Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

Humans and robots working together in an environment to enhance human performance is the aim of Industry 5.0. Although significant progress in outdoor positioning has been seen, indoor positioning remains a challenge. In this paper, we introduce a new research concept by exploiting the potential of indoor positioning for Industry 5.0. We use Wi-Fi Received Signal Strength Indicator (RSSI) with trilateration using cheap and easily available ESP32 Arduino boards for positioning as well as sending effective route signals to a human and a robot working in a simulated-indoor factory environment in real-time. We utilized machine learning models to detect safe closeness between two co-workers (a human subject and a robot). Experimental data and analysis show an average deviation of less than 1m from the actual distance while the targets are mobile or stationary.

In this paper, a new Indoor Positioning System (IPS) is introduced. The IPS allows people to navigate in an indoor environment such as malls, public buildings, hospitals and airports where GPS cannot be used due to accuracy problem. The proposed system addresses the multiple floor localization, which is not addressed carefully by researches. The system can recognize the user place across multiple floors, using the Wi-Fi Received Signal Strength Indicator (RSSI) and a smartphone barometer sensor to build the indoor environment radio map. The system is to be implemented and tested at Seef mall Arad in the Kingdom of Bahrain. Such new applications will enrich the quality of life and help in converting our building and city to a smart one.

Positioning services are increasingly used for applications such as navigation, advertising and social media. While outdoor navigation based on GPS and/or cellular systems works well, indoor navigation is a much tougher challenge. This paper proposes an indoor localization using Wi-Fi received signal strength indicator (RSSI) measurements from existing access points. Using RSSI data from multiple access points, additional access points will be added in the system thru linear regression statistical model to create virtual access points (VAPs). This paper fuses the low-cost and flexible system of fingerprint localization with VAP creation and the improvement in accuracy by adapting Kalman Filter (KF) and Particle Filter (Pf). The effectiveness of using KF with VAP has demonstrated positive results in previous research. This paper aims to integrate the use of PF in the system. To improve the performance of indoor localization, the proposed model will be an improved virtual access point localization, proposed filtering (VAP+KF+PF).

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Internet of Things (IoT) systems, along with Machine Learning (ML) and Artificial Intelligence (AI), performed well in present systems. For location-based IoT systems, it is vital to accurately estimate the object’s geographical position to differentiate objects in an indoor environment. In this research study, Received Signal Strength Indicators (RSSI) and ML-based solutions are proposed for indoor localization. Although the RSSI-based position techniques are much more interested in position estimation, as it does not require any additional hardware, the precision remains a significant issue because of the considerable fading effects, multipath propagation, and different parameters in the indoor environments. This research study examines ML-based Indoor Positioning Systems (IPS) using different signal filtering techniques. In this work, RSSI signals are filtered separately using three filters, Moving Average, Gaussian and Median, and the impact on position estimation is observed. To examine each filter’s performance, the error is compared in terms of statistical figures of RMSE (Root Mean Squared Error) and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (Coefficient of Determination). Most widely used Random Forest Regression (RFR) and Extra Tree Regressor (ETR) have been used as the Supervised ML techniques, and results are compared. According to the experimental results, the above filters can reduce the position estimation error to a maximum of 12 cm, which is negligible in many IPS applications with the ETR ML technique.

This work presents an indoor localization service with the aid of received signal strength indicator (RSSI) from IoT. The RSSI emmitted from the IoT are converted into the relative distance between the IoT nodes to determine the current location. In order to enhance the accuracy, the area of interest is further divided into few zones. The RSSI from the nearby IoT devices (BLE Beacons) detected in each zones are collected. This enable the system to first identify the correct zone located, and utilizing the relative distance from the RSSI at the zone to further acquire the current position inside the zone. A demo system has been developed with the Bluetoothe Low Energy (BLE) to evaluate the indoor localization service with this zone system.

Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user’s body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

Machine learning (ML) algorithms are increasingly used to predict outcomes in orthopaedic surgery, but their utility in hip arthroscopy remains unclear. This study aimed to (1) evaluate ML models for predicting outcomes, complications, and resource use following hip arthroscopy, (2) compare ML performance to traditional statistical methods, and (3) critically appraise the quality of existing literature. A systematic search of PUBMED, MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials was conducted on 4 April 2023. Included studies used ML models for clinical prediction in hip arthroscopy. Data on study design, outcomes, model development, validation and adherence to TRIPOD guidelines were extracted. Seventeen studies involving 37,549 patients (52.8% female; mean age 34.4 ± 6.1 years) were included. ML was used to predict clinical outcomes (47.1%), adverse events (47.1%) and resource utilisation (5.9%). The median area under the curve was 0.66 (range 0.5-0.94) for clinical outcomes and 0.7 (range 0.6-0.9) for adverse events. Resource utilisation was reported with a root-mean-square error of $3800, a logarithmic error of 0.2, and R² of 0.8. Only four studies compared ML to traditional regression; two found no difference, while two favoured ML. On average, studies met 12.3 of the 22 TRIPOD criteria, with only 29.4% meeting more than two-thirds. ML models in hip arthroscopy show variable performance in predicting outcomes and complications. Furthermore, most studies comparing ML models to traditional regression did not report any significant difference. Lastly, variability in adherence to TRIPOD guidelines highlights the need for future studies to improve transparency and reporting in the development of ML algorithms for hip arthroscopy. Given the potential of ML models in enhancing clinical decision-making for hip arthroscopy, along with the findings of the current study, their application should be used with caution. Level IV, systematic review of level II-IV studies.

An indoor localization system in wireless sensor networks has become a hot development area. Received signal strength indicator (RSSI)-based localization is a promising technique since it requires a relatively low configuration, battery power and easy control. However, the received signal strength is influenced by channel interference and propagation environments. This characteristic affects channel stability and location accuracy in RSSI-based localization. As a result, we propose a novel indoor localization system consisting of a preprocessing method and a post-processing method. To improve channel stability, the pre-processing method selects an optimal channel in terms of the smallest distance error. The optimal channel is less affected to IEEE 802.11. To develop location accuracy, the post-processing method performs maximum likelihood estimation-based location tracking scheme considering the reliability of the RSSI value measurement. We apply three methods to the existing MLE to improve the reliability of the RSSI value. By using this indoor localization system with preprocessing and post-processing, the location error can be reduced. We also proved the performance of the indoor localization system by conducting experiments in a real indoor environment.

This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed anchor, nor it needs a predefined network topology. The nodes could be asymmetric in terms of the available sensors onboard, the computational resources, and the power capacity. This asymmetry adversely affects the positioning performance of the weaker nodes. Collaboration between different nodes is achieved through a distributed estimator without the need of a single centralized computing element. The ranging measurement component of the system is based on the DW1000 UWB transceiver chip from Decawave, which is attached to a set of smartphones equipped with asymmetric sensors. The distributed positioning filter fuses, locally on each node, the relative range measurements, the reading from the internal sensors, and the Wi-Fi received signal strength indicator (RSSI) readings to obtain an estimate of the position of each node. The described system does not depend on fixed UWB anchors and supports online addition and removal of nodes and dynamic node role assignment, either as an anchor or as a rover. The performance of the system is evaluated by real-world test scenarios using a set of four smartphones navigating an indoor environment on foot. The performance is compared to that of a commercial UWB-based system. The results presented in this paper show that weak mobile nodes, in terms of available positioning sensors, can benefit from collaboration with other nearby nodes.

Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT’s prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.

The state of a meeting room (or of a classroom) provides important context to the level of interest (or participation level) in a meeting. For example, in one uninteresting meeting only one presenter presents text-filled slides, while other attendees are either checking their emails or even napping. In contrast, during a very interesting talk, all attendees are excited by the presentation and are involved in the discussions with frequent hand clapping. Thus, automatically detecting the state of a meeting room (or of a classroom) is an important and interesting problem. In this paper, we make the observation that the aforementioned group behaviors will have different motion signatures, as captured by a present Wi-Fi signal. Consequently, in this paper, we present a meeting room state detection approach leveraging environmental Wi-Fi signature. The Wi-Fi signal is provided by the existing access point (AP) in the meeting room and it is captured by one any Wi-Fi enabled device. The existing infrastructure provides all the information needed and no extra devices are needed. In our solution, different features are extracted from the raw Wi-Fi Received Signal Strength Indicator (RSSI) signal and four popular machine learning algorithms (e.g. support vector machine (SVM), decision tree, nearest neighbor and naive Bayes networks) are evaluated for detecting the state of a meeting room. Our approach is evaluated for two scenarios: classroom state detection and meeting room state detection. The experimental results show an accuracy of our proposed solution of 90.7% and 94.1% for classroom state detection and meeting/lab room state detection, respectively.

Non line of sight (NLOS) conditions significantly degrade the accuracy of Wi-Fi-Based indoor positioning systems (IPS). Although round-trip time (RTT) is generally more reliable than the received signal strength indicator (RSSI), it remains vulnerable to attenuation and multipath; moreover, dense RTT-compatible AP deployments may threaten scalability. This paper proposes a fingerprinting-based IPS that achieves high accuracy in NLOS scenarios, while remaining scalable and lightweight for Internet of Things (IoT) devices. By combining RTT, its standard deviation, and RSSI, accurate position estimates are obtained with only two RTT requests, reaching 93% accuracy and a 62 cm root mean squared error (RMSE) despite harsh conditions.