Long-Range RFID Indoor Positioning System for an Autonomous Wheelchair

A new Radio-Frequency Identification (RFID) indoor positioning system (IPS) has been developed to operate in environments where the Global Positioning System (GPS) is unavailable. Traditional RFID tracking systems, such as anti-theft systems in clothing stores, typically work within close proximity to exit doors. This paper presents a novel RFID IPS capable of locating and tracking passive RFID tags over a larger area with greater precision. These tags, costing approximately EUR 0.10 each, are in the form of small stickers that can be attached to any item requiring tracking. The proposed system is designed for an autonomous wheelchair, built from scratch, which will be identified and monitored using passive RFID tags. Our new RFID IPS, with a 12 m range, is implemented in this "smart" wheelchair.

A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes

Accurate and reliable localization is crucial for various wireless communication applications. A multitude of studies have presented accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different anchor nodes, which may not accurately reflect real-world scenarios with varying noise distributions. In this paper, we propose a simple and efficient localization method based on hybrid RSS-AOA measurements that accounts for the varying measurement noises of different anchor nodes. We develop a closed-form estimator for the target location employing the linear-weighted least squares (LWLS) algorithm, where the weight of each LWLS equation is the inverse of its residual variance. Due to the unknown variances of LWLS equation residuals, we employ a two-stage LWLS method for estimation. The proposed method is computationally efficient, adaptable to different types of wireless communication systems and environments, and provides more accurate and reliable localization results compared to existing RSS-AOA localization techniques. Additionally, we derive the Cramer-Rao lower bound (CRLB) for the RSS-AOA signal sequences used in the proposed method. Simulation results demonstrate the superiority of the proposed method.

Efficient Deep Learning-Based Device-Free Indoor Localization Using Passive Infrared Sensors

Internet of Things (IoT) technology has continuously advanced over the past decade. As a result, device-free indoor localization functions have become a crucial part of application areas such as healthcare, safety, and energy management. Passive infrared (PIR) sensors detecting changes in temperature in an environment are one of the suitable options for human localization due to their lower cost, low energy consumption, electromagnetic tolerance, and enhanced private awareness. Although existing localization methods, including machine/deep learning, have been proposed to detect multiple persons based on signal phase and amplitude, they still face challenges regarding signal quality, ambiguity, and interference caused by the complex, interleaving movements of multiple persons. This paper proposes a novel deep learning method for multi-person localization using channel separation and template-matching techniques. The approach is based on a deep CNN-LSTM architecture with ensemble models using a mean bagging technique for achieving higher localization accuracy. Our results show that the proposed method can estimate the locations of two participants simultaneously with a mean distance error of 0.55 m, and 80% of the distance errors are within 0.8 m.

Accuracy of an Ultra-Wideband-Based Tracking System for Time-Motion Analysis in Tennis

Player-tracking systems provide vital time-motion and tactical data for analyzing athletic performance. Ultra-wideband (UWB) systems are promising for racquet sports due to their accuracy and cost-effectiveness compared to GNSS and optical systems. This study evaluated the accuracy of a UWB tracking system (GenGee Insait KS) for tennis-specific movements by comparing it with an optical motion capture system (VICON). Ten amateur players (International Tennis Numbers: 2-5) participated, performing seven exercises, including warm-up, agility drills, and tactical drills, with and without racquets. Raw data from both systems were processed to calculate the distances traversed. The average root mean square error between the two systems was 0.65 m (X-axis) and 0.76 m (Y-axis). Significant measurement discrepancies were observed (standardized mean difference: 0.86-1.95), except for jogging and walking exercises (p > 0.05). The overall percentage error was 16.29%. The intraclass correlation coefficient for distance measurements was 0.91, indicating good reliability. Tasks involving rapid acceleration and directional changes, such as the spider run, exhibited larger errors (mean bias: 4.13 m, effect size: 1.03). While the UWB system demonstrated acceptable accuracy for steady movements, it showed notable discrepancies during high-speed, tennis-specific activities. Overestimation due to arm movement and hip rotation suggests caution when applying arm-mounted UWB devices in training and competitive settings.

Mobile Robot Positioning with Wireless Fidelity Fingerprinting and Explainable Artificial Intelligence

Wireless Fidelity (Wi-Fi) based positioning has gained popularity for accurate indoor robot positioning in indoor navigation. In daily life, it is a low-cost solution because Wi-Fi infrastructure is already installed in many indoor areas. In addition, unlike the Global Navigation Satellite System (GNSS), Wi-Fi is more suitable for use indoors because signal blocking, attenuation, and reflection restrictions create a unique pattern in places with many Wi-Fi transmitters, and more precise positioning can be performed than GNSS. This paper proposes a machine learning-based method for Wi-Fi-enabled robot positioning in indoor environments. The contributions of this research include comprehensive 3D position estimation, utilization of existing Wi-Fi infrastructure, and a carefully collected dataset for evaluation. The results indicate that the AdaBoost algorithm attains a notable level of accuracy, utilizing the dBm signal strengths from Wi-Fi access points distributed throughout a four-floor building. The mean average error (MAE) values obtained in three axes with the Adaptive Boosting algorithm are 0.044 on the x-axis, 0.063 on the y-axis, and 0.003 m on the z-axis, respectively. In this study, the importance of various Wi-Fi access points was examined with explainable artificial intelligence methods, and the positioning performances obtained by using data from a smaller number of access points were examined. As a result, even when positioning was conducted with only seven selected Wi-Fi access points, the MAE value was found to be 0.811 for the x-axis, 0.492 for the y-axis, and 0.134 for the Z-axis, respectively.

