Finding 9-1-1 Callers in Tall Buildings

Accurately determining a user’s floor location is essential for minimizing delays in emergency response. This paper presents a floor localization system intended for emergency calls. We aim to provide floor-level accuracy with minimum infrastructure support. Our approach is to use multiple sensors, all available in today’s smartphones, to trace a user’s vertical movements inside buildings. We make three contributions. First, we present a hybrid architecture for floor localization with emergency calls in mind. The architecture combines beacon-based infrastructure and sensor-based dead reckoning, striking the right balance between accurately determining a user’s location and minimizing the required infrastructure. Second, we present the elevator module for tracking a user’s movement in an elevator. The elevator module addresses three core challenges that make it difficult to accurately derive displacement from acceleration. Third, we present the stairway module which determines the number of floors a user has traveled on foot. Unlike previous systems that track users’ foot steps, our stairway module uses a novel landing counting technique

Unified cooperative location system

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do Grau de Mestre em Engenharia Informática.The widespread use of smaller, less expensive, and more capable mobile devices, has opened the door for more complex and varied mobile computing applications. Additionally, manufacturers are increasingly equipping these handheld devices with every type of wireless connectivity and sensors that can be explored for providing more complex services. In recent years, several techniques for location estimation have been developed, providing different degrees of accuracy. Some of these solutions require the installation of specific hardware in the environment, while others explore the existing infrastructure. In particular, it is possible to explore the existing communication infrastructure to build a location system relying on signal strength measures. However, while several systems already exist to locate users based on different approaches, there is no single one that is good for every situation while providing high accuracy, low cost and ubiquitous coverage. Not only that, but very few research has been made regarding on how a group of users can cooperate to improve accuracy or reduce energy consumption while using the location system. This work presents the Unified Cooperative Location System, a modular and extensible location system. Its modular design can use every available technology on each device and different algorithms for location estimation. This approach allows to provide location services with high availability by relying on different technologies. It also allows to reduce the energy consumption on devices by sharing the responsibility of executing energy-heavy operations. The system also includes an information exchange mechanism, allowing devices to gather location information from nearby users, like GPS or Wi-Fi, which would otherwise be unavailable for some. The results of our experiences show that the possibility of exchanging GSM information provides a practical solution for location estimation based on multiple GSM signals, thus significantly increasing location accuracy with this technology

A survey on wireless indoor localization from the device perspective

With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals. This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.</jats:p

On the application of graph neural networks for indoor positioning systems.

Due to the inability of GPS (or other GNSS methods) to provide satisfactory precision for the indoor location scenario, indoor positioning systems resort to other signals already available on site, typically Wi-Fi given its ubiquity. However, instead of relying on an error-prone propagation model as in ranging methods, the popular fingerprinting positioning technique considers a more direct data-driven approach to the problem. First of all, the area of interest is divided into zones, and then a machine learning algorithm is trained to map, for instance, power measurements (RSSI) from APs to the localization zone, thus effectively turning the problem into a classification one. However, although the positioning problem is a geometrical one, virtually all methods proposed in the literature disregard the underlying structure of the data, using generic machine learning algorithms. In this chapter we consider instead a graph-based learning method, Graph Neural Networks, a paradigm that has emerged in the last few years and that constitutes the state of the art for several problems. After presenting the pertinent theoretical background, we discuss two possibilities to construct the underlying graph for the positioning problem. We then perform a thorough evaluation of both possibilities and compare it with some of the most popular machine learning alternatives. The main conclusion is that these graph-based methods obtain systematically better results, particularly with regard to practical aspects (e.g., gracefully tolerating faulty APs), which makes them a serious candidate to consider when deploying positioning systems

Barometric phone sensors: More hype than hope!

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiative; Ministry of Education, Singapore under its Academic Research Funding Tier

Next Generation Emergency Call System with Enhanced Indoor Positioning

The emergency call systems in the United States and elsewhere are undergoing a transition from the PSTN-based legacy system to a new IP-based system. The new system is referred to as the Next Generation 9-1-1 (NG9-1-1) or NG112 system. We have built a prototype NG9-1-1 system which features media convergence and data integration that are unavailable in the current emergency calling system. The most important piece of information in the NG9-1-1 system is the caller's location. The caller's location is used for routing the call to the appropriate call center. The emergency responders use the caller's location to find the caller. Therefore, it is essential to determine the caller's location as precisely as possible to minimize delays in emergency response. Delays in response may result in loss of lives. When a person makes an emergency call outdoors using a mobile phone, the Global Positioning System (GPS) can provide the caller's location accurately. Indoor positioning, however, presents a challenge. GPS does not generally work indoors because satellite signals do not penetrate most buildings. Moreover, there is an important difference between determining location outdoors and indoors. Unlike outdoors, vertical accuracy is very important in indoor positioning because an error of few meters will send emergency responders to a different floor in a building, which may cause a significant delay in reaching the caller. This thesis presents a way to augment our NG9-1-1 prototype system with a new indoor positioning system. The indoor positioning system focuses on improving the accuracy of vertical location. Our goal is to provide floor-level accuracy with minimum infrastructure support. Our approach is to use a user's smartphone to trace her vertical movement inside buildings. We utilize multiple sensors available in today's smartphones to enhance positioning accuracy. This thesis makes three contributions. First, we present a hybrid architecture for floor localization with emergency calls in mind. The architecture combines beacon-based infrastructure and sensor-based dead reckoning, striking a balance between accurately determining a user's location and minimizing the required infrastructure. Second, we present the elevator module for tracking a user's movement in an elevator. The elevator module addresses three core challenges that make it difficult to accurately derive displacement from acceleration. Third, we present the stairway module which determines the number of floors a user has traveled on foot. Unlike previous systems that track users' foot steps, our stairway module uses a novel landing counting technique. Additionally, this thesis presents our work on designing and implementing an NG9-1-1 prototype system. We first demonstrate how emergency calls from various call origination devices are identified, routed to the proper Public Safety Answering Point (PSAP) based on the caller's location, and terminated by the call taker software at the PSAP. We then show how text communications such as Instant Messaging and Short Message Service can be integrated into the NG9-1-1 architecture. We also present GeoPS-PD, a polygon simplification algorithm designed to improve the performance of location-based routing. GeoPS-PD reduces the size of a polygon, which represents the service boundary of a PSAP in the NG9-1-1 system

Enhanced Indoor Localization System based on Inertial Navigation

An algorithm for indoor localization of pedestrians using an improved Inertial Navigation system is presented for smartphone based applications. When using standard inertial navigation algorithm, errors in sensors due to random noise and bias result in a large drift from the actual location with time. Novel corrections are introduced for the basic system to increase the accuracy by counteracting the accumulation of this drift error, which are applied using a Kalman filter framework. A generalized velocity model was applied to correct the walking velocity and the accuracy of the algorithm was investigated with three different velocity models which were derived from the actual velocity measured at the hip of walking person. Spatial constraints based on knowledge of indoor environment were applied to correct the walking direction. Analysis of absolute heading corrections from magnetic direction was performed . Results show that the proposed method with Gaussian velocity model achieves competitive accuracy with a 30\% less variance over Step and Heading approach proving the accuracy and robustness of proposed method. We also investigated the frequency of applying corrections and found that a 4\% corrections per step is required for improved accuracy. The proposed method is applicable in indoor localization and tracking applications based on smart phone where traditional approaches such as GNSS suffers from many issues

Rover-II: A Context-Aware Middleware for Pervasive Computing Environment

It is well recognized that context plays a significant role in all human endeavors. All decisions are based on information which has to be interpreted in context. By making information systems context-aware we can have systems that significantly enhance human capabilities to make critical decisions. A major challenge of context-aware systems is to balance usability with generality and extensibility. The relevant context changes depending on the particular application. The model used to represent the context and its relationship to entities must be general enough to allow additions of context categories without redesign while remaining usable across many applications. Also, while efforts are put in by application designers and developers to make applications context-aware, these efforts are customized to specific needs of the target application, and only certain common contexts like location and time are taken into account. Therefore, a general framework is called for that can (i) efficiently maintain, represent and integrate contextual information, (ii) act as an integration platform where different applications can share contexts and (iii) provide relevant services to make efficient use of the contextual information. This dissertation presents: * a generic and effective context model - Rover Context Model (RoCoM) that is structured around four primitives: entities, events, relationships, and activities; and practically usable through the concept of templates, * a flexible, extensible and generic ontology - Rover Context Model Ontology (RoCoMO) supporting the model, that addresses the shortcomings of existing ontologies, * an effective mechanism of modeling the context of a situation, through the concept of relevant context, with the help of situation graph, efficiently handling and making best use of context information, * a context middleware - Rover-II, which serves as a framework for contextual information integration, that could be used not just to store and compile the contextual information, but also integrate relevant services to enhance the context information; and more importantly, enable sharing of context among the applications subscribed to it, * the initial design and implementation of a distributed architecture for Rover-II, following a P2P arrangement inspired from Tapestry, The above concepts are illustrated through M-Urgency, a context-aware public safety system that has been deployed at the University of Maryland Police Department

Improved Tracking with IEEE 802.11 and Location Fingerprinting

In recent years, location-based services have become increasingly important for our daily lives. To estimate a user’s position, these days mainly the Global Positioning System (GPS) is used. In situations where GPS in unavailable, location fingerprinting with the help of IEEE 802.11 has proven as a viable alternative. However, the latter still suffers from some problems that impede a widespread use. These problems firstly are identified in this thesis, and secondly solutions to the different issues are introduced and evaluated. The covered topics contain means to improve the positioning accuracy of location fingerprinting with IEEE 802.11, algorithms to greatly decrease the effort that is necessary to set up a fingerprint database, and ways to estimate the error that has to be expected when estimating a position with IEEE 802.11 and location fingerprinting. Furthermore, the thesis covers problems that occur when estimating the position of a mobile user with IEEE 802.11 and location fingerprinting. Finally, an overview of application scenarios for the given algorithms is presented and a conclusion is given