Indoor localization methods based on Wi-Fi lateration and signal strength data collection

The paper describes two Wi-Fi lateration methods based on signal propagation model and signal strength data collection for indoor localization using Android-based mobile device. The considered methods use log-normal path loss model for signal propagation and received signal strength measurement collection for distance estimation and lateration approach for localization. The indoor signal propagation problem is resolved by received signal strength measuring and special ring radio map building those improve localization accuracy. Indoor localization technique opens possibilities for development various intelligent systems that provide the user location-based information inside buildings

Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI

We present a low complexity experimental RF-based indoor localization system based on the collection and processing of WiFi RSSI signals and processing using a RSS-based multi-lateration algorithm to determine a robotic mobile node's location. We use a real indoor wireless testbed called w-iLab.t that is deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this testbed is that it provides tools and interfaces using Global Environment for Network Innovations (GENI) project to easily create reproducible wireless network experiments in a controlled environment. We provide a low complexity algorithm to estimate the location of the mobile robots in the indoor environment. In addition, we provide a comparison between some of our collected measurements with their corresponding location estimation and the actual robot location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

WiFiPoz -- an accurate indoor positioning system

Location based services are becoming an important part of life. Wide adoption of GPS in mobile devices combined with cellular networks has practically solved the problem of outdoor localization needs. The problem of locating an indoor user has being studied only recently. Much research contributed to the innovative concept of an indoor positioning system. By analyzing different technologies and algorithms, this thesis concluded that, considering a trade-off between accuracy and cost, a Wi-Fi based Fingerprint method is proved to be the most promising approach to determine the location of a mobile device. However, the Fingerprint method works in two phases-an offline training phase (collection of Received Signal Strength signatures) and an online phase in which data from the first phase is used to determine the current position of a mobile user. The number of training points in a certain area has a direct impact on the accuracy of the system. As a result, the offline phase is a tedious and cumbersome process and the positioning systems are only as accurate as the offline training phase has been detailed. Moreover, the offline phase must be repeated every time a change in the environment occurs. To avoid these limitations, we focus on improving the accuracy of the indoor positioning system, without increasing the number of training points. This thesis presents a Wi-Fi based system for locating a user inside a building. The system is named WiFiPoz, which means Wi-Fi positioning system based on the zoning method. WiFiPoz has a novel approach to Fingerprint method that incorporates Propagation and zoning methods. Experimental results show that WiFiPoz is highly efficient both in accuracy and costs. Compared to traditional Fingerprint methods, with the optimization of the accuracy of the location estimation, WiFiPoz reduces the number of training points. This feature makes it possible to quickly adapt to changes in the environment. In order to explore another possible solution, this thesis also developed, implemented and tested an indoor positioning system named GIS (Geometric Information based positioning System), which is based on a model proposed by another researcher. Several experiments were run in the offline phase and results were compared between the traditional Fingerprint method, GIS and proposed WiFiPoz. We concluded that WiFiPoz is a more efficient and simple way to increase the accuracy of the location determination with fewer training points --Document

Generalizable Deep-Learning-Based Wireless Indoor Localization

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To address the generalizability challenge faced by conventionally trained deep learning localization models, we propose the use of meta-learning-based approaches. By leveraging meta-learning, we aim to improve the models\u27 ability to adapt to new environments without extensive retraining. Additionally, since meta-learning algorithms typically require diverse datasets from various scenarios, which can be difficult to collect specifically for localization tasks, we introduce a novel meta-learning algorithm called TB-MAML (Task Biased Model Agnostic Meta Learning). This algorithm is specifically designed to enhance generalization when dealing with limited datasets. Finally, we conduct an evaluation to compare the performance of TB-MAML-based localization with conventionally trained localization models and other meta-learning algorithms in the context of indoor localization

L/F-CIPS: Collaborative indoor positioning for smartphones with lateration and fingerprinting

The demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to alleviate these drawbacks. In this paper, we propose a smartphone-based collaborative architecture using neural networks and received signal strength, which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of traditional indoor positioning systems.The authors gratefully acknowledge funding from European Union’s Horizon 2020 RIA programme under the Marie Skłodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories). The associate editor coordinating the review of this article and approving it for publication was Prof. Name Surname (Corresponding authors: J. Torres-Sospedra and S. Casteleyn)

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Collaborative Techniques for Indoor Positioning Systems