An Integration of Deep Neural Network-Based Extended Kalman Filter (DNN-EKF) Method in Ultra-Wideband (UWB) Localization for Distance Loss Optimization

This paper examines the critical role of indoor positioning for robots, with a particular focus on small and confined spaces such as homes, warehouses, and similar environments. We develop an algorithm by integrating deep neural networks (DNNs) with the extended Kalman filter (EKF) method, which is known as DNN-EKF, to obtain an accurate indoor localization for ensuring precise and reliable robot movements within the use of Ultra-Wideband (UWB) technology. The study introduces a novel methodology that combines advanced technology, including DNN, filtering techniques, specifically the EKF and UWB technology, with the objective of enhancing the accuracy of indoor localization systems. The objective of integrating these technologies is to develop a more robust and dependable solution for robot navigation in challenging indoor environments. The proposed approach combines a DNN with the EKF to significantly improve indoor localization accuracy for mobile robots. The results clearly show that the proposed model outperforms existing methods, including NN-EKF, LPF-EKF, and other traditional approaches. In particular, the DNN-EKF method achieves optimal performance with the least distance loss compared to NN-EKF and LPF-EKF. These results highlight the superior effectiveness of the DNN-EKF method in providing precise localization in indoor environments, especially when utilizing UWB technology. This makes the model highly suitable for real-time robotic applications, particularly in dynamic and noisy environments.

Validating Ultra-Wideband Positioning System for Precision Cow Tracking in a Commercial Free-Stall Barn

UWB positioning systems offer innovative solutions for precision monitoring dairy cow behaviour and social dynamics, yet their performance in complex commercial barn environments requires thorough validation. This study evaluated the TrackLab 2.13 (Noldus) UWB system in a dairy barn housing 44-49 cows. We assessed stationary tag positioning using ten fixed tags over seven days, proximity detection between eight cows and ten stationary tags, and moving tag positioning using three tags on a stick to simulate cow movement. System performance varied by tag location, with reliability ranging from 4.09% to 96.73% and an overall mean accuracy of 0.126 ± 0.278 m for stationary tags. After the provider updated the software, only 0.62% of measures exceeded the declared accuracy of 0.30 m. Proximity detection between moving cows and stationary tags showed 81.42% accuracy within a 2-m range. While generally meeting specifications, spatial variations in accuracy and reliability were observed, particularly near barn perimeters. These findings highlight UWB technology's potential for precision livestock farming, welfare assessment, and behaviour research, including social interactions and space use patterns. Results emphasise the need for careful system setup, regular updates, and context-aware data interpretation in commercial settings to maximise benefits in animal welfare monitoring.

Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures

In the era of the Internet of Things (IoT), the demand for accurate positioning services has become increasingly critical, as location-based services (LBSs) depend on users' location data to deliver contextual functionalities. While the Global Positioning System (GPS) is widely regarded as the standard for outdoor localization due to its reliability and comprehensive coverage, its effectiveness in indoor positioning systems (IPSs) is limited by the inherent complexity of indoor environments. This paper examines the various measurement techniques and technological solutions that address the unique challenges posed by indoor environments. We specifically focus on three key aspects: (i) a comparative analysis of the different wireless technologies proposed for IPSs based on various methodologies, (ii) the challenges of IPSs, and (iii) forward-looking strategies for future research. In particular, we provide an in-depth evaluation of current IPSs, assessing them through multidimensional matrices that capture diverse architectural and design considerations, as well as evaluation metrics established in the literature. We further examine the challenges that impede the widespread deployment of IPSs and highlight the potential risk that these systems may not be recognized with a single, universally accepted standard method, unlike GPS for outdoor localization, which serves as the golden standard for positioning. Moreover, we outline several promising approaches that could address the existing challenges of IPSs. These include the application of transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning methods, all of which hold the potential to significantly enhance the accuracy and reliability of IPSs. By leveraging these advanced methodologies, we aim to improve the overall performance of IPSs, thus paving the way for more robust and dependable LBSs in indoor environments.

Design a novel algorithm for enhancing UWB positioning accuracy in GPS denied environments

Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.

Millimeter-precision positioning for wide-angle indoor area enabled by metalens-integrated camera

Due to signal shielding caused by building structures, conventional mature positioning technologies such as the Global Positioning System (GPS) are only suitable for outdoor navigation and detection. However, there are many scenarios that urgently require high-precision indoor positioning technologies, such as indoor wireless optical communications (OWCs), navigation in large buildings, and warehouse management. Here, we proposed a millimeter-precision indoor positioning technology based on metalens-integrated camera, which determines the position of the device through imaging of beacon LEDs. Thanks to the wide-angle imaging design of our metalens, the camera can accurately capture images of beacon LEDs even when it is situated in distant corner locations. Consequently, our localization scheme achieves millimeter-level positioning accuracy across majority of wide-angle (∼120°) indoor area. Compared to traditional positioning schemes by photodiode (PD), our imaging-based approach demonstrates superior resistance to interference, thereby safeguarding positioning precision from the external signals influence. Furthermore, the compact dimensions and high performances of the positioning device make it suitable for integration into highly portable devices, such as smartphones and drones, revealing its broad potential applications in the future.

Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.

A Survey of Visible-Light-Communication-Based Indoor Positioning Systems

There is a growing demand for indoor positioning systems (IPSs) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging, the visible light communication (VLC)-based IPS, which offers a combination of high accuracy, low cost, and energy efficiency. This article presents a comprehensive review of VLC-based IPSs, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. Experimental results have demonstrated that VLC-based systems can achieve localization accuracy to within 10 cm in controlled environments. The mechanisms of VLC-based IPSs are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity, operating range, and efficiency of VLC-based IPSs are examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize VLC-based IPSs, various strategies are explored, including the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the application of state-of-the-art localization algorithms. In addition, system parameters are carefully examined. These include LED placement, receiver sensitivity, and transmit power. Their impact on energy efficiency and localization accuracy is highlighted. Altogether, this paper serves as a comprehensive guide to VLC IPSs, providing in-depth insights into their vast potential and the challenges that they present.

Indoor augmented reality (AR) pedestrian navigation for emergency evacuation based on BIM and GIS

Nowadays with the increase of high-rise buildings, emergency evacuation is an indispensable part of urban environment management. Due to various disaster incidents occurred in indoor environments, research has concentrated on ways to deal with the different difficulties of indoor emergency evacuation. Although global navigation satellite systems (GNSSs) such as global positioning system (GPS) come in handy in outdoor spaces, they are not of much use in enclosed places, where satellite signals cannot penetrate easily. Therefore, other approaches must be considered for pedestrian navigation to cope with the indoor positioning problem. Another problem in such environments is the information of the building indoor space. The majority of the studies has used prepared maps of the building, which limits their methodology to that specific study area. However, in this study we have proposed an end-to-end method that takes advantage of BIM model of the building, thereby applicable to every structure that has an equivalent building information model (BIM). Moreover, we have used a mixture of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) method with relatively higher accuracy compared to other similar methods for navigating the user to the exit point. For implementing PDR, we used the sensors in smartphones to calculate user steps and direction. In addition, the navigational information was superimposed on the smartphone screen using augmented reality (AR) technology, thus communicating the direction information in a user-friendly manner. Finally, the AR mobile emergency evacuation application developed was assessed with a sample audience. After an experience with the app, they filled out a questionnaire which was designed in the system usability scale test (SUS) format. The evaluation results showed that the app achieved an acceptable suitability for usage.

Decentralized triangular relative localization of multiple UAVs based on relative range and inertial measurements

In this paper, a novel algorithm for cooperative relative localization of multiple Unmanned Aerial Vehicles (UAVs) is proposed based on relative range and inertial measurements. In this algorithm, a relative motion estimation model is established for each group of three UAVs that can form a triangle in space. Each group member estimates the relative position and heading angle of the other group members and shares the estimation results with the other group members. When members satisfy the triangle law among their estimated relative position vectors, they can estimate more accurately. After analyzing the observability of the presented model, the necessary conditions for observability are also given, which are less restrictive compared to previous methods. Then, the presented method is compared with the decentralized consensus-based Kalman filter approach. The results of both methods are analyzed in the presence of noise and disturbances, and under the condition of disconnection between the members.

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment

This research delves into advancing an ultra-wideband (UWB) localization system through the integration of filtering technologies (moving average (MVG), Kalman filter (KF), extended Kalman filter (EKF)) with a low-pass filter (LPF). We investigated new approaches to enhance the precision and reduce noise of the current filtering methods-MVG, KF, and EKF. Using a TurtleBot robotic platform with a camera, our research thoroughly examines the UWB system in various trajectory situations (square, circular, and free paths with 2 m, 2.2 m, and 5 m distances). Particularly in the square path trajectory with the lowest root mean square error (RMSE) values (40.22 mm on the X axis, and 78.71 mm on the Y axis), the extended Kalman filter with low-pass filter (EKF + LPF) shows notable accuracy. This filter stands out among the others. Furthermore, we find that integrated method using LPF outperforms MVG, KF, and EKF consistently, reducing the mean absolute error (MAE) to 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. This study highlights the effectiveness of EKF + LPF for accurate indoor localization for UWB systems.

A Holistic Approach for Enhancing Museum Performance and Visitor Experience

Managing modern museum content and visitor data analytics to achieve higher levels of visitor experience and overall museum performance is a complex and multidimensional issue involving several scientific aspects, such as exhibits' metadata management, visitor movement tracking and modelling, location/context-aware content provision, etc. In related prior research, most of the efforts have focused individually on some of these aspects and do not provide holistic approaches enhancing both museum performance and visitor experience. This paper proposes an integrated conceptualisation for improving these two aspects, involving four technological components. First, the adoption and parameterisation of four ontologies for the digital documentation and presentation of exhibits and their conservation methods, spatial management, and evaluation. Second, a tool for capturing visitor movement in near real-time, both anonymously (default) and eponymously (upon visitor consent). Third, a mobile application delivers personalised content to eponymous visitors based on static (e.g., demographic) and dynamic (e.g., visitor movement) data. Lastly, a platform assists museum administrators in managing visitor statistics and evaluating exhibits, collections, and routes based on visitors' behaviour and interactions. Preliminary results from a pilot implementation of this holistic approach in a multi-space high-traffic museum (MELTOPENLAB project) indicate that a cost-efficient, fully functional solution is feasible, and achieving an optimal trade-off between technical performance and cost efficiency is possible for museum administrators seeking unfragmented approaches that add value to their cultural heritage organisations.

Determining design criteria for indoor positioning system projects in hospitals: A design science approach

Objectives

Indoor navigation systems (indoor positioning systems) can improve orientation for patients in hospitals and help employees to track assets. Many hospitals would like to implement indoor positioning systems but do not know how. To support them in doing this, and to gain knowledge about the requirements for indoor positioning system implementation, our research identifies the design criteria relevant to indoor positioning system implementation projects.

Methods

A design science research process is built to design and evaluate an artifact. For this, five indoor positioning system developers and five hospital IT management representatives from various hospitals and companies in Germany are interviewed. Further, controlled experiments are conducted in Germany, using an ultrasound-based indoor positioning system.

Results

We determined and tested indoor positioning system functions, evaluated indoor positioning system performance criteria, and identified the operating conditions in hospitals. Our results show that indoor positioning system functions should provide a benefit to a hospital's daily operations, that some performance criteria are more important than others, and that operating conditions are important, e.g., radiation.

Conclusion

As a theoretical contribution, we show how design science research can be applied to the context of indoor positioning systems in hospitals. In addition, we make a practical contribution in that our propositions can be used for future indoor positioning system developments.

Design principles and implementation of receiver positioning and beam steering for laser power transfer systems

Laser power transfer (LPT) is an emerging technology that can provide convenient and long-range wireless power to the ever-expanding array of electronic devices. One of the biggest challenges in implementing LPT systems is to realize receiver positioning and beam steering (RPBS) for directing power toward the intended target which, however, have only been investigated by a few studies. Herein, a set of design principles is proposed, intended to assist researchers in developing systematic schemes for RPBS. Then, an open-source implementation of RPBS is designed and evaluated using two experimental protocols that simulate real-world receiver movement patterns. Notably, the experimental results show that the implementation enables 3D receiver movement within an operating range exceeding 2-m height and achieves RPBS in ∼1 s, sufficient for most indoor settings. Moreover, strategies that can improve the current design are discussed in detail. Overall, this study provides guidance that can facilitate new ideas and improvements to RPBS among researchers in relevant fields.

Indoor UWB Positioning and Position Tracking Data Set

Indoor positioning has become a hot topic in various fields, such as industry, healthcare, and commerce. Ultra-wideband (UWB) radio technology provides a cost-effective solution for range-based positioning, offering exceptionally high accuracy and precision. Its ultra-high temporal resolution enables range measurements with accuracy of a few centimeters. To develop and evaluate range-based positioning systems, we collected measurements in four different indoor environments using eight fixed devices and one mobile positioning device. To eliminate the fluctuation of walking speed from the data, we pre-defined a path in each indoor environment, similar to the human walking path, which was sampled at equidistant positions. We collected multiple range measurements and channel impulse response (CIR) data at each tag position on the path. The resulting dataset supports the development of range-based positioning and position tracking algorithms with various combinations of network topologies and anchor-tag combinations. We have also provided a full set of data analysis tools that enable the reproducibility of results and serve as a basis for further development of range-based UWB positioning algorithms.

Inertial Measurement Unit-Assisted Ultrasonic Tracking System for Ultrasound Probe Localization

Ultrasonic tracking is a promising technique in indoor object localization. However, limited success has been reported in dynamic orientational and positional ultrasonic tracking for ultrasound (US) probes due to its instability and relatively low accuracy. This article aims at developing an inertial measurement unit (IMU)-assisted ultrasonic tracking system that enables a high accuracy positional and orientational localization. The system was designed with the acoustic pressure field simulation of the transmitter, receiver configuration, position-variant error simulation, and sensor fusion. The prototype was tested in a tracking volume required in typical obstetric sonography within the typical operation speed ranges (slow mode and fast mode) of US probe movement. The performance in two different speed ranges was evaluated against a commercial optical tracking device. The results show that the proposed IMU-assisted US tracking system achieved centimeter-level positional tracking accuracy with the mean absolute error (MAE) of 12 mm and the MAE of orientational tracking was less than 1°. The results indicate the possibility of implementing the IMU-assisted ultrasonic tracking system in US probe localization.

Some Design Considerations in Passive Indoor Positioning Systems

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

extendGAN+: Transferable Data Augmentation Framework Using WGAN-GP for Data-Driven Indoor Localisation Model

For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Strength (RSS) could easily be affected by obstacles and other factors. In this paper, we propose an extendGAN+ pipeline that leverages up-sampling with the Dirichlet distribution to improve location prediction accuracy with small sample sizes, applies transferred WGAN-GP for synthetic data generation, and ensures data quality with a filtering module. The results highlight the effectiveness of the proposed data augmentation method not only by localisation performance but also showcase the variety of RSS patterns it could produce. Benchmarking against the baseline methods such as fingerprint, random forest, and its base dataset with localisation models, extendGAN+ shows improvements of up to 23.47%, 25.35%, and 18.88% respectively. Furthermore, compared to existing GAN+ methods, it reduces training time by a factor of four due to transfer learning and improves performance by 10.13%.

Indoor high-precision visible light positioning system using Jaya algorithm

Several indoor positioning systems that utilize visible light communication (VLC) have recently been developed. Due to the simple implementation and high precision, most of these systems are dependent on received signal strength (RSS). The position of the receiver can be estimated according to the positioning principle of the RSS. To improve positioning precision, an indoor three-dimensional (3D) visible light positioning (VLP) system with the Jaya algorithm is proposed. In contrast to other positioning algorithms, the Jaya algorithm has a simple structure with only one phase and achieves high accuracy without controlling the parameter settings. The simulation results show that an average error of 1.06 cm is achieved using the Jaya algorithm in 3D indoor positioning. The average errors of 3D positioning using the Harris Hawks optimization algorithm (HHO), ant colony algorithm with an area-based optimization model (ACO-ABOM), and modified artificial fish swam algorithm (MAFSA) are 2.21 cm, 1.86 cm and 1.56 cm, respectively. Furthermore, simulation experiments are performed in motion scenes, where a high-precision positioning error of 0.84 cm is achieved. The proposed algorithm is an efficient method for indoor localization and outperforms other indoor positioning algorithms.

Testing and Evaluation of Wi-Fi RTT Ranging Technology for Personal Mobility Applications

The rapid growth in the technological advancements of the smartphone industry has classified contemporary smartphones as a low-cost and high quality indoor positioning tools requiring no additional infrastructure or equipment. In recent years, the fine time measurement (FTM) protocol, achieved through the Wi-Fi round trip time (RTT) observable, available in the most recent models, has gained the interest of many research teams worldwide, especially those concerned with indoor localization problems. However, as the Wi-Fi RTT technology is still new, there is a limited number of studies addressing its potential and limitations relative to the positioning problem. This paper presents an investigation and performance evaluation of Wi-Fi RTT capability with a focus on range quality assessment. A set of experimental tests was carried out, considering 1D and 2D space, operating different smartphone devices at various operational settings and observation conditions. Furthermore, in order to address device-dependent and other type of biases in the raw ranges, alternative correction models were developed and tested. The obtained results indicate that Wi-Fi RTT is a promising technology capable of achieving a meter-level accuracy for ranges both in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, subject to suitable corrections identification and adaptation. From 1D ranging tests, an average mean absolute error (MAE) of 0.85 m and 1.24 m is achieved, for LOS and NLOS conditions, respectively, for 80% of the validation sample data. In 2D-space ranging tests, an average root mean square error (RMSE) of 1.1m is accomplished across the different devices. Furthermore, the analysis has shown that the selection of the bandwidth and the initiator-responder pair are crucial for the correction model selection, whilst knowledge of the type of operating environment (LOS and/or NLOS) can further contribute to Wi-Fi RTT range performance enhancement.

Low-cost depth/IMU intelligent sensor fusion for indoor robot navigation

This paper presents a mobile robot platform, which performs both indoor and outdoor localization based on an intelligent low-cost depth–inertial fusion approach. The proposed sensor fusion approach uses depth-based localization data to enhance the accuracy obtained by the inertial measurement unit (IMU) pose data through a depth–inertial fusion. The fusion approach is based on feedforward cascade correlation networks (CCNs). The aim of this fusion approach is to correct the drift accompanied by the use of the IMU sensor, using a depth camera. This approach also has the advantage of maintaining the high frequency of the IMU sensor and the accuracy of the depth camera. The estimated mobile robot dynamic states through the proposed approach are deployed and examined through real-time autonomous navigation. It is shown that using both the planned path and the continuous localization approach, the robot successfully controls its movement toward the destination. Several tests were conducted with different numbers of layers and percentages of the training set. It is shown that the best performance is obtained with 12 layers and 80% of the pose data used as a training set for CCN. The proposed framework is then compared to the solution based on fusing the information given by the XSens IMU–GPS sensor and the Kobuki robot built-in odometry solution. As demonstrated in the results, an enhanced performance was achieved with an average Euclidean error of 0.091 m by the CCN, which is lower than the error achieved by the artificial neural network by 56%.

Ubiquitous WiFi and Acoustic Sensing: Principles, Technologies, and Applications

With the increasing pervasiveness of mobile devices such as smartphones, smart TVs, and wearables, smart sensing, transforming the physical world into digital information based on various sensing medias, has drawn researchers' great attention. Among different sensing medias, WiFi and acoustic signals stand out due to their ubiquity and zero hardware cost. Based on different basic principles, researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition, motion tracking, indoor localization, health monitoring, and the like. To enable readers to get a comprehensive understanding of ubiquitous wireless sensing, we conduct a survey of existing work to introduce their underlying principles, proposed technologies, and practical applications. Besides we also discuss some open issues of this research area. Our survey reals that as a promising research direction, WiFi and acoustic sensing technologies can bring about fancy applications, but still have limitations in hardware restriction, robustness, and applicability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11390-023-3073-5.

