Geometry-based localization for GPS outage in vehicular cyber physical systems

Vehicular localization has witnessed significant attention due to the growing number of location-based services in vehicular cyber physical systems (VCPS). In vehicular localization, GPS outage is a challenging issue considering the growing urbanization including high rise buildings, multilevel flyovers and bridges. GPS-free and GPS-assisted cooperative localization techniques have been suggested in the literature for GPS outage. Due to the cost of infrastructure in GPS-free techniques, and the absence of location aware neighbors in cooperative techniques, efficient and scalable localization is a challenging task in VCPS. In this context, this paper proposes a geometry-based localization for GPS outage in VCPS (GeoLV). It is a GPS-assisted localization which reduces location-aware neighbor constraint of cooperative localization. GeoLV utilizes mathematical geometry to estimate vehicle location focusing on vehicular dynamics and road trajectory. The static and dynamic relocations are performed to reduce the impact of GPS outage on location-based services. A case study based comparative performance evaluation has been carried out to assess the efficiency and scalability of GeoLV. It is evident from the results that GeoLV handles both shorter and longer GPS outage problem better than the state-of-the-art techniques in VCPS

Heuristics for spatial finding using iterative mobile crowdsourcing

Crowdsourcing has become a popular method for involving humans in socially-aware computational processes. This paper proposes and investigates algorithms for finding regions of interest using mobile crowdsourcing. The algorithms are iterative, using cycles of crowd-querying and feedback till specified targets are found, each time adjusting the query according to the feedback using heuristics. We describe three (computationally simple) heuristics, incorporated into crowdsourcing algorithms, to reducing the costs (the number of questions required) and increasing the efficiency (or reducing the number of rounds required) in using such crowdsourcing: (i) using additional questions in each round in the expectation of failures, (ii) using neighbourhood associations in the case where regions of interest are clustered, and (iii) modelling regions of interest via spatial point processes. We demonstrate the improved performance of using these heuristics using a range of stylised scenarios. Our research suggests that finding in the city is not as difficult as it can be, especially for phenomena that exhibit some degree of clustering

Análise de comportamento do usuário em redes sociais veiculares

Participation in social networks can provide a significant amount of data about users and their surroundings. When properly processed, such data can be used as an important source of information on human behavior if it provides reliable and quality information. In this work, we use a vehicular social network with the main objective of evaluating the impact of external factors on the users present in these environments through their contributions. We can see how speed of user and delay influence the reliability attributed to alerts. It also studies the tendency of improvement or degradation of the reliability attributed to the alerts of each user. It is possible to observe the association between pairs of alerts that occur on the same street in short intervals of time. We verify the interval of consecutive contributions of each user and the ratio of time interval between the first and last contribution and its total number of contributions. Results were obtained through a public dataset of the Waze application, available on the Internet. It was discovered that the most posted alerts are about congestion, and that users mostly do it during peak hours on weekdays and on weekends on the afternoon. It was found that users who move at higher speeds do not contribute to the network, and postings that present the longest delays to be published on the network are poorly evaluated. In addition, there was also a significant association between climate risk alerts and congestion. As the main result, it turned out that users who receive low reliability in their posts tend to keep score low on the following posts. Finally, it was also possible to notice that the interval between the contributions of each user has an average of 10 minutes and are not made daily to the social network, but when they do, the time interval between the alerts has a linear growth.A participação em redes sociais pode fornecer significativa quantidade de dados sobre usuários e o ambiente que os cerca. Quando adequadamente processados, esses dados podem ser usados como uma importante fonte de informação sobre o comportamento humano, se oferecerem informações confiáveis e de qualidade. Neste trabalho, usamos uma rede social veicular com o principal objetivo de avaliar o impacto de fatores externos sobre os usuários presentes nesses ambientes através de suas contribuições na rede. Verifica-se como a velocidade do usuário e o atraso da publicação influenciam na confiabilidade atribuída aos alertas. Estuda-se a tendência de melhora ou degradação da confiabilidade de cada usuário. Observa-se a associação entre pares de alerta que ocorrem em uma mesma rua. Verifica-se também o intervalo de contribuições consecutivas de cada usuário e a relação de intervalo de tempo entre sua primeira e a última contribuição. Os resultados foram obtidos através de um conjunto de dados público do aplicativo Waze, disponibilizado na Internet. Foi descoberto que os alertas mais postados são sobre congestionamentos, e que usuários o fazem principalmente nas horas de pico em dias úteis e durante a tarde nos fins de semana. Percebeu-se que os usuários que se movem em velocidades mais elevadas não contribuem para a rede e postagens que apresentam maiores atrasos para serem publicadas na rede são mal avaliadas. Além disso, percebeu-se também significativa associação entre alertas de risco climático e congestionamento. Por fim, foi possível notar que o intervalo entre as contribuições de cada usuário tem uma média de 10 minutos e não são feitas diariamente a rede social, mas quando o fazem, o intervalo de tempo entre os alertas possui um crescimento linear