The demand for Indoor Positioning Systems (IPSs) developed specifically for mobile and wearable devices is continuously growing as a consequence of the expansion of the global market of Location-based Services (LBS), increasing adoption of mobile LBS applications, and ubiquity of mobile/wearable devices in our daily life. Nevertheless, the design of mobile/wearable devices-based IPSs requires to fulfill additional design requirements, namely low power consumption, reuse of devices’ built-in technologies, and inexpensive and straightforward implementation. Within the available indoor positioning technologies, embedded in mobile/wearable devices, IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE) in combination with lateration and fingerprinting have received extensive attention from research communities to meet the requirements. Although these technologies are straightforward to implement in positioning approaches based on Received Signal Strength Indicator (RSSI), the positioning accuracy decreases mainly due to propagation signal fluctuations in Line-of-sight (LOS) and Non-line-of-sight (NLOS), and the heterogeneity of the devices’ hardware. Therefore, providing a solution to achieve the target accuracy within the given constraints remains an open issue. The motivation behind this doctoral thesis is to address the limitations of traditional IPSs for human positioning based on RSSI, which suffer from low accuracy due to signal fluctuations and hardware heterogeneity, and deployment cost constraints, considering the advantages provided by the ubiquity of mobile devices and collaborative and machine learning-based techniques. Therefore, the research undertaken in this doctoral thesis focuses on developing and evaluating mobile device-based collaborative indoor techniques, using Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), for human positioning to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. The methodology followed during the research consists of four phases. In the first phase, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to identify the key design aspects and evaluations used in/for CIPSs and the main concerns, limitations, and gaps reported in the literature. In the second phase, extensive experimental data collections using mobile devices and considering collaborative scenarios were performed. The data collected was used to create a mobile device-based BLE database for testing ranging collaborative indoor positioning approaches, and BLE and Wi-Fi radio maps to estimate devices’ position in the non-collaborative phase. Moreover, a detailed description of the methodology used for collecting and processing data and creating the database, as well as its structure, was provided to guarantee the reproducibility, use, and expansion of the database. In the third phase, the traditional methods to estimate distance (i.e., based on Logarithmic Distance Path Loss (LDPL) and fuzzy logic) and position (i.e., RSSI-lateration and fingerprinting–9-Nearest Neighbors (9-NN)) were described and evaluated in order to present their limitations and challenges. Also, two novel approaches to improve distance and positioning accuracy were proposed. In the last phase, our two proposed variants of collaborative indoor positioning system using MLP ANNs were developed to enhance the accuracy of the traditional indoor positioning approaches (BLE–RSSI lateration-based and fingerprinting) and evaluated them under real-world conditions to demonstrate their feasibility and benefits, and to present their limitations and future research avenues. The findings obtained in each of the aforementioned research phases correspond to the main contributions of this doctoral thesis. Specifically, the results of evaluating our CIPSs demonstrated that the first proposed variant of mobile device-based CIPS outperforms the positioning accuracy of the traditional lateration-based IPSs. Considering the distances among collaborating devices, our CIPS significantly outperforms the lateration baseline in short distances (≤ 4m), medium distances (>4m and ≤ 8m), and large distances (> 8m) with a maximum error reduction of 49.15 %, 19.24 %, and 21.48 % for the “median” metric, respectively. Regarding the second variant, the results demonstrated that for short distances between collaborating devices, our collaborative approach outperforms the traditional IPSs based on BLE–fingerprinting and Wi-Fi–fingerprinting with a maximum error reduction of 23.41% and 19.49% for the “75th percentile” and “90th percentile” metric, respectively. For medium distances, our proposed approach outperforms the traditional IPSs based on BLE–fingerprinting in the first 60% and after the 90% of cases in the Empirical Cumulative Distribution Function (ECDF) and only partially (20% of cases in the ECDF) the traditional IPSs based on Wi-Fi–fingerprinting. For larger distances, the performance of our proposed approach is worse than the traditional IPSs based on fingerprinting. Overall, the results demonstrate the usefulness and usability of our CIPSs to improve the positioning accuracy of traditional IPSs, namely IPSs based on BLE– lateration, BLE–fingerprinting, and Wi-Fi–fingerprinting under specific conditions. Mainly, conditions where the collaborative devices have short and medium distances between them. Moreover, the integration of MLP ANNs model in CIPSs allows us to use our approach under different scenarios and technologies, showing its level of generalizability, usefulness, and feasibility.Cotutelle-yhteistyöväitöskirja