Indoor positioning framework for visually impaired people using Internet of Things

Tracking a moving user in indoor environments using Bluetooth low energy beacons.

The integration of location information with day-to-day applications will grow significantly over the next decade as the technology’s accuracy evolves. Currently outdoor localization, thanks to the Global Positioning System (GPS), has revolutionized navigation-based applications running on automotive GPS-enabled devices and smart phones. Applications range from guiding drivers to their destinations to providing point-by-point directions to the closest cinema or coffee shop. The success of GPS has been due to the reliability, availability, and practical accuracy that the orbiting satellites covering the globe provide. However, GPS provides limited coverage indoors and poor quality information in urban areas and near buildings which has motivated research in indoor geolocation in the last decade. A major challenge facing the indoor localization industry is the indoor propagation problem. The indoor environment is not friendly to wireless signals and that is manifested through limited coverage, multipath propagation, and the non-line-of-sight (NLOS) problem. Many research areas developed in the last decade to address those challenges. A fundamental aspect to addressing the challenges is understanding the indoor propagation mechanisms affecting the accuracy of localization; and efforts to measure, characterize and model the wireless channel from a geolocation perspective have received significant attention. Further algorithms and techniques to mitigate the multipath and NLOS problems have emerged recently for many systems (e.g. WLAN, WSN, UWB, etc.). Another area of research that has evolved around the indoor problem is the inertial measurement unit (IMU) based approaches in navigation and tracking (e.g. Kalman filters, particle filters, etc.) which adds another dimension to indoor location estimation. Finally hybrid technologies and signals of opportunity approaches have gained popularity recently to harness the most available information in order to improve robustness and accuracy of indoor localization systems. Although many approaches have been investigated and proposed, accurate indoor location remains a challenging problem. This special issue aims at attracting research papers focusing on mitigating the indoor geolocation challenges. The annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) is one of the premier conferences in the wireless research arena and has a long history of bringing together academia, industry and regulatory bodies. Today, it has become one of the IEEE Communication Society’s flagship conferences in telecommunications. The 23rd IEEE PIMRC was held successfully in Sydney Australia, from 9 to 12 September 2012, where several hundred researchers met to share their latest research results in wireless technologies. In this special issue, we have compiled the extended versions of the best papers on indoor geolocation techniques from this conference as well as a few invited papers from wellknown experts in this field. The invited authors have done an excellent job in substantially extending the conference version of the papers, which significantly improved the X. Li (&) Department of Electrical Engineering, University of North Texas, Denton, TX, USA e-mail: xinrong.li@unt.edu

In emergency scenarios, such as a terrorist attack or a building on fire, it is desirable to track first responders in order to coordinate the operation. Pedestrian tracking methods solely based on inertial measurement units in indoor environments are candidates for such operations since they do not depend on pre-installed infrastructure. A very powerful indoor navigation method represents collaborative simultaneous localization and mapping (collaborative SLAM), where the learned maps of several users can be combined in order to help indoor positioning. In this paper, maps are estimated from several similar trajectories (multiple users) or one user wearing multiple sensors. They are combined successively in order to obtain a precise map and positioning. For reducing complexity, the trajectories are divided into small portions (sliding window technique) and are partly successively applied to the collaborative SLAM algorithm. We investigate successive combinations of the map portions of several pedestrians and analyze the resulting position accuracy. The results depend on several parameters, e.g., the number of users or sensors, the sensor drifts, the amount of revisited area, the number of iterations, and the windows size. We provide a discussion about the choice of the parameters. The results show that the mean position error can be reduced to ≈0.5 m when applying partly successive collaborative SLAM.

Over the last years, Indoor Localization Systems (ILS) evolved, due to the inability of Global Positioning Systems (GPS) to localize in indoor environments. A variety of studies tackle indoor localization with technologies such as Bluetooth Beacons and RFID that require costly installation, or techniques such as Google Wi-Fi/Cell DB and fingerprinting that leverage from the already existing Wi-FI and telecommunication infrastructure. Additionally, recent studies attempt to solve the same problem using Bio-Inspired techniques, such as Artificial Neural Networks (ANNs) and Deep Neural Networks (DNN). In this paper, we introduce a Multi-Objective Optimization Radiomap Modelling (MOO-RM) based ILS. The MOO-RM ILS divides the dataset into clusters using a K-Means algorithm and trains ANN models on the data of each cluster. The resulting models are fed into a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), which minimizes the required storage space and the localization error, simultaneously. Our experimental studies demonstrate the superiority of the proposed approach on real datasets of Wi-Fi traces with respect to various existing techniques.

One key technique in Internet of Things(IoT) is localization, which has been an indispensable context for most of the applications in IoT. In this paper, we introduce an indoor location detecting platform based on the passive RFID (Radio Frequency Identification) technology. The major advantage of our platform is that it can be adopted to meet different detection granularity requirement based on the flexible RFID antenna design. Second, the only thing for a user to carry with is a tiny RFID tag on his/her shoes which is light weight and no charging power problems. Thirdly it will be shown that our design is easy to be augmented into normal artifacts in an unobtrusive way for the indoor environment. Two versions of the RFID-based location detection systems are introduced. One is called U-tile (Ubiquitous tile), in which passive RFID reader is embedded into the tiles. Another improved version is called Indoor-tracer, which adopts anti-collision RFID and more light weight comparing with the previous version U-tile so that it can embedded into various artifacts like bags and drawers. The performances of both two prototype systems were evaluated and the mutual-aware applications for remote family based on IoT were developed by using the platform.

The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms using Bluetooth Low Energy (BLE) beacons. We propose using the BLE beacon’s received signal strength indication (RSSI) and the geometric distance from the current beacon to the fingerprint point in the framework of fuzzy logic for calculating the Euclidean distance for the subsequent determination of location. According to our results, the fingerprinting algorithm with fuzzy logic type-2 (hesitant fuzzy sets) is fit for use as an indoor localization method with BLE beacons. The average error of localization is only 0.43 m, and the algorithm obtains a navigation precision of 98.2 ± 1%. This precision confirms that the algorithms provide great aid to a visually impaired person in unknown spaces, especially those designed without physical tactile guides, as confirmed by low Fréchet and Hausdorff distance values and high navigation efficiency index (NEI) scores.

Indoor localization systems (ILS) evolved over the last years, mainly due to the fact that Global Positioning Systems (GPS) lack precision or fail entirely to localize smartphone users in indoor environments. Many studies attempted to alleviate this issue by utilizing techniques developed on top of technologies, such as Bluetooth beacons and RFID that require costly installation of specialized equipment, or techniques such as Google Wi-Fi/Cell DB and fingerprinting that utilize the received signal strength intensity (RSSI) of the already existing Wi-Fi access points (APs) of a building or cellular towers of the surrounding telecommunication infrastructure. The latter approaches, however, face privacy and/or performance issues since they gain knowledge regarding the requested user location during the indoor localization process and/or require high energy, storage and computational power resources in order to localize the user in situ the smartphone device. In this chapter, we introduce a bio-inspired modeling approach for addressing all the aforementioned concerns. In particular, an artificial neural network is used to model an area’s RSSI data at the server side, which is then forwarded to the client side to predict the user’s current location on the smartphone device. The proposed approach improves the smartphone performance during user navigation while maintaining a fine-grain localization accuracy, but also preserving the user’s privacy with respect to conventional fingerprinting approaches. Our experimental studies demonstrate the superiority of the proposed approach on real datasets of Wi-Fi traces.

Nowadays, indoor positioning services of mobile phone with Bluetooth low energy (BLE) beacons are increasingly used. However, due to the indoor environment has dense multipath fading, the positioning system has low accuracy. In this paper, the accuracy study of trilateration method for indoor positioning with BLE beacons is presented. The indoor environment room with 9.0 m width, 10.2 m length and 3 BLE beacons is studied. The received signal strength (RSS) of each BLE beacon is measured by using mobile phone. The distance between each BLE beacon and mobile phone is evaluated using channel model. After that, the trilateration method is used to approximate the position of mobile phone. The accuracies, which are obtained from one, average five and average ten measurements, are studied. The distance errors of all positions and the cumulative distribution function (CDF) of the distance error are calculated and shown. The results show that the average multiple measurements tend to decrease the distance error.

We describe a low-cost wearable system that tracks the location of individuals indoors using commonly available inertial navigation sensors fused with radio frequency identification (RFID) tags placed around the smart environment. While conventional pedestrian dead reckoning (PDR) calculated with an inertial measurement unit (IMU) is susceptible to sensor drift inaccuracies, the proposed wearable prototype fuses the drift-sensitive IMU with a RFID tag reader. Passive RFID tags placed throughout the smart-building then act as fiducial markers that update the physical locations of each user, thereby correcting positional errors and sensor inaccuracy. Experimental measurements taken for a 55 m × 20 m 2D floor space indicate an over 1200% improvement in average error rate of the proposed RFID-fused system over dead reckoning alone.

