with high accuracy. Results from our experiments and simulations have shown that even in the scenario of noisy inertial</span><span lang="VI"> measurement unit (</span><span lang="EN-GB">IMU</span><span lang="VI">)</span><span lang="EN-GB"> sensors data, using RSSI measurements and the coordinate of 8 points, our proposed method was able to achieve 1.589 meters of average distance error which is 34.4 percent lower than the conventional Fingerprinting method.</span>

Landmark-Based Indoor Positioning: a Non-Intrusive and Cost-Effective Approach

This paper proposes a method for identifying indoor landmarks that can serve as fingerprints in indoor environments. The reason for this proposal is that every building has several distinctive locations. These signatures will be used as positional markers if they can be found. Landmarks such as Wi-Fi, entrance, windows, and elevators are recognizable. The proposed method detects these landmarks as follows: Wi-Fi Received Signal Strength (RSS) is used to detect the Wi-Fi landmark. The difference between GPS signals and Wi-Fi RSS values is used to locate the entrance landmark. GPS signals, though not available indoors, can be detected near windows and used to locate windows. Elevators are identified using two criteria: user movement behavior and loss of Wi-Fi RSS. The accuracy of detecting Wi-Fi, entrance, and window landmarks is 2 m, while the accuracy of detecting the elevator landmark is 1 m.

An extreme value based algorithm for improving the accuracy of WiFi localization

More mobile devices can obtain WiFi Received Signal Strength (RSS) for indoor positioning, so selecting reasonable Access Points (APs) for rapid positioning is important. Furthermore, RSS is vulnerable to diverse interference, making the positioning accuracy decrease. To solve the above mentioned issues, two novel algorithms, AP selection and positioning algorithms, are proposed, in this paper, which both use the location distance between RPs to construct a circle and treat the RSS from each AP individually. RPs in the same circle are seen as having similar RSS. 1) For each received signal, the proposed selection algorithm leverages the RSS extreme values (maximum and minimum values) of circles to find out RPs which may surround Test Point (TP), then it uses the degree of RSS difference and location concentration to evaluate the importance of each AP. 2) For the positioning algorithm, it uses the extreme values to find the useful APs and determines the weight of RPs. Extensive experiments are conducted on simulation and two real-world sites, results show the effectiveness of the proposed algorithms.

Fingerprinting-Based Indoor Positioning Using Data Fusion of Different Radiocommunication-Based Technologies

Wireless-radio-communication-based devices are used in more and more places with the spread of Industry 4.0. Localization plays a crucial part in many of these applications. In this paper, a novel radiocommunication-based indoor positioning method is proposed, which applies the fusion of fingerprints extracted with various technologies to improve the overall efficiency. The aim of the research is to apply the differences, which occur due to that different technologies behave differently in an indoor space. The proposed method was validated using training and test data collected in a laboratory. Four different technologies, namely WiFi received signal strength indication (RSSI), ultra-wideband (UWB) RSSI, UWB time of flight (TOF) and RSSI in 433 MHz frequency band and all of their possible combinations, were tested to examine the performance of the proposed method. Three widely used fingerprinting algorithms, the weighted k-nearest neighbor, the random forest, and the artificial neural network were implemented to evaluate their efficiency with the proposed method. The achieved results show that the accuracy of the localization can be improved by combining different technologies. The combination of the two low-cost technologies, i.e., the WiFi and the 433 MHz technology, resulted in an 11% improvement compared to the more accurate technology, i.e., the 433 MHz technology. Combining the UWB module with other technologies results in a less significant improvement since this sensor provides lower error rates, when used alone.

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments in clinical studies. Nevertheless, the real-life application of these technologies in studies involving a large number of participants for a significant observation period may be hindered by several practical challenges. In this study, we present a modified protocol from a previous intervention study for the mitigation of health effects from desert dust storms. The study involved two distinct population groups: asthmatic children aged 6-11 years and elderly patients with atrial fibrillation (AF). Both groups were equipped with a smartwatch for the assessment of physical activity (using a heart rate monitor, pedometer, and accelerometer) and location (using GPS signals to locate individuals in indoor "at home" or outdoor microenvironments). The participants were required to wear the smartwatch equipped with a data collection application on a daily basis, and data were transmitted via a wireless network to a centrally administered data collection platform for the near real-time assessment of compliance. Over a period of 26 months, more than 250 children and 50 patients with AF participated in the aforementioned study. The main technical challenges identified included restricting access to standard smartwatch features, such as gaming, internet browser, camera, and audio recording applications, technical issues, such as loss of GPS signal, especially in indoor environments, and the internal smartwatch settings interfering with the data collection application. The aim of this protocol is to demonstrate how the use of publicly available application lockers and device automation applications made it possible to address most of these challenges in a simple and cost-effective way. In addition, the inclusion of a Wi-Fi received signal strength indicator significantly improved indoor localization and largely minimized GPS signal misclassification. The implementation of these protocols during the roll-out of this intervention study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality.

