Special Issue on “Recent Advances in Indoor Localization Systems and Technologies”

Understanding the indoor whereabouts of individuals and objects is important, especially for those who fall within the 71% of visually impaired individuals with a school education, students in 450 special education units and many other areas and aspects in Sri Lanka. Researchers have declared that, there isn’t any particularly good localization system, and the performance should be evaluated considering the approach and application. The most well-known indoor positioning (IP) technologies that have been historically deployed are Bluetooth, Wi-Fi, RFID (radio frequency identification), IR (Infrared), and UV (ultraviolet) whereas received signal strength (RSSI), fingerprinting, and triangulation methods have been used as common IP techniques. The combination of both IP technologies and techniques creates an IP system, and the integration of machine learning and IoT with the structured system essentially delivers an accurate and more advanced system. This paper contains a detailed, analytical review of a developed indoor positioning system derived from the existing indoor localization techniques, localization technologies, localization systems, algorithms, and performance matrixes. This also provides a comprehensive comparison between numerous existing systems to justify the proposed solution. This project has been developed to achieve better accuracy through low-cost deployment as an effective system to fill the gap in the scarcity of positioning systems in the world. This paper presents a descriptive introduction and problem definition, a critical discussion of results, machine learning models, benefits of the project, and future works. As later justified, ESP32 microcontroller and BLE beacons are utilized with RSSI fingerprinting method to develop this IP system and, as a part of the project, two data visualization methods have been introduced here using NodeRED dashboard and LC display. Overall, this project was developed with an effective combination of RSSI fingerprinting, IoT protocols, machine learning, and data interpretation methods. KEYWORDS: Indoor positioning, RSSI, Fingerprinting, Machine Learning, MQTT, ESP32, BLE beacons, NodeRED

In recent years, indoor and outdoor positioning technology for the blind has attracted much more attention. The outdoor positioning technology has relatively matured. However, three-dimensional indoor positioning technology is facing many problems. Due to low positioning accuracy as well as slow positioning speed, there is no ideal implementation system at present. Light Fidelity (LiFi) technology is a new wireless transmission technology that uses the visible light spectrum, such as the light emitted by a light bulb, to transmit data. It transmits signals through the changing light of LED lights. When LED lights are properly laid out in the building, not only the function of lighting but also that of transmitting information are met at the same time. It is a good way to solve indoor positioning problems. This article studies an indoor positioning glasses for blind based on the LiFi technology. Different from other indoor positioning technologies such as ultrasound and wireless fidelity (WiFi), the design of indoor positioning glasses for the blind will propose new ideas of three-dimensional indoor positioning problems for the blind.

With the increasing demand for location-based services, indoor localization technology based on wireless sensor networks (WSNs) has recently received significant attention. Bluetooth low energy (BLE) has become a research hotspot among the numerous WSNs due to its ubiquitous deployment, low-power consumption, and low cost. Many previous BLE-based indoor localization technologies can only use received signal strength indication (RSSI) for localization in the 2-D plane and achieve coarse accuracy. However, high-accuracy location information is required in an increasing number of applications, e.g., elderly fall detection for medical applications, which urgently requires a new BLE-based localization technology in 3-D space. To this end, we propose Spotlight, an angle-based fine-grained indoor localization system in 3-D space using BLE sensors. The critical idea of the Spotlight is to measure the angle-of-arrival (AoA) and elevation-of-angle (EoA) of the signal from the target to the BLE receivers and utilize them to achieve accurate 3-D localization. Specifically, we develop an offset elimination algorithm and data stitching algorithm, utilizing the second-order cone (SoC) programming to obtain accurate angle estimates. Then, the simulated annealing algorithm (SAA) is used to search for the target location in 3-D space. Finally, we develop the prototype system with commercial off-the-shelf (COTS) BLE sensors and conduct extensive experiment measurements in real-world scenarios. The results demonstrate that the proposed system can achieve a median localization error of 36 cm in the 2-D plane and 48 cm in the 3-D space.

A brief summary of localization principle, merits and limitations of the aforementioned technologies is presented in Table 1.

In the post-epidemic era, with the resumption of production, orders for large-scale and small-batch production exponentially increase, new challenges to the productivity of the traditional production line have appeared, such as the redundant information, complex production process, which lead the traditional monitoring system cannot satisfy the requirements of rapid information interaction between management and executive layer in the workshop. According to the healthy control during epidemic, physical contact between workers should be reduced. Therefore, Ultra Wide Band(UWB) indoor localization and Augmented Reality(AR) technology are introduced, based on localization information and mobile device camera attitude estimation, an "actively push" mobile monitoring system can be built, which makes the workshop managers timely and completely monitor the manufacturing resources, non-contact production was also ensured. Finally, taking one equipment in a workshop as an example, the virtual visualization models are constructed in Unity software, and the effectiveness of the mobile monitoring system is realized based on UWB indoor localization technology and AR technology.

High-precision indoor positioning technology is regarded as one of the core components of artificial intelligence (AI) and Internet of Things (IoT) applications. Over the past decades, society has observed a burgeoning demand for indoor location-based services (iLBSs). Concurrently, ongoing technological innovations have been instrumental in establishing more accurate, particularly meter-level indoor positioning systems. In scenarios where the penetration of satellite signals indoors proves problematic, research efforts focused on high-precision intelligent indoor positioning technology have seen a substantial increase. Consequently, a stable assortment of location sources and their respective positioning methods have emerged, characterizing modern technological resilience. This academic composition serves to illuminate the current status of meter-level indoor positioning technologies. An in-depth overview is provided in this paper, segmenting these technologies into distinct types based on specific positioning principles such as geometric relationships, fingerprint matching, incremental estimation, and quantum navigation. The purpose and principles underlying each method are elucidated, followed by a rigorous examination and analysis of their respective technological strides. Subsequently, we encapsulate the unique attributes and strengths of high-precision indoor positioning technology in a concise summary. This thorough investigation aspires to be a catalyst in the progression and refinement of indoor positioning technologies. Lastly, we broach prospective trends, including diversification, intelligence, and popularization, and we speculate on a bright future ripe with opportunities for these technological innovations.

At present, location technology divides into outdoor location technology and indoor location technology. Comparing with other indoor location methods, ultra-wide band (UWB)indoor location technology has the advantages of strong anti-multipath interference ability and signal penetration ability. But, when this method faces large obstacles or metal obstacles, non line of sight (NLOS) will still occur. Indoor location system is a part of our project Home Care System for The Elderly. The NLOS of Indoor location system reduces the reliability of Home Care System for The Elderly. In order to improve the accuracy of location and efficiency of operation, an NLOS detection method based on k-nearest neighbors (KNN) algorithm is proposed. This algorithm modeled the training samples to find the K value with the highest recognition accuracy. The experimental results show that The NLOS detection accuracy of this method is 78% in the simulated apartment scene. With the same number of samples, the accuracy of the widely used Convolutional Neural Networks (CNN) algorithm is 67%. The experimental results show that KNN algorithm is more suitable for Home Care System for The Elderly for it can keep high accuracy with less requirement for the number of samples than CNN.

According to the paper published at 2010-2013 Indoor Positioning and Indoor Navigation (IPIN), this paper classify the indoor position technology to four classes by technical characteristics. The developing trends and main characteristics of future indoor position technology are deduced based on the paper data published by IPIN. The bottlenecks of indoor position technology was proposed. Finally, the future development trend of indoor position technology was predicted.

BackgroundHospital-acquired infections are still amongst the major problems health systems are facing. Their occurrence can lead to higher morbidity and mortality rates, increased length of hospital stay, and higher costs for both hospital and patients. Performing hand hygiene is a simple and inexpensive prevention measure, but healthcare workers’ compliance with it is often far from ideal. To raise awareness regarding hand hygiene compliance, individual behaviour change and performance optimization, we aimed to develop a gamification solution that collects data and provides real-time feedback accurately in a fun and engaging way.MethodsA Design Science Research Methodology (DSRM) was used to conduct this work. DSRM is useful to study the link between research and professional practices by designing, implementing and evaluating artifacts that address a specific need. It follows a development cycle (or iteration) composed by six activities. Two work iterations were performed applying gamification components, each using a different indoor location technology. Preliminary experiments, simulations and field studies were performed in an Intensive Care Unit (ICU) of a Portuguese tertiary hospital. Nurses working on this ICU were in a focus group during the research, participating in several sessions across the implementation process.ResultsNurses enjoyed the concept and considered that it allows for a unique opportunity to receive feedback regarding their performance. Tests performed on the indoor location technology applied in the first iteration regarding distances estimation presented an unacceptable lack of accuracy. Using a proximity-based technique, it was possible to identify the sequence of positions, but beacons presented an unstable behaviour. In the second work iteration, a different indoor location technology was explored but it did not work properly, so there was no chance of testing the solution as a whole (gamification application included).ConclusionsCombining automated monitoring systems with gamification seems to be an innovative and promising approach, based on the already achieved results. Involving nurses in the project since the beginning allowed to align the solution with their needs. Despite strong evolution through recent years, indoor location technologies are still not ready to be applied in the healthcare field with nursing wards.

Over the last decade, there have been several unsuccessful attempts to commercialize indoor localization technology for emergency responders mainly due to a poor product-market fit. This paper describes in detail the findings and conclusions of NSF Innovation Corps (I-Corps) Team 735 (Customer Discovery for Field-Deployable Indoor Localization Technology), part of the Spring 2016 DC-area cohort. As part of the I-Corps curriculum, the team interviewed over 100 relevant stakeholders to better understand the challenges facing the successful commercialization of indoor localization technologies. Throughout the course of these interviews, it became evident that the current solutions available commercially and in research environments did not meet all the requirements needed for effective response in challenging indoor environments. In addition, it is clear that previously demonstrated localization capabilities are sufficient to meet the technical requirements demanded by emergency response scenarios, but have previously not been unified in a holistic system that can be successfully introduced to the market.

Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization.

Indoor tracking and navigation (ITN) mainly depend on indoor localization. An impulse radio ultra-wideband (IR-UWB) is the most advanced technology for precision indoor localization. Besides its precision, the IR-UWB also has low complex hardware, low power consumption, and a flexible data rate that makes it the ideal candidate for ITN. However, two significant challenges impede the achievement of high-resolution accuracy and optimum performance: non-line-of-sight (NLOS) channel condition and multipath propagation (MPP). To enhance the performance under these conditions, the ranging error is estimated and corrected using parameters’ uncertainties. The uncertainties in the channel’s parameters have a relationship with the error, and these uncertainties are induced due to the NLOS and MPP propagation conditions. The parameters are collected in real-time experimental setups in two different environments. A proposed fuzzy inference model utilizes these uncertainties and the relationship to estimate ranging errors. The model is evaluated, and its performance is gauged in terms of residual ranging error cumulative distribution, root mean square error, and outage probability parameters using experimental measurements and compared with the state-of-the-art work. Moreover, the proposed fuzzy model is evaluated for computational complexity in terms of execution time and compared with the state-of-the-art work. The time is estimated on the targeted embedded system. The experimental and simulated results show that the proposed model effectively minimizes the ranging errors and computational burden. Moreover, the model does not induce a delay in estimating ranging error due to the non-statistical based solution.

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

Indoor localization technology is completing a generational shift, improving from meter to near-centimeter level accuracy, and shifting research priorities from improving accuracy to building scalable, deployable systems. Leading this shift are ultra-wideband (UWB) RF solutions that have demonstrated their potential for very high accuracy indoor localization.This talk will explore how and why UWB technologies have driven many of the recent advancements in wireless localization. It will then explore how non-traditional UWB designs can solve two key upcoming challenges: scaling up in number (to thousands of localized devices) and down in power (to microwatt scale power budgets). Enabling these opportunities requires decoupling localization from data transmission and rethinking how we utilize the UWB channel from the ground up.

Nowadays, location-based services are common in our daily lives. Traditional Global Positioning System (GPS) location can provide real-time location function in outdoor complex environments, but it is insufficient for indoor location. There are many indoor location technologies, such as ultrasound, Zigbee, RFID and WIFI. RFID location technology has attracted the attention of researchers due to its high precision and low cost. Most existing RFID location algorithms are based on RSSI (Received Signal Strength Indicator) measurement. When converting RSSI to distance, the inaccurate estimation of the path loss parameter may lead to large error. In order to reduce the deviation, this paper proposes a new RFID location algorithm. Specifically, the RSSI of the target tag is read in different directions of the antenna, and the position information is predicted by the general regression neural network, which is optimized by the optimization algorithm. The experimental results show the efficiency of our proposed algorithm.