Indoor localization has gained significant attention recently due to the growing demand for location-aware applications within indoor environments. Among various techniques, Wi-Fi fingerprinting combined with the Internet of Things (IoT) has emerged as a promising solution for accurate and cost-effective indoor localization. This paper surveys the existing research related to indoor localization using Wi-Fi fingerprinting with the IoT, aiming to provide a comprehensive understanding of the advancements, challenges, and potential applications in this field. The paper begins by introducing the fundamental concepts of indoor localization and the role of Wi-Fi fingerprinting in achieving accurate position estimation. In addition, this paper focuses on the contributions of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in improving localization accuracy, robustness, and scalability. The reviewed papers have been examined from various aspects, including system architecture, deployment strategies, fingerprint creation techniques, and localization algorithms, with a discussion about the advantages and limitations of each approach. In addition to discussing the state-of-the-art techniques, this paper identifies research gaps and open challenges in indoor localization using Wi-Fi fingerprinting with the IoT. The findings presented in this paper can guide future research efforts, leading to the development of intelligent and context-aware IoT applications within indoor environments.

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

Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.

Indoor Positioning System (IPS) has always been a focused area of research due to an increasing demand for IPS in a variety of civilian and defence applications. Bluetooth low energy beacons are best suited for IPS owing to their compatibility with mobile devices such as smart phones. In this paper, a standard bluetooth module is configured to work as a Bluetooth-Beacon (BT-Beacons) and an IPS is implemented using the RSSI values of signals from the BT-Beacons. The position of user in indoors is computed using the principle of trilateration. The proposed system achieved good positioning accuracy and is economical. Further, the proposed system can be improved to assist localization and navigation in indoor environment such as huge buildings.

BackgroundThe location in which physical activity and sedentary behavior are performed can provide valuable behavioral information, both in isolation and synergistically with other areas of physical activity and sedentary behavior research. Global positioning systems (GPS) have been used in physical activity research to identify outdoor location; however, while GPS can receive signals in certain indoor environments, it is not able to provide room- or subroom-level location. On average, adults spend a high proportion of their time indoors. A measure of indoor location would, therefore, provide valuable behavioral information.ObjectiveThis systematic review sought to identify and critique technology which has been or could be used to assess the location of physical activity and sedentary behavior.MethodsTo identify published research papers, four electronic databases were searched using key terms built around behavior, technology, and location. To be eligible for inclusion, papers were required to be published in English and describe a wearable or portable technology or device capable of measuring location. Searches were performed up to February 4, 2015. This was supplemented by backward and forward reference searching. In an attempt to include novel devices which may not yet have made their way into the published research, searches were also performed using three Internet search engines. Specialized software was used to download search results and thus mitigate the potential pitfalls of changing search algorithms.ResultsA total of 188 research papers met the inclusion criteria. Global positioning systems were the most widely used location technology in the published research, followed by wearable cameras, and radio-frequency identification. Internet search engines identified 81 global positioning systems, 35 real-time locating systems, and 21 wearable cameras. Real-time locating systems determine the indoor location of a wearable tag via the known location of reference nodes. Although the type of reference node and location determination method varies between manufacturers, Wi-Fi appears to be the most popular method.ConclusionsThe addition of location information to existing measures of physical activity and sedentary behavior will provide important behavioral information.

The Global Positioning System (GPS) uses signals from satellites to specialised devices to detect an entity’s location on Earth’s surface. However, GPS is ineffective for indoor navigation due to high power consumption and signal obstruction by walls. This study proposes a low-cost, energy-efficient alternative using Bluetooth 4.0-enabled Bluetooth Low Energy (BLE) beacons to enable Indoor Positioning and guide users in confined spaces, such as libraries. Tiny, low-energy BLE beacons transmit Advertising Packets at regular intervals. In contrast, Anchor Nodes send and receive signals to construct a Received Signal Strength Indication (RSSI)-based signal-to-distance model, enabling triangulation-based localisation. The system monitors behaviour and environmental interaction through RSSI analysis and user interaction with Beacons. Proven in a library setting, the BLE-based system proved extremely accurate in guiding users to destinations, bridging GPS shortcomings at minimal energy expenditure and cost.