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies.

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments in clinical studies. Nevertheless, the real-life application of these technologies in studies involving a large number of participants for a significant observation period may be hindered by several practical challenges. In this study, we present a modified protocol from a previous intervention study for the mitigation of health effects from desert dust storms. The study involved two distinct population groups: asthmatic children aged 6-11 years and elderly patients with atrial fibrillation (AF). Both groups were equipped with a smartwatch for the assessment of physical activity (using a heart rate monitor, pedometer, and accelerometer) and location (using GPS signals to locate individuals in indoor "at home" or outdoor microenvironments). The participants were required to wear the smartwatch equipped with a data collection application on a daily basis, and data were transmitted via a wireless network to a centrally administered data collection platform for the near real-time assessment of compliance. Over a period of 26 months, more than 250 children and 50 patients with AF participated in the aforementioned study. The main technical challenges identified included restricting access to standard smartwatch features, such as gaming, internet browser, camera, and audio recording applications, technical issues, such as loss of GPS signal, especially in indoor environments, and the internal smartwatch settings interfering with the data collection application. The aim of this protocol is to demonstrate how the use of publicly available application lockers and device automation applications made it possible to address most of these challenges in a simple and cost-effective way. In addition, the inclusion of a Wi-Fi received signal strength indicator significantly improved indoor localization and largely minimized GPS signal misclassification. The implementation of these protocols during the roll-out of this intervention study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality.

A Wi-Fi-Based Passive Indoor Positioning System via Entropy-Enhanced Deployment of Wi-Fi Sniffers.

This study presents a Wi-Fi-based passive indoor positioning system (IPS) that does not require active collaboration from the user or additional interfaces on the device-under-test (DUT). To maximise the accuracy of the IPS, the optimal deployment of Wi-Fi Sniffers in the area of interest is crucial. A modified Genetic Algorithm (GA) with an entropy-enhanced objective function is proposed to optimize the deployment. These Wi-Fi Sniffers are used to scan and collect the DUT's Wi-Fi received signal strength indicators (RSSIs) as Wi-Fi fingerprints, which are then mapped to reference points (RPs) in the physical world. The positioning algorithm utilises a weighted k-nearest neighbourhood (WKNN) method. Automated data collection of RSSI on each RP is achieved using a surveying robot for the Wi-Fi 2.4 GHz and 5 GHz bands. The preliminary results show that using only 20 Wi-Fi Sniffers as features for model training, the offline positioning accuracy is 2.2 m in terms of root mean squared error (RMSE). A proof-of-concept real-time online passive IPS is implemented to show that it is possible to detect the online presence of DUTs and obtain their RSSIs as online fingerprints to estimate their position.

A Machine Learning Approach to Passive Human Motion Detection Using WiFi Measurements From Commodity IoT Devices

Human motion is a primary indicator of indoor occupancy and activity. Motion sensing has paramount importance in the energy management of modern smart buildings and is employed for automated controls of lighting and heating, ventilation and air conditioning (HVAC) equipments. The all-pervasive WiFi infrastructure in urban buildings offers an opportunistic method of human motion detection through passive sensing of WiFi received signal strength indicator (RSSI) and channel state information (CSI). This technique unfolds a plethora of Building IoT related services, in addition to sustainable energy utilization and reduced emission of greenhouse gases. In this paper, a device free human motion detection method through WiFi RSSI and CSI collected from commercial-off-the-shelf (COTS) IoT devices, is proposed. Utilizing a laptop, a smartphone and an ESP32 as receivers, WiFi RSSI and CSI samples were collected from two residential buildings, to constitute 6 datasets. A four dimensional feature vector that exploits the data spread in time domain, is extracted from the collected samples and utilized to train a two stage ensemble machine learning model. A comparison of various RSSI based datasets indicates a mean cross validation accuracy of up to 99.7% and 97.8% in line of sight (LoS) and through-the-wall scenarios, respectively. The detection accuracy in non LoS environments can be enhanced using CSI based features, enabling motion detection in different rooms using a single WiFi router.

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1-0.3 m less than that of the traditional method.

WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time

The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals to reflect, attenuate, and diffract in different directions. Therefore, detecting the non-line-of-sight condition of WiFi Access Points is crucial for enhancing the performance of indoor positioning systems. To this end, we propose a novel feature selection algorithm for non-line-of-sight identification of the WiFi Access Points. Using the WiFi Received Signal Strength and Round Trip Time as inputs, our algorithm employs multi-scale selection and Machine Learning-based weighting methods to choose the most optimal feature sets. We evaluate the algorithm on a complex campus WiFi dataset to demonstrate a detection accuracy of 93% for all 13 Access Points using 34 out of 130 features and only 3 s of test samples at any given time. For individual Access Point line-of-sight identification, our algorithm achieved an accuracy of up to 98%. Finally, we make the dataset available publicly for further research.