Indoor Localization for Fire Safety : A brief overview of fundamentals, needs and requirements and applications

An indoor localization system for positioning evacuating people can be anticipated to increase the chances of a safe evacuation and effective rescue intervention in case of a tunnel fire. Such a system may utilize prevalent wireless technologies, e.g., Bluetooth, RFID and Wi-Fi, which today are used to survey incoming and outgoing traffic to a certain space or location, to estimate group sizes and to measure the duration of visits during normal operation of buildings. Examples also exist of where the same wireless technologies are used for safety purposes, for example to assess real-time location, tracking and monitoring of vehicles, personnel and equipment in mining environments. However, they are relatively few, and typically rely on a high degree of control over the people that are to be tracked, and their association with (connection to) the localization system used for the tracking. In this report, the results of a brief overview of the literature within the field of indoor localization in general, and the application of indoor localization systems within the field of particularly fire safety, is summarized. This information forms the underlying basis for the planning and execution of a future field study, in which an indoor Wi-Fi localization system will be tested and evaluated in terms of if, and if so how, it can be used to position evacuating people in tunnels. Whereas such a system allows digital footprints to be collected within a wireless network infrastructure (also already existing ones), questions remains to be answered regarding aspects such as precision and accuracy, and furthermore, how these aspects are affected by other independent variables. In the end of this report, examples of research questions deemed necessary to answer in order to enable a sound evaluation of the system is presented. These need to be addressed in the future planning of the above-mentioned field study

Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios

In the last decades, the rise of life expectancy has accelerated the demand for new technological solutions to provide a longer life with improved quality. One of the major areas of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose, indoor positioning systems are valuable tools and can be classified depending on the need of a supporting infrastructure. Infrastructure-based systems require the investment on expensive equipment and existing infrastructure-free systems, although rely on the pervasively available characteristics of the buildings, present some limitations regarding the extensive process of acquiring and maintaining fingerprints, the maps that store the environmental characteristics to be used in the localisation phase. These problems hinder indoor positioning systems to be deployed in most scenarios. To overcome these limitations, an algorithm for the automatic construction of indoor floor plans and environmental fingerprints is proposed. With the use of crowdsourcing techniques, where the extensiveness of a task is reduced with the help of a large undefined group of users, the algorithm relies on the combination ofmultiple sources of information, collected in a non-annotated way by common smartphones. The crowdsourced data is composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into smaller groups, each corresponding to specific areas of the building. Distance metrics applied to magnetic field signals are used to identify geomagnetic similarities between different users’ trajectories. The building’s floor plan is then automatically created, which results in fingerprints labelled with physical locations. Experimental results show that the proposed algorithm achieved comparable floor plan and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free systems. With these results, this solution can be applied in any fingerprinting-based indoor positioning system

Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies

Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe

Improving fingerprint-based positioning by using IEEE 802.11mc FTM/RTT observables

Received signal strength (RSS) has been one of the most used observables for location purposes due to its availability at almost every wireless device. However, the volatile nature of RSS tends to yield to non-reliable location solutions. IEEE 802.11mc enabled the use of the round trip time (RTT) for positioning, which is expected to be a more consistent observable for location purposes. This approach has been gaining support from several companies such as Google, which introduced that feature in the Android O.S. As a result, RTT estimation is now available in several recent off-the-shelf devices, opening a wide range of new approaches for computing location. However, RTT has been traditionally addressed to multilateration solutions. Few works exist that assess the feasibility of the RTT as an accurate feature in positioning methods based on classification algorithms. An attempt is made in this paper to fill this gap by investigating the performance of several classification models in terms of accuracy and positioning errors. The performance is assessed using different AP layouts, distinct AP vendors, and different frequency bands. The accuracy and precision of the RTT-based position estimation is always better than the one obtained with RSS in all the studied scenarios, and especially when few APs are available. In addition, all the considered ML algorithms perform pretty well. As a result, it is not necessary to use more complex solutions (e.g., SVM) when simpler ones (e.g., nearest neighbor classifiers) achieve similar results both in terms of accuracy and location error.This research was partially supported by MCIN/AEI/10.13039/ 501100011033 and ERDF “A way of making Europe” under grant PGC2018-099945-BI00, and by the European GNSS Agency (GSA) under grant GSA/GRANT/04/2019/BANSHEEPeer ReviewedPostprint (published version

Fingerprint-based Wi-Fi indoor localization using map and inertial sensors

It is a common understanding that the localization accuracy can be improved by indoor maps and inertial sensors. However, there is a lack of concrete and generic solutions that combine these two features together and practically demonstrate its validity. This article aims to provide such a solution based on the mainstream fingerprint-based indoor localization approach. First, we introduce the theorem called reference points placement, which gives a theoretical guide to place reference points. Second, we design a Wi-Fi signal propagation-based cluster algorithm to reduce the amount of computation. The paper gives a parameter called reliability to overcome the skewing of inertial sensors. Then we also present Kalman filter and Markov chain to predict the system status. The system is able to provide high-accuracy real-time tracking by integrating indoor map and inertial sensors with Wi-Fi signal strength. Finally, the proposed work is evaluated and compared with the previous Wi-Fi indoor localization systems. In addition, the effect of inertial sensors’ reliability is also discussed. Results are drawn from a campus office building which is about 80 m×140 m with 57 access points

Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles

Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly reduces the efforts for collecting WiFi fingerprints. Furthermore, since these NAVs explore a 3D space, the WiFi fingerprints of a 3D space can be obtained increasing the localization accuracy. The proposed system is implemented on a commercially available miniature open-source quadcopter platform by integrating a contemporary WiFi - fingerprint - based localization system. Experimental results demonstrate that the localization error is about 2m, which exhibits only about 20cm of accuracy degradation compared with the manual WiFi fingerprint survey methods

Indoor navigation systems based on data mining techniques in internet of things: a survey

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges