Precise positioning systems for Vehicular Ad-Hoc Networks

Vehicular Ad Hoc Networks (VANET) is a very promising research venue that can offers many useful and critical applications including the safety applications. Most of these applications require that each vehicle knows precisely its current position in real time. GPS is the most common positioning technique for VANET. However, it is not accurate. Moreover, the GPS signals cannot be received in the tunnels, undergrounds, or near tall buildings. Thus, no positioning service can be obtained in these locations. Even if the Deferential GPS (DGPS) can provide high accuracy, but still no GPS converge in these locations. In this paper, we provide positioning techniques for VANET that can provide accurate positioning service in the areas where GPS signals are hindered by the obstacles. Experimental results show significant improvement in the accuracy. This allows when combined with DGPS the continuity of a precise positioning service that can be used by most of the VANET applications.Comment: 15 pages, 15 figures, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 4, No. 2, April 201

Indoor Localization Using Wi-Fi Signals

RÉSUMÉ Plusieurs approches ont été développées pour localiser des appareils mobiles à l'intérieur de bâtiments d’une façon précise. Certaines donnent une précision de moins d'un mètre, mais elles nécessitent des infrastructures et du matériel spécifiques. D'autres utilisent une infrastructure qui est déjà déployée, mais donnent une position avec une précision inférieure. Dans ce mémoire, nous proposons plusieurs méthodes de positionnement basées sur les mesures de l'intensité du signal reçu d'une infrastructure Wi-Fi existant. Le but de ces méthodes de positionnement est de localiser le plus précisément possible l'emplacement du dispositif mobile utilisé. La première méthode de positionnement que nous proposons transforme la puissance du signal reçue en une entité appelée signature. Cette entité caractérise chaque emplacement de l'environnement où la localisation doit être effectuée. Pour localiser l'appareil mobile, la signature calculée est jumelée avec les signatures de référence les plus représentatives et qui sont déjà enregistrées dans une base de données. Dans ce mémoire, nous proposons deux approches pour produire les signatures de référence: une empirique et une théorique. La deuxième méthode de positionnement que nous proposons dans ce mémoire est de localiser les appareils mobiles en utilisant la différence entre les mesures de puissance de signaux reçus. On a appelé cette méthode la différence de puissances des signaux reçues (RSSD). Cette méthode consiste à convertir la différence de puissances des signaux reçues en des distances et d’utiliser ces distances pour estimer la position des appareils mobiles. Ensuite, nous décrivons les expériences qui nous ont conduits à développer la méthode de traitement du signal et les algorithmes de localisation. Les algorithmes et les méthodes proposés ont conduit à un système de localisation précis qui atteint 2 mètres de précision dans 90% des cas. Les résultats actuels des systèmes proposés montrent que les emplacements estimés sont précis (moins de 2 mètres) dans un environnement fermé en utilisant la méthode des signatures et une localisation précise dans les espaces ouverts en utilisant la méthode de la RSSD. Certains endroits critiques ont besoin de plus de collecte de données et plus d'informations sur l'environnement pour atteindre le même niveau de précision. Les résultats obtenus sont décrits et discutés à l’aide de cartes et de statistiques.----------ABSTRACT Several approaches have been developed to provide an accurate estimation of the position of mobile devices inside buildings. Some of them give a precision of less than one meter but they require special infrastructure and materials. Some others use an infrastructure that is already deployed but gives a position with lower precision. In this thesis, we propose several positioning methods based on the received signal strength (RSS) measurements of an existing Wi-Fi infrastructure. The aim of these positioning methods is to locate a mobile device as accurately as possible. The first method that we propose transforms the RSS to an entity called signature. This entity characterises each location of the environment where the localization should be performed. This computed signature is matched with the most representative reference signatures already recorded in a database in order to locate the mobile device. In this thesis, we propose two approaches to produce the reference signatures: an empirical and a theoretical one. The second method that we propose in this thesis is about locating the mobile devices using the difference between the received signals strength measurements. We call this method the received signal strength difference (RSSD) method. We then describe the experiments that led us to develop the signal processing method and the localization algorithms. The algorithm proposed led to an accurate localization system that reaches 2 meters of accuracy in 90% of the cases. Current results of the proposed systems show that the estimated locations are accurate (less than 2 meters) in closed environments when using the fingerprinting method and in open spaces when using the RSSD method. Some critical locations need more collected data and more information about the environment to reach the same level of accuracy. The results obtained are described and discussed using maps and statistics

Indoor Location-Based Services In The Telecommunications Network

‘Location-based services’ are services that take the location of mobile devices into account. Traditionally, these services have revolved around positioning and navigation. However, with the advent of smartphones equipped with GPS receivers, a number of innovative location-based services have come into the market and caught users’ interest. Users spend nearly 90% of their time indoors and GPS receivers do not function well within buildings. Hence, there is a need for a reliable indoor positioning system. Alongside technological research, a study of indoor location-based services is also necessary. ‘Open Telco’ refers to the endeavour by mobile operators to follow in the footsteps of internet companies and embrace open innovation and open APIs (Application Programming Interfaces). Network service exposure and the ecosystem approach are believed to be critical to the future success of mobile operators. This Thesis attempts to bring together these three dissimilar but related topics- indoor positioning, location-based services and network service exposure. This is done via the study of existing literature and the implementation of a service prototype

Investigation of indoor positioning based on WLAN 802.11

The need for location based services has dramatically increased within the past few years, especially with the popularity and capability of mobile device such as smart phones and tablets. The limitation of GPS for indoor positioning has seen an increase of indoor positioning based on Wireless Local Area Network 802.11.\ud This thesis reviews the various different techniques used by applications to determine one’s location through the measurement of Wi-Fi signals. It particularly focuses on the Cisco Context-Aware Mobility which provides a Real Time Location System solution based on Wi-Fi. It details the implementation of an Android application, developed to communicate with the Cisco Context-Aware Mobility to visually display the location of the mobile device. The application was tested in a production environment. Limitations in the production environment along with the diagnostic capabilities of the Context-Aware Mobility were identified

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

Optimizing Deployment and Maintenance of Indoor Localization Systems

Pervasive computing envisions the achievement of seamless and distraction-free support for tasks by means of context-aware applications. Context can be defined as the information which can be used to characterize the situation of an entity such as persons or objects which are relevant for the behaviour of an application. A context-aware application is one which can adapt its functionality based on changes in the context of the user or entity. Location is an important piece of context because a lot of information can be inferred about the situation of an entity just by knowing where it is. This makes location very useful for many context-aware applications. In outdoor scenarios, the Global Positioning System (GPS) is used for acquiring location information. However, GPS signals are relatively weak and do not penetrate buildings well, rendering them less than suitable for location estimation in indoor environments. However, people spend most of their time in indoor locations and therefore it is necessary to have location systems which would work in these scenarios. In the last two decades, there has been a lot of research into and development of indoor localization systems. A wide range of technologies have been applied in the development of these systems ranging from vision-based systems, sound-based systems as well as Radio Frequency (RF) signal based systems. In a typical indoor localization system deployment, an indoor environment is setup with different signal sources and then the distribution of the signals in the environment is recorded in a process known as calibration. The distribution of signals, also known as a radio map, is then later employed to estimate location of users by matching their signal observations to the radio map. However, not all the different signal technologies and approaches provide the right balance of accuracy, precision and cost to be suitable for most real world deployment scenarios. Of the different RF signal technologies, WLAN and Bluetooth based indoor localization systems are the most common due to the ubiquity of the signal deployments for communication purposes, and the accessibility of compatible mobile computing devices to the users of the system. Many of the indoor localization systems have been developed under laboratory conditions or only with small-scale controlled indoor areas taken into account. This poses a challenge when transposing these systems to real-world indoor environments which can be rather large and dynamic, thereby significantly raising the cost, effort and practicality of the deployment. Furthermore, due to the fact that indoor environments are rarely static, changes in the environment such as moving of furniture or changes in the building layout could adversely impact the performance of the localization system deployment. The system would then need to be recalibrated to the new environmental conditions in order to achieve and maintain optimal localization performance in the indoor environment. If this happens regularly, it can significantly increase the cost and effort for maintenance of the indoor localization system over time. In order to address these issues, this dissertation develops methods for more efficient deployment and maintenance of the indoor localization systems. A localization system deployment consists of three main phases; setup and calibration, localization and maintenance. The main contributions of this dissertation are proposed optimizations to the different stages of the localization system deployment lifecycle. First, the focus is on optimizing setup and calibration of fingerprinting-based indoor localization systems. A new method for dense and efficient calibration of the indoor environmental areas is proposed, with minimal effort and consequently reduced cost. During calibration, the signal distribution in the indoor environment is distorted by the presence of the person doing the calibration. This leads to a radio map which is not a very accurate representation of the environment. Therefore a model for WLAN signal attenuation by the human body is proposed in this dissertation. The model captures the pattern of change to the signal due the presence of the human body in the signal path. By applying the model, we can compensate for the attenuation caused by the person and thereby generate a more accurate map of the signal distribution in the environment. A more precise signal distribution leads to better precision during location estimation. Secondly, some optimizations to the localization phase are presented. The dense fingerprints of the environment created during the setup phase are used for generating location estimates by matching the captured signal distribution with the pre-recorded distribution in the environment. However, the location estimates can be further refined given additional context information. This approach makes use of sensor fusion and ambient intelligence in order to improve the accuracy of the location estimates. The ambient intelligence can be gotten from smart environments such as smart homes or offices, which trigger events that can be applied to location estimation. These optimizations are especially useful for indoor tracking applications where continuous location estimation and accurate high frequency location updates are critical. Lastly, two methods for autonomous recalibration of localization systems are presented as optimizations to the maintenance phase of the deployment. One approach is based on using the localization system infrastructure to monitor the signal characteristic distribution in the environment. The results from the monitoring are used by the system to recalibrate the signal distribution map as needed. The second approach evaluates the Received Signal Strength Indicator (RSSI) of the signals as measured by the devices using the localization system. An algorithm for detecting signal displacements and changes in the distribution is proposed, as well as an approach for subsequently applying the measurements to update the radio map. By constantly self-evaluating and recalibrating the system, it is possible to maintain the system over time by limiting the degradation of the localization performance. It is demonstrated that the proposed approach achieves results comparable to those obtained by manual calibration of the system. The above optimizations to the different stages of the localization deployment lifecycle serve to reduce the effort and cost of running the system while increasing the accuracy and reliability. These optimizations can be applied individually or together depending on the scenario and the localization system considered

Indoor localization using multiple wireless technologies

Indoor localization techniques using location fingerprints are gaining popularity because of their cost-effectiveness compared to other infrastructure-based location systems. However, their reported accuracy fall short of their counterparts. In this paper, we investigate many aspects of fingerprint-based location systems in order to enhance their accuracy. First, we derive analytically a robust location fingerprint definition, and then verify it experimentally as well. We also devise a way to facilitate under-trained location systems through simple linear regression technique. This technique reduces the training time and effort, and can be particularly useful when the surrounding or setup of the localization area changes. We further show experimentally that because of the positions of some access points or the environmental factors around them, their signal strength correlates nicely with distance. We argue that it would be more beneficial to give special consideration to these access points for location computation, owing to their ability to distinguish locations distinctly in signal space. The probability of encountering such access points will be even higher when we denote a location's signature using the signals of multiple wireless technologies collectively. We present the results of two well- known localization algorithms (K-Nearest Neighbor and Bayesian Probabilistic Model) when the above factors are exploited, using Bluetooth and Wi-Fi signals. We have observed significant improvement in their accuracy when our ideas are implemented