Estudio de entornos de simulación en redes de vehículos

La última década ha sido testigo de un creciente interés en las VANETs (Vehicular Ad-Hoc Networks), redes formadas entre diferentes vehículos que intercambian datos utilizando tecnologías de comunicación de corto alcance, intentando ofrecer al conductor información dinámica que pueda resultarle de utilidad, brindándole seguridad y evitando riesgos potenciales al proporcionar un mayor conocimiento de las condiciones del camino. Se han propuesto diversos sistemas de intercambio y gestión de datos para redes de vehículos. Sin embargo, para realizar pruebas de su funcionamiento es conveniente utilizar simuladores del entorno real. De esta manera los costes serían menores, se garantizaría la obtención de resultados fiables y un mayor rendimiento del producto final. El objetivo de este trabajo es realizar un estudio de los simuladores más utilizados en el ámbito de las redes de vehículos, con el fin de contribuir a la comunidad investigadora en el momento de decidir qué simulador utilizar. Para ello se emplearán artículos realizados por diferentes investigadores y distintos software de simulación que permitan determinar características, ventajas, desventajas, actividad en la comunidad científica y otros criterios de evaluación que nos ayuden a la toma de decisiones en el momento de utilizar algún simulador específico. A partir de este punto se seleccionarán los que se consideren más adecuados para realizar simulaciones de diversos tipos, incluyendo escenarios de ciudades reales, así como ejemplos propios de los simuladores, donde se darán directrices de cómo llevar cabo la simulación y qué análisis se puede realizar con los resultados obtenidos

On the use of smartphones as novel photogrammetric water gauging instruments: Developing tools for crowdsourcing water levels

The term global climate change is omnipresent since the beginning of the last decade. Changes in the global climate are associated with an increase in heavy rainfalls that can cause nearly unpredictable flash floods. Consequently, spatio-temporally high-resolution monitoring of rivers becomes increasingly important. Water gauging stations continuously and precisely measure water levels. However, they are rather expensive in purchase and maintenance and are preferably installed at water bodies relevant for water management. Small-scale catchments remain often ungauged. In order to increase the data density of hydrometric monitoring networks and thus to improve the prediction quality of flood events, new, flexible and cost-effective water level measurement technologies are required. They should be oriented towards the accuracy requirements of conventional measurement systems and facilitate the observation of water levels at virtually any time, even at the smallest rivers. A possible solution is the development of a photogrammetric smartphone application (app) for crowdsourcing water levels, which merely requires voluntary users to take pictures of a river section to determine the water level. Today’s smartphones integrate high-resolution cameras, a variety of sensors, powerful processors, and mass storage. However, they are designed for the mass market and use low-cost hardware that cannot comply with the quality of geodetic measurement technology. In order to investigate the potential for mobile measurement applications, research was conducted on the smartphone as a photogrammetric measurement instrument as part of the doctoral project. The studies deal with the geometric stability of smartphone cameras regarding device-internal temperature changes and with the accuracy potential of rotation parameters measured with smartphone sensors. The results show a high, temperature-related variability of the interior orientation parameters, which is why the calibration of the camera should be carried out during the immediate measurement. The results of the sensor investigations show considerable inaccuracies when measuring rotation parameters, especially the compass angle (errors up to 90° were observed). The same applies to position parameters measured by global navigation satellite system (GNSS) receivers built into smartphones. According to the literature, positional accuracies of about 5 m are possible in best conditions. Otherwise, errors of several 10 m are to be expected. As a result, direct georeferencing of image measurements using current smartphone technology should be discouraged. In consideration of the results, the water gauging app Open Water Levels (OWL) was developed, whose methodological development and implementation constituted the core of the thesis project. OWL enables the flexible measurement of water levels via crowdsourcing without requiring additional equipment or being limited to specific river sections. Data acquisition and processing take place directly in the field, so that the water level information is immediately available. In practice, the user captures a short time-lapse sequence of a river bank with OWL, which is used to calculate a spatio-temporal texture that enables the detection of the water line. In order to translate the image measurement into 3D object space, a synthetic, photo-realistic image of the situation is created from existing 3D data of the river section to be investigated. Necessary approximations of the image orientation parameters are measured by smartphone sensors and GNSS. The assignment of camera image and synthetic image allows for the determination of the interior and exterior orientation parameters by means of space resection and finally the transfer of the image-measured 2D water line into the 3D object space to derive the prevalent water level in the reference system of the 3D data. In comparison with conventionally measured water levels, OWL reveals an accuracy potential of 2 cm on average, provided that synthetic image and camera image exhibit consistent image contents and that the water line can be reliably detected. In the present dissertation, related geometric and radiometric problems are comprehensively discussed. Furthermore, possible solutions, based on advancing developments in smartphone technology and image processing as well as the increasing availability of 3D reference data, are presented in the synthesis of the work. The app Open Water Levels, which is currently available as a beta version and has been tested on selected devices, provides a basis, which, with continuous further development, aims to achieve a final release for crowdsourcing water levels towards the establishment of new and the expansion of existing monitoring networks.Der Begriff des globalen Klimawandels ist seit Beginn des letzten Jahrzehnts allgegenwärtig. Die Veränderung des Weltklimas ist mit einer Zunahme von Starkregenereignissen verbunden, die nahezu unvorhersehbare Sturzfluten verursachen können. Folglich gewinnt die raumzeitlich hochaufgelöste Überwachung von Fließgewässern zunehmend an Bedeutung. Pegelmessstationen erfassen kontinuierlich und präzise Wasserstände, sind jedoch in Anschaffung und Wartung sehr teuer und werden vorzugsweise an wasserwirtschaftlich-relevanten Gewässern installiert. Kleinere Gewässer bleiben häufig unbeobachtet. Um die Datendichte hydrometrischer Messnetze zu erhöhen und somit die Vorhersagequalität von Hochwasserereignissen zu verbessern, sind neue, kostengünstige und flexibel einsetzbare Wasserstandsmesstechnologien erforderlich. Diese sollten sich an den Genauigkeitsanforderungen konventioneller Messsysteme orientieren und die Beobachtung von Wasserständen zu praktisch jedem Zeitpunkt, selbst an den kleinsten Flüssen, ermöglichen. Ein Lösungsvorschlag ist die Entwicklung einer photogrammetrischen Smartphone-Anwendung (App) zum Crowdsourcing von Wasserständen mit welcher freiwillige Nutzer lediglich Bilder eines Flussabschnitts aufnehmen müssen, um daraus den Wasserstand zu bestimmen. Heutige Smartphones integrieren hochauflösende Kameras, eine Vielzahl von Sensoren, leistungsfähige Prozessoren und Massenspeicher. Sie sind jedoch für den Massenmarkt konzipiert und verwenden kostengünstige Hardware, die nicht der Qualität geodätischer Messtechnik entsprechen kann. Um das Einsatzpotential in mobilen Messanwendungen zu eruieren, sind Untersuchungen zum Smartphone als photogrammetrisches Messinstrument im Rahmen des Promotionsprojekts durchgeführt worden. Die Studien befassen sich mit der geometrischen Stabilität von Smartphone-Kameras bezüglich geräteinterner Temperaturänderungen und mit dem Genauigkeitspotential von mit Smartphone-Sensoren gemessenen Rotationsparametern. Die Ergebnisse zeigen eine starke, temperaturbedingte Variabilität der inneren Orientierungsparameter, weshalb die Kalibrierung der Kamera zum unmittelbaren Messzeitpunkt erfolgen sollte. Die Ergebnisse der Sensoruntersuchungen zeigen große Ungenauigkeiten bei der Messung der Rotationsparameter, insbesondere des Kompasswinkels (Fehler von bis zu 90° festgestellt). Selbiges gilt auch für Positionsparameter, gemessen durch in Smartphones eingebaute Empfänger für Signale globaler Navigationssatellitensysteme (GNSS). Wie aus der Literatur zu entnehmen ist, lassen sich unter besten Bedingungen Lagegenauigkeiten von etwa 5 m erreichen. Abseits davon sind Fehler von mehreren 10 m zu erwarten. Infolgedessen ist von einer direkten Georeferenzierung von Bildmessungen mittels aktueller Smartphone-Technologie abzusehen. Unter Berücksichtigung der gewonnenen Erkenntnisse wurde die Pegel-App Open Water Levels (OWL) entwickelt, deren methodische Entwicklung und Implementierung den Kern der Arbeit bildete. OWL ermöglicht die flexible Messung von Wasserständen via Crowdsourcing, ohne dabei zusätzliche Ausrüstung zu verlangen oder auf spezifische Flussabschnitte beschränkt zu sein. Datenaufnahme und Verarbeitung erfolgen direkt im Feld, so dass die Pegelinformationen sofort verfügbar sind. Praktisch nimmt der Anwender mit OWL eine kurze Zeitraffersequenz eines Flussufers auf, die zur Berechnung einer Raum-Zeit-Textur dient und die Erkennung der Wasserlinie ermöglicht. Zur Übersetzung der Bildmessung in den 3D-Objektraum wird aus vorhandenen 3D-Daten des zu untersuchenden Flussabschnittes ein synthetisches, photorealistisches Abbild der Aufnahmesituation erstellt. Erforderliche Näherungen der Bildorientierungsparameter werden von Smartphone-Sensoren und GNSS gemessen. Die Zuordnung von Kamerabild und synthetischem Bild erlaubt die Bestimmung der inneren und äußeren Orientierungsparameter mittels räumlichen Rückwärtsschnitt. Nach Rekonstruktion der Aufnahmesituation lässt sich die im Bild gemessene 2D-Wasserlinie in den 3D-Objektraum projizieren und der vorherrschende Wasserstand im Referenzsystem der 3D-Daten ableiten. Im Soll-Ist-Vergleich mit konventionell gemessenen Pegeldaten zeigt OWL ein erreichbares Genauigkeitspotential von durchschnittlich 2 cm, insofern synthetisches und reales Kamerabild einen möglichst konsistenten Bildinhalt aufweisen und die Wasserlinie zuverlässig detektiert werden kann. In der vorliegenden Dissertation werden damit verbundene geometrische und radiometrische Probleme ausführlich diskutiert sowie Lösungsansätze, auf der Basis fortschreitender Entwicklungen von Smartphone-Technologie und Bildverarbeitung sowie der zunehmenden Verfügbarkeit von 3D-Referenzdaten, in der Synthese der Arbeit vorgestellt. Mit der gegenwärtig als Betaversion vorliegenden und auf ausgewählten Geräten getesteten App Open Water Levels wurde eine Basis geschaffen, die mit kontinuierlicher Weiterentwicklung eine finale Freigabe für das Crowdsourcing von Wasserständen und damit den Aufbau neuer und die Erweiterung bestehender Monitoring-Netzwerke anstrebt