The principal aim of this thesis is to assess the state of the mobile marketing as of the first quarter of 2011 and to discuss various scenarios of the future development. This thesis defines the terms "mobile marketing" and "mobile advertising" and identifies the main players in the industry. It explores the main categories of mobile advertising such as mobile messaging, in-content and mobile internet advertising. Later, it analyzes the latest trends in the industry and describes in detail the advancements in QR tagging, location based advertising and augmented reality.

Depuis quelques dizaines d’années, l’utilisation de téléphones contenant un capteur GPS a fortement augmenté. Cependant, tous ces usages ne sont pas sans menace pour la vie privée des utilisateurs. En effet, les données de mobilité qu’ils envoient à ces services peuvent être utilisées pour inférer des informations sensibles telles que leur domicile ou leur lieu de travail. C’est à ce moment qu’entrent en action les mécanismes de protection, visant à redonner aux utilisateurs le contrôle sur leur vie privée. Nous commençons par répertorier les mécanismes de protection existants et les métriques utilisées pour les évaluer. Cette première analyse met en avant une information particulièrement sensible : les points d’intérêt. Ces derniers représentent tous les lieux où les utilisateurs passent la majeure partie de leur temps. Cela nous conduit à proposer un nouveau mécanisme de protection, PROMESSE, dont le but principal est de cacher ces points d’intérêt. Les mécanismes de protection sont en général configurés par des paramètres, qui ont un grand impact sur leur efficacité. Nous proposons ALP, une solution destinée à aider les utilisateurs à configurer leurs mécanismes de protection à partir d’objectifs qu’ils ont spécifié. Enfin, nous présentons Accio, un logiciel regroupant la majeure partie du travail de cette thèse. Il permet de lancer facilement des expériences destinées à étudier des mécanismes de protection, tout en renforçant leur reproductibilité. / In the past decades, the usage of GPS-enabled smartphones has dramatically risen. However, all these usages do not come without privacy threats. Indeed, location data that users are sending to these services can be used to infer sensitive knowledge about them, such as where they live or where they work. This is were protection mechanisms come into play, whose goal is to put users back in control of their privacy. We start by surveying existing protection mechanisms and metrics used to evaluate them. This first analysis highlights a particularly sensitive information, namely the points of interest. These are all the places where users use to spend most of their time. This leads us towards building a new protection mechanism, PROMESSE, whose main goal is to hide these points of interest. Protection mechanisms tend to be configured by parameters, which highly impact their effectiveness in terms of privacy and utility. We propose ALP, a solution to help users to configure their protection mechanisms from a set of objectives they specified. Finally, we introduce Accio, which is a software encompassing most of our work. Its goal is to allow to easily launch location privacy experiments and enforce their reproducibility.

Infrastruktursatsningar är en stor del av den regionala utvecklingen i Sverige och forskare i ämnet är eniga om att satsningar på infrastruktur genererar en ökad ekonomisk utveckling. Något de fortfarande är oeniga om, är vilka metoder som är lämpliga att använda för att räkna ut effekten av den ekonomiska vinsten. Finns det några som gynnas mer än andra eller till och med missgynnas av dessa infrastrukturprojekt, hur ser det till exempel ut på landsbygden i de samhällen där större vägar tidigare passerat. Har dessa samhällen gynnats av att ha snabbare och enklare pendling till staden, eller har de istället drabbats negativt när motorvägen, som en del livnärde sig på, fått en ny sträcka. Denna forskningsrapport fördjupar sig i en av de största infrastruktursatsningarna i norra Sverige, nämligen ombyggnationen av europaväg 4 (E4) genom Sundsvall. Projektet som pågick mellan år 2010 och 2015 innebar att sträckan från Njurunda i söder till Skönsberg i norr byggdes om samt att Sveriges fjärde längsta bro anlades över Sundsvallsfjärden. Studien har med hjälp av ortsprismetoden och ortsanalyser utvisat vilken värdeförändring som skett på fastigheter taxerade som småhus, vid detta infrastrukturprojekt. Resultatet av dessa studier visar på den genomsnittliga förändringen av fastighetspriser i områdena Njurundabommen, Nedre Haga/Skönsberg, Nolby/Kvissleby samt Västbo. Data som låg till grund för resultatet visar delvis för få transaktioner, vilket är vanligt vid försäljningsanalyser på ortsnivå. Det genererar i sin tur en osäkerhet i resultatet, vilket medför att förändringarna kan bli stora i förhållande till den verkliga försäljningsutvecklingen. I denna studie minimerades detta genom att två likställda områden slogs ihop samt ett område fick tas bort, vilket slutligen gav ett resultat som indikerar på att områdena i denna studie haft en mer negativ prisutveckling mot Sundsvall och Sverige, sedan området fått en ny infrastruktur i form av E4:an Sundsvall. / Infrastructure investments is a large part of the regional development in Sweden, researchers in the subject agrees that investment in infrastructure generate increased economic development, one thing they still share a disagreement in which calculating methods are suitable. Are there any winners or losers in infrastructure projects, for example, smaller cities in the countryside where highways have previously passed, have they benefited from a quicker and easier commuting to the city, or have they been adversely affected when the highway has been given a new stretch? This research report focuses in one of the largest infrastructure investments in northern Sweden, European road 4 (E4) through Sundsvall. The research, combined with location-based sales comparison method and local analyzes, will show what effect market values will have on the houses real estate’s and what influence the road construction might have regarding to the values of the properties. The results of these studies are shown trough the average changes in property prices in the areas of Njurundabommen, Nedre Haga/Skönsberg, Nolby/Kvissleby and Västbo. However, the result was shown to have too few transactions, which is common in local level sales analyzes. This generates uncertainty in the result, meaning that the values can be large in relation to the actual sales development. The uncertainty was minimized in the results by merging two similar areas, which finally gave a result that indicates that the areas in the study had a more negative development towards Sundsvall and Sweden, since the area received a new infrastructure in the form of the E4 Sundsvall.

Online social networks (OSNs) are Web platforms providing different services to facilitate social interaction among their users. A particular kind of OSNs is the location-based social network (LBSN), which adds services based on location. One of the most important challenges in LBSNs is the link prediction problem. Link prediction problem aims to estimate the likelihood of the existence of future friendships among user pairs. Most of the existing studies in link prediction focus on the use of a single information source to perform predictions, i.e. only social information (e.g. social neighborhood) or only location information (e.g. common visited places). However, some researches have shown that the combination of different information sources can lead to more accurate predictions. In this sense, in this thesis we propose different link prediction methods based on the use of different information sources naturally existing in these networks. Thus, we propose seven new link prediction methods using the information related to user membership in social overlapping groups: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA) and group naïve Bayes of Resource Allocation (GNB-RA). Due to that social groups exist naturally in networks, our proposals can be used in any type of OSN.We also propose new eight link prediction methods combining location and social information: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP) and Nearby Distance Allocation (NDA). These eight methods are exclusively for work in LBSNs. Obtained results indicate that our proposals are as competitive as state-of-the-art methods, or better than they in certain scenarios. Moreover, since our proposals tend to be computationally more efficient, they are more suitable for real-world applications. / Redes sociais online (OSNs) são plataformas Web que oferecem serviços para promoção da interação social entre usuários. OSNs que adicionam serviços relacionados à geolocalização são chamadas redes sociais baseadas em localização (LBSNs). Um dos maiores desafios na análise de LBSNs é a predição de links. A predição de links refere-se ao problema de estimar a probabilidade de conexão futura entre pares de usuários que não se conhecem. Grande parte das pesquisas que focam nesse problema exploram o uso, de maneira isolada, de informações sociais (e.g. amigos em comum) ou de localização (e.g. locais comuns visitados). Porém, algumas pesquisas mostraram que a combinação de diferentes fontes de informação pode influenciar o incremento da acurácia da predição. Motivado por essa lacuna, neste trabalho foram desenvolvidos diferentes métodos para predição de links combinando diferentes fontes de informação. Assim, propomos sete métodos que usam a informação relacionada à participação simultânea de usuários en múltiples grupos sociais: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA), e group naïve Bayes of Resource Allocation (GNB-RA). Devido ao fato que a presença de grupos sociais não está restrita a alguns tipo de redes, essas propostas podem ser usadas nas diversas OSNs existentes, incluindo LBSNs. Também, propomos oito métodos que combinam o uso de informações sociais e de localização: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP), e Nearby Distance Allocation (NDA). Tais propostas são para uso exclusivo em LBSNs. Os resultados obtidos indicam que nossas propostas são tão competitivas quanto métodos do estado da arte, podendo até superá-los em determinados cenários. Ainda mais, devido a que na maioria dos casos nossas propostas são computacionalmente mais eficientes, seu uso resulta mais adequado em aplicações do mundo real.

Les fournisseurs de services géo-localisés (LBS) offrent des données textuelles et spatiales complémentaires, parfois incohérentes et imprécises, représentant les différents points d’intérêt (POI) sur un territoire donné. Ainsi, une même requête lancée auprès de divers fournisseurs de services touristiques peut donner des résultats différents et parfois incohérents, pour les attributs terminologiques et/ou les attributs spatiaux. De plus, chaque fournisseur utilise sa propre convention graphique pour représenter les POIs. L’intégration de ces données spatiales hétérogènes dans un contexte dynamique, large échelle, utilisant des sources incomplètes et de qualités variables est actuellement un verrou technologique. Dans ce travail de thèse, nous cherchons une solution à cette intégration aussi bien au niveau des données que de leur représentation. / Location Based Services (LBS) had been involved to deliver relevant geospatial information based on a geographic position or address. The amount of geospatial data is constantly increasing, making it a valuable source of information for enriching LBS applications. However, these geospatial data are highly inconsistent and contradictory from one source to another. We assume that integrating geospatial data from several sources may improve the quality of information offered to users. In this thesis, we specifically focus on data representing Points of Interest (POIs) that tourists can get through LBS. Retrieving, matching and merging such geospatial entities lead to several challenges. We mainly focus on three main challenges including (i) detecting and merging corresponding entities across multiple sources and (ii) considering the uncertainty of integrated entities and their representation in LBS applications.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-16T17:52:50Z No. of bitstreams: 1 RodrigoFeitosa.pdf: 4009932 bytes, checksum: 55ef5e97ddc653cf1849e17eafdc396f (MD5) / Made available in DSpace on 2017-08-16T17:52:50Z (GMT). No. of bitstreams: 1 RodrigoFeitosa.pdf: 4009932 bytes, checksum: 55ef5e97ddc653cf1849e17eafdc396f (MD5) Previous issue date: 2013-03-22 / The search of knowledge and its manipulation in companies, institutions or other organizations has become a challenge nowadays. Mostly due to two aspects: the large volume of information available and the difficulty in extracting the knowledge proper to each person (intellectual capital). This difficulty becomes more accentuated when the scenario involved the extraction of knowledge is the Web. The area of Knowledge Management seeks a solution to the limitations described above. Techniques for extracting and control of knowledge can be adopted with the use of Artificial Intelligence, particularly the Knowledge Discovery in Databases. This work proposes the creation of a methodology and application that perform the Data Mining with textual information linked to geo data in a social network, in order to promote Social Recommendation. However, approaches in building recommendation systems present some shortcomings in filtering the results and the way they are suggested to users. The research aims to remedy these deficiencies and addresses issues that still need to search more effective and consolidated results. / A busca do conhecimento e a sua manipulação em empresas, instituições ou outras organizações tem se tornado um desafio nos dias atuais. Em grande parte devido a dois aspectos: o grande volume de informação disponibilizada e a dificuldade em extrair o conhecimento próprio de cada pessoa (capital intelectual). Essa dificuldade torna-se mais acentuada quando o cenário envolvido para a extração de conhecimento é a Web. A área da Gestão de Conhecimento busca a solução para as limitações descritas anteriormente. Técnicas para a extração e controle do conhecimento podem ser adotadas com o uso da Inteligência Artificial, sobretudo a Descoberta de Conhecimento em Bases de Dados. Este trabalho propõe-se a criação de uma metodologia e aplicação que realize a Mineração de Dados com informações textuais vinculados a dados geolocalizados em uma Rede Social, com o intuito de promover a Recomendação Social. Entretanto, as abordagens na construção dos Sistemas de Recomendação apresentam algumas deficiências na filtragem dos resultados e na forma que estes são sugeridos aos usuários. A pesquisa busca a solução destas deficiências e aborda temas que ainda carecem de pesquisas mais efetivas e resultados consolidados.

The mobile and wireless community has brought a significant growth of location-aware devices including smart phones, connected vehicles and IoT devices. The combination of location-aware sensing, data processing and wireless communication in these devices leads to the rapid development of mobile and location-aware applications. Meanwhile, user privacy is becoming an indispensable concern. These mobile and location-aware applications, which collect data from mobile sensors carried by users or vehicles, return valuable data collection services (e.g., health condition monitoring, traffic monitoring, and natural disaster forecasting) in real time. The sequential spatial-temporal data queries sent by users provide their location trajectory information. The location trajectory information not only contains users’ movement patterns, but also reveals sensitive attributes such as users’ personal habits, preferences, as well as home and work addresses. By exploring this type of information, the attackers can extract and sell user profile data, decrease subscribed data services, and even jeopardize personal safety. This research spans from the realization that user privacy is lost along with the popular usage of emerging location-aware applications. The outcome seeks to relive user location and trajectory privacy problems. First, we develop a pseudonym-based anonymity zone generation scheme against a strong adversary model in continuous location-based services. Based on a geometric transformation algorithm, this scheme generates distributed anonymity zones with personalized privacy parameters to conceal users’ real location trajectories. Second, based on the historical query data analysis, we introduce a query-feature-based probabilistic inference attack, and propose query-aware randomized algorithms to preserve user privacy by distorting the probabilistic inference conducted by attackers. Finally, we develop a privacy-aware mobile sensing mechanism to help vehicular users reduce the number of queries to be sent to the adversarial servers. In this mechanism, mobile vehicular users can selectively query nearby nodes in a peer-to-peer way for privacy protection in vehicular networks.

Although air pollution is one of the largest threats to human health, the data available to the public is often sparse and not very accurate nor updated. For example, there exists only about 5-10 air quality measuring points across the city of Stockholm. This means that the available data is good in close proximity of the sensing equipment but can differentiate much only a couple of blocks away. In order for individuals to receive up to date information around a larger city, stationary measurements are not sufficient enough to get a clear picture of how the current state of the air quality stands. Instead, other methods of collecting this data is needed, for instance by making the measurements mobile. GOEASY is a project financed by the European Commission where Galileo, Europe’s new navigational service, is used to enable more location-based service applications. As part of the GOEASY project is the evaluation of the potential of collaborative applications where users are engaged to help individuals affected by breathing-related diseases such as asthma. This thesis presents the choice of architecture and the implementation of a mobile platform serving this purpose. Using sensors mounted on a range of objects real time air quality data is collected and made available. The result is a mobile platform and connected Android application which by utilizing air quality sensors, reports pollution measurements together with positional coordinates to a central server. Thanks to the features of the underlying systems used, this provides a platform which is accurate and more resilient to exploits compared to traditional location-based services available today. The result allows individuals with respiratory conditions to receive much more accurate and up to date information in a larger resolution. It also serves the purpose of demonstrating the potential of the supporting technology as part of the GOEASY project. / Trots att föroreningar i luften är bland de största hoten mot mänsklig hälsa är den information som finns tillgänglig för allmänheten ofta både gles och inte tillräckligt noggrann eller uppdaterad. Till exempel finns det i hela Storstockholm endast mellan 5–10 luftkvalitetstationer som mäter föroreningar. Detta innebär att den data som finns tillgänglig är bra i närheten av mätutrustningen men kan skilja sig mycket enbart ett par kvarter bort. För att öka mängden information som är tillgänglig till allmänheten räcker inte längre enbart de stationära lösningarna som finns idag för att visa hur de rådande halterna av föroreningar står sig. Andra metoder måste införas, exempelvis genom att nyttja mobila mätningar från en plattform som kan röra sig fritt. GOEASY är ett projekt finansierat av den Europeiska Kommissionen, där Galileo, Europas nya navigationssystem används för att tillåta fler platsbaserade tjänster att äntra marknaden. Som en del av GOEASY projektet ingår evalueringen av potentialen i en applikation där användare samlar in data för att hjälpa individer med andningssvårigheter som astma. Denna avhandling presenterar valen till arkitekturen samt implementationen av en mobil plattform som en del av GOEASY. Lösningen använder sig av mobila luftkvalitetsensorer som kan monteras på en rad olika objekt som samlar data i realtid som görs tillgänglig för allmänheten. Resultatet är en mobil plattform och tillhörande Android applikation som med hjälp av luftkvalitetsensorer rapporterar halten av olika skadliga föroreningar tillsammans med platsinformation till en central server. Tack vare egenskaperna av de underliggande systemen som används, skapas en plattform som är mycket mer precis när det gäller positionering jämfört med liknande system som finns tillgängligt. Det resulterande systemet gör det möjligt för individer med andningssvårigheter att få tillgång till noggrannare samt mer uppdaterad information i större utsträckning än vad som för närvarande är tillgängligt. Systemet fyller även syftet med att demonstrera potentialen i den bakomliggande teknologin som en del av GOEASY.

Zipf’s law is one of the empirical statistical regularities found within many natural systems, ranging from protein sequences of immune receptors in cells to the intensity of solar flares from the sun. Verifying the universality of Zipf’s law can provide many opportunities for us to further seek the commonalities of phenomena that possess the power law behavior. Since power law-like phenomena, as many studies have previously indicated, is often interpreted as evidence for studying complex systems, exploring the universality of Zipf’s law is also of potential capability in explaining underlying generative mechanisms and endogenous processes, i.e. self-organization and chaos theory. The main purpose of this study was to verify whether Zipf’s law is valid for city sizes, city numbers and population extracted from natural cities. Unlike traditional city boundaries extracted by applying census-imposed and top-down imposed data, which are arbitrary and subjective, the study established the new kind of boundaries of cities, namely, natural cities through using four location-based social media data from Twitter, Brightkite, Gowalla and Freebase and head/tail breaