LocATER: Localization and accountability technologies for emergency responders

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

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this special issue present recent advances and new developments in indoor localization systems and technologies, proposing novel or improved methods with increased performance, providing insight into various aspects of quality control, and also introducing some unorthodox positioning methods.

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

Nowadays indoor positioning applications have received great attentions among short range wireless communication systems. In particular, Wireless Sensor Network (WSN) is the most widely used technology for indoor localization as it is cheap and easy to deploy. However, location accuracy is limited by the harsh propagation of the Radio Frequency (RF) signals in indoor environments, for instance, caused by the presence of obstacles between wireless nodes, multi-paths, interference, etc. To overcome these effects and improve positioning accuracy, Radio Frequency Identification (RFID) technology is employed to assist the WSN based positioning. This paper proposes two hybrid positioning approaches based both on WSN and RFID. One solution uses an Extended Kalman Filter (EKF) algorithm while the other one uses a Particle Filter (PF) approach. Both methods are compared through Matlab simulations which demonstrate that in general the WSN/RFID hybrid approach is feasible and provides good positioning estimation capabilities even under harsh indoor conditions.

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.

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.

In this paper, we introduce indoor localization technologies categorizing them into ON/OFF switch and senor based, wireless communication based, and image based technologies. Then we describe several representative techniques for each of them, emphasizing their strengths and weaknesses. We define important performance issues for indoor localization technologies and analyze recent technologies according to the performance issues. Our analyses show that ON/OFF switch based technologies are difficult to install, but accurate and not limited by light. Wireless communication technologies are not limited by light nor distance (space) and do not need additional device. Image based technologies do not need additional device but are limited by light, and their accuracies are affected by light. We believe that this paper provide wise view and necessary information for recent indoor localization technologies.

Multi-algorithm UWB-based localization method for mixed LOS/NLOS environments

Passive ultra-high frequency (UHF) radio frequency identification (RFID) provides an attractive technology for indoor positioning and localization. It offers contactless communication, non line-of-sight readability, compactness, and low cost. Localization techniques and algorithms have been actively researched recently from accuracy point of view. However, less attention has been paid to the antenna and its effects on the achieved localization accuracy. Particularly, wearable antennas in human localization applications are key components affecting the achieved accuracy and reliability. In this paper, we elaborate on the material choice for wearable UHF RFID patch antennas. Two highly attractive and flexible conductive electro-textiles are discussed and compared: anisotropic embroidered textile structure and uniform commercially available copper fabric. A patch tag antenna is designed for each electro-textile to operate at 866 MHz. The on-body readability and Received Signal Strength (RSS) in office environment for the antennas are recorded and analyzed. Based on the results we decide on the antenna feasibility in localization applications.

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.

The rapid increase of the senior population in our societies calls for innovative tools to early detect symptoms of cognitive decline. To this aim, several methods have been recently proposed that exploit Internet of Things data and artificial intelligence techniques to recognize abnormal behaviors. In particular, the analysis of position traces may enable early detection of cognitive decline. However, indoor movement analysis introduces several challenges. Indeed, indoor movements are constrained by the ambient shape and by the presence of obstacles, and are affected by variability of activity execution. In this paper, we propose a novel method to identify abnormal indoor movement patterns that may indicate cognitive decline according to well known clinical models. Our method relies on trajectory segmentation, visual feature extraction from trajectory segments, and vision-based deep learning on the edge. In order to avoid privacy issues, we rely on indoor localization technologies without the use of cameras. Preliminary experimental results with a real-world dataset gathered from cognitively healthy persons and people with dementia show that this research direction is promising.

The field of indoor localization is fast developing and has important ramifications for a number of areas, such as smart infrastructure development, healthcare settings, industrial automation, and military operations. Advances in a range of technologies, each suited to certain use cases and objectives, have been fueled by the capacity to precisely locate objects or people inside places. Prominent indoor localization technologies like Bluetooth, Wi-Fi, ultra-wideband (UWB), ZigBee, and RFID-based systems are examined in this review, along with hybrid solutions that combine several technologies to get around their individual drawbacks and enhance system performance. The field still faces several obstacles in spite of these developments. Widespread acceptance is hampered by persistent problems such as signal interference, high energy consumption, and restricted scalability. The deployment of these systems is further complicated by elements like cost-effectiveness, privacy issues, and compatibility in a variety of situations. This study also examines potential avenues for future research to improve the precision, dependability, and versatility of indoor localization technology in order to overcome these obstacles. Designing systems with increased resilience to environmental changes, utilizing edge computing for real-time processing, and integrating artificial intelligence for predictive modeling are all promising areas of emphasis. This study attempts to help academics and practitioners navigate the changing terrain of indoor localization by offering a comprehensive picture of the field’s present status and future directions.

Real-time monitoring of indoor air quality (IAQ) is significant for ensuring occupants’ health and comfort. While smart sensing technologies were used for IAQ monitoring, there is still a gap of quantitatively assessing IAQ and visualizing its interactions with the surrounding physical world. Therefore, this study proposes a novel real-time IAQ monitoring and visualization system. This system specifically integrates an unmanned ground vehicle (UGV), an indoor localization system, and the Internet of Things (IoT). First, an IoT sensing unit was developed to dynamically measure the concentration of different indoor air pollutants. Second, a UGV was configured with the capability of full autonomy for providing mobility to the sensing unit. Third, an indoor localization system was developed using ultra-wideband technology to track the sensing unit. Fourth, a cloud-based web server was created to establish communication for data transmission. Fifth, a 2D visualization interface was generated to visualize the indoor air condition and its interactions with the physical world. The proposed system was validated in a real-world application at the authors’ institution to test its applicability and performance. The proposed system achieved promising performance regarding (1) full navigation autonomy to sense the indoor environment, (2) reliably monitoring and quantifying the indoor air condition, (3) accurately localizing the sensing unit, and (4) intuitively visualizing the IAQ with consideration of both temporal and spatial characteristics of indoor air condition. This study contributes to the body of knowledge by enhancing existing practices of IAQ monitoring using a cost-effective and mobile robotic system and indoor localization technology.

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.

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.