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 Single-Layer Multimode Metasurface Antenna with a CPW-Fed Aperture for UWB Communication Applications

A single-layer multimode metasurface antenna is proposed with a coplanar waveguide (CPW)-fed aperture. The ultra-wideband (UWB) performance is implemented based on a three-step evolution process with the aid of characteristic mode analysis (CMA). Considering the efficient excitation with a fixed feeding structure, the metasurface modal current variation at different frequencies is analyzed and optimized, in addition to that at the resonant frequency. Correspondingly, the metasurface is firstly designed utilizing an array of 4 × 4 patches. Then, the 1 × 3 and the 1 × 1 parasitic patch arrays are located near the edge patches. Finally, every patch is split into two by a center slot along the current distribution of the required polarization. Four resonant modes of the metasurface become more desirable step by step and can be efficiently excited over the entire band. To enhance the impedance matching level, a pair of 5-stage gradient transitions are added to the CPW-fed slot. The slot mode combined with the four modes further improves the bandwidth. The experimental results demonstrate that the proposed antenna exhibits a 3 dB gain bandwidth of over 74% (4.0-8.7 GHz) with a peak gain of 8.2 dBi. The overall dimensions of the prototype are 1.40λ₀ × 1.40λ₀ × 0.075λ₀ (λ₀ is the free-space wavelength at 6 GHz).

Development and Evaluation of BLE-Based Room-Level Localization to Improve Hand Hygiene Performance Estimation

Hand hygiene is one of the most effective ways to prevent infection transmission. However, current electronic monitoring systems are not able to identify adherence to all hand hygiene (HH) guidelines. Location information can play a major role in enhancing HH monitoring resolution. This paper proposes a BLE-based solution to localize healthcare workers inside the patient room. Localization accuracy was evaluated using one to four beacons in a binary (entrance/proximal patient zone) or multiclass (entrance/sink/right side of the bed/left side of the bed) proximity-based positioning problem. Dynamic fingerprints were collected from nine different subjects performing 30 common nursing activities. Extremely randomized trees algorithm achieved the best accuracies of 81% and 71% in the binary and multiclass classifications, respectively. The proposed method can be further used as a proxy for caregiving activity recognition to improve the risk of infection transmission in healthcare settings.

Self-localization of monaural microphone using dipole sound sources

This paper introduces a method for indoor self-localization of a monaural microphone, which is required for various location-based services. By generating two pairs of dipole sound fields, localization is performed on each device, irrespective of the number of devices, based on orthogonal detection of observed signals and some simple operations that are feasible with limited computational resources. A method using multiple source frequencies for enhancing robustness against the effects of reflection and scattering is also proposed. The effectiveness of this method was evaluated by numerical simulations and experiments in an anechoic chamber and indoor environment, and the average errors for the azimuth and zenith angles were 4.8 and 1.9 deg, respectively, in the anechoic chamber and 21 and 11 deg, respectively, in the indoor environment.

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only.

An Intelligent Multi-Floor Navigational System Based on Speech, Facial Recognition and Voice Broadcasting Using Internet of Things

Modern technologies such as the Internet of Things (IoT) and physical systems used as navigation systems play an important role in locating a specific location in an unfamiliar environment. Due to recent technological developments, users can now incorporate these systems into mobile devices, which has a positive impact on the acceptance of navigational systems and the number of users who use them. The system that is used to find a specific location within a building is known as an indoor navigation system. In this study, we present a novel approach to adaptable and changeable multistory navigation systems that can be implemented in different environments such as libraries, grocery stores, shopping malls, and official buildings using facial and speech recognition with the help of voice broadcasting. We chose a library building for the experiment to help registered users find a specific book on different building floors. In the proposed system, to help the users, robots are placed on each floor of the building, communicating with each other, and with the person who needs navigational help. The proposed system uses an Android platform that consists of two separate applications: one for administration to add or remove settings and data, which in turn builds an environment map, while the second application is deployed on robots that interact with the users. The developed system was tested using two methods, namely system evaluation, and user evaluation. The evaluation of the system is based on the results of voice and face recognition by the user, and the model's performance relies on accuracy values obtained by testing out various values for the neural network parameters. The evaluation method adopted by the proposed system achieved an accuracy of 97.92% and 97.88% for both of the tasks. The user evaluation method using the developed Android applications was tested on multi-story libraries, and the results were obtained by gathering responses from users who interacted with the applications for navigation, such as to find a specific book. Almost all the users find it useful to have robots placed on each floor of the building for giving specific directions with automatic recognition and recall of what a person is searching for. The evaluation results show that the proposed system can be implemented in different environments, which shows its effectiveness.

Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems

In recent years, the use of indoor localization techniques has increased significantly in a large number of areas, including industry and healthcare, primarily for monitoring and tracking reasons. From the field of radio frequency technologies, an ultra-wideband (UWB) system offers comparatively high accuracy and is therefore suitable for use cases with high precision requirements in position determination, for example for localizing an employee when interacting with a machine tool on the shopfloor. Indoor positioning systems with radio signals are influenced by environmental obstacles. Although the influence of building structures like walls and furniture was already analysed in the literature before, the influence of metal machine tools was not yet evaluated concerning the accuracy of the position determination. Accordingly, the research question for this article is defined: To what extent is the positioning accuracy of the UWB system influenced by a metal machine tool?The accuracy was measured in a test setup, which consists of a total of four scenarios in a production environment. For this purpose, the visual contact between the transmitter and the receiver modules, including the influence of further interfering factors of a commercially available indoor positioning system, was improved step by step from scenario 1 to 4. A laser tracker was used as the reference measuring device. The data was analysed based on the type A evaluation of standard uncertainty according to the guide to the expression of uncertainty in measurement (GUM). It was possible to show an improvement in standard deviation from 87.64cm±32.27cm to 6.07cm±2.24cm with confidence level 95% and thus provides conclusions about the setup of an indoor positioning system on the shopfloor.

Modified FMCW Scheme for Improved Ultrasonic Positioning and Ranging of Unmanned Ground Vehicles at Distances < 50 mm

Unmanned ground vehicles (UGVs) find extensive use in various applications, including that within industrial environments. Efforts have been made to develop cheap, portable, and light-ranging/positioning systems to accurately locate their absolute/relative position and to automatically avoid potential obstacles and/or collisions with other drones. To this aim, a promising solution is the use of ultrasonic systems, which can be set up on UGVs and can potentially output a precise reconstruction of the drone's surroundings. In this framework, a so-called frequency-modulated continuous wave (FMCW) scheme is widely employed as a distance estimator. However, this technique suffers from low repeatability and accuracy at ranges of less than 50 mm when used in combination with low-resource hardware and commercial narrowband transducers, which is a distance range of the utmost importance to avoid potential collisions and/or imaging UGV surroundings. We hereby propose a modified FMCW-based scheme using an ad hoc time-shift of the reference signal. This was shown to improve performance at ranges below 50 mm while leaving the signal unaltered at greater distances. The capabilities of the modified FMCW were evaluated numerically and experimentally. A dramatic enhancement in performance was found for the proposed FMCW with respect to its standard counterpart, which is very close to that of the correlation approach. This work paves the way for the future use of FMCWs in applications requiring high precision.

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the "negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least 17.15% smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm.

RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization

Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE's) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique's accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path-Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path-Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.

OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning

In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces (Cmip) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the target. Thus, the objective function was minimized over the new feature spaces. Extensive experiments were conducted on two real-world scenarios of datasets, and the predictive power of the different modeling techniques were evaluated for predicting the location of a mobile device. The results have revealed that the proposed algorithm outperforms the state-of-the-art methods for fingerprint-based indoor positioning and is found robust to changing environments. Moreover, the proposed algorithm is not only resilient to fluctuating environments but also mitigates the model's overfitting problem.

An Experimental Assessment of People's Location Efficiency Using Low-Energy Communications-Based Movement Tracking

(1) Background: public transport demand dynamics represents important information for fleet managers and is also a key factor in making public transport attractive to reduce the environmental footprint of urban traffic. This research presents some experimental results on the assessment of low-energy communication technologies, such as Wi-Fi and Bluetooth, as support for people density and/or movement tracking sensing technologies. (2) Methods: the research is based on field measurements to determine the percentage of discoverable devices carried by people, in relation to the total number of physical persons in interest, different scenarios of mobile devices usage and evaluation of influences on radio signals' propagation, RSSI / RX read values, and efficiency of indoor localization, or in similar GPS-denied environments. Different situations are investigated, especially public transport-related ones, such as subway stations, indoors of commuting hubs, railway stations and trains. (3) Results: diagrams and experiments are presented, and models of signal behavior are also proposed. (4) Conclusions: recommendations on the efficiency of these non-conventional traveler and passenger flow tracking solutions and models are presented at the end of the paper.

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting

Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, djr (iv) correlation of the measurements, ρrjai and (v) signal propagation parameters σC and γ.

Spatial data analysis for intelligent buildings: Awareness of context and data uncertainty

Intelligent buildings are among the most active Internet-of-Things (IoT) verticals, encompassing various IoT-enabled devices and sensing technologies for digital transformation. Analysis of spatial data, a very common type of data collected in intelligent buildings, offers a lot of insights for many purposes such as facilitating space management and enhancing the utilization efficiency of buildings. In this paper, we recognize two major challenges in spatial data analysis for intelligent buildings (SDAIB): (1) the complicated analytical contexts that are related to the building space and internal entities and (2) the uncertainty of spatial data due to the limitations of positioning and other sensing technologies. To address these challenges, we identify and categorize different kinds of analytical contexts and spatial data uncertainties in SDAIB, and propose a unified modeling framework for handling them. Furthermore, we showcase how the proposed framework and the associated modeling techniques are used to enable context-aware and uncertainty-aware SDAIB, in the tasks of hotspot discovery, path planning, semantic trajectory generation, and distance monitoring. Finally, we offer several research directions of SDAIB, in line with the emerging trends of the IoT.

An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare

The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology's concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature.

WiFi Indoor Location Based on Area Segmentation

Indoor positioning is the basic requirement of future positioning services, and high-precision, low-cost indoor positioning algorithms are the key technology to achieve this goal. Different from outdoor maps, indoor data has the characteristic of uneven distribution and close correlation. In areas with low data density, in order to achieve a high-precision positioning effect, the positioning time will be correspondingly longer, but this is not necessary. The instability of WiFi leads to the introduction of noise when collecting data, which reduces the overall performance of the positioning system, so denoising is very necessary. For the above problems, a positioning system using the DBSCAN algorithm to segment regions and realize regionalized positioning is proposed. DBSCAN algorithm not only divides the dataset into core points and edge points, but also divides part of the data into noise points to achieve the effect of denoising. In the core part, the dimensionality of the data is reduced by using stacking auto-encoders (SAE), and the localization task is accomplished by using a deep neural network (DNN) with an adaptive learning rate. At the edge points, the random forest (RF) algorithm is used to complete the localization task. Finally, the proposed architecture is verified on the UJIIndoorLoc dataset. The experimental results show that our positioning accuracy does not exceed 1.5 m with a probability of less than 87.2% at the edge point, and the time is only 32 ms; the positioning accuracy does not exceed 1.5 m with a probability of less than 98.8% at the core point. Compared with indoor positioning algorithms such as multi-layer perceptron and K Nearest Neighbors (KNN), good results have been achieved.

Precision Enhancement of Wireless Localization System Using Passive DOA Multiple Sensor Network for Moving Target

Determining the direction-of-arrival (DOA) of any signal of interest has long been of great interest to the wireless localization research community for military and civilian applications. To efficiently facilitate the deployment of DOA systems, the accuracy of wireless localization is critical. Hence, this paper proposes a novel method to improve the prediction result of a wireless DOA localization system. By considering the signal variation existing in the complex environment, the actual location of the target can be determined including the maximum prediction error. Moreover, the scenario of the moving target is further investigated by incorporating the adaptive Kalman Filter algorithm to obtain the prediction route of the flying drone based on the accuracy assessment method. This proposed adaptive Kalman Filter is a high-efficiency algorithm that can filter out the noise in the multipath area and optimize the predicted data in real-time. The simulation result agrees well with the measured data and thus validates the proposed DOA system with the adaptive Kalman Filter algorithm. The measured DOA of the fixed radiation source obtained by a single base station and the moving route of a flying drone from a two-base station localization system are presented and compared with the calculated results. Results show that the prediction error in an outdoor region of 500×500 m2 is about 10−20 m, which demonstrate the usefulness of the proposed wireless DOA system deployment in practical applications.

Locating Smartphone Indoors by Using Tightly Coupling Bluetooth Ranging and Accelerometer Measurements

High-precision, low-cost, and wide coverage indoor positioning technology is the key to indoor and outdoor integrated location-based services, and it has broad market prospects and social value. However, achieving sub-meter level positioning accuracy in indoor environments remains a real challenge due to the blockage of indoor Global Navigation Satellite System (GNSS) signals, the complexity of indoor environments, and the unpredictability of user behavior. In this paper, we introduce a multi-module BLE broadcaster (MMBB)-based indoor positioning solution in which a tightly coupled fusion architecture is implemented on a smartphone. The solution integrates ranging measurements from multiple MMBB and the measurements of the accelerometer built into a smartphone. It becomes an instant positioning solution without any training phase by adopting a calibrated linearly segmented path loss model for ranging. We apply the pedestrian walking speed derived by the smartphone accelerometer to constrain an unscented Kalman filter method that is used to estimate the location and speed. The accuracy of the proposed method is 50% at 0.79 m and 95% at 1.6 m at in terms of horizontal error distance. Position update frequency is 10 Hz and the time to first fix is 0.1 s. The system can easily adapt a global coordinator system so that it can seamlessly work together with the GNSS to form an indoor/outdoor positioning solution.

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment.

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

Localization within Hostile Indoor Environments for Emergency Responders

Recent advances in techniques to improve indoor localization accuracy for personnel and asset tracking challenges has enabled wide-spread adoption within the retail, manufacturing, and health care industries. Most currently deployed systems use distance estimates from known reference locations to localize a person or asset using geometric lateration techniques. The distances are determined using one of many radio frequency (RF) based ranging techniques. Unfortunately, such techniques are susceptible to interference and multipath propagation caused by obstructions within buildings. Because range inaccuracies from known locations can directly lead to incorrect position estimates, these systems often require careful upfront deployment design to account for site-specific interference sources. However, the upfront system deployment requirements necessary to achieve high positioning accuracy with RF-based ranging systems makes the use of such systems impractical, particularly for structures constructed of challenging materials or dense configurations. In this paper, we evaluate and compare the accuracy and precision of alternative RF-based devices within a range of indoor spaces composed of different materials and sizes. These spaces range from large open areas such as gymnasiums to confined engineering labs of traditional buildings as well as training buildings at the local Fire Department Training Facility. Our goal is to identify the impact of alternative RF-based systems on localization accuracy and precision specifically for first responders that are called upon to traverse structures composed of different materials and configurations. Consequently, in this study we have specifically chosen spaces that are likely to be encountered by firefighters during building fires or emergency medical responses. Moreover, many of these indoor spaces can be considered hostile using RF-based ranging techniques. We built prototype wearable localization edge devices designed for first responders and characterize both ranging and localization accuracy and precision using alternative transceivers including Bluetooth Low Energy, 433 MHz, 915 MHz, and ultra-wide band. Our results show that in hostile environments, using ultra-wide band transceivers for localization consistently outperforms the alternatives in terms of precision and accuracy.

A Multi-Resident Number Estimation Method for Smart Homes

Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%.

Positioning in 5G and 6G Networks—A Survey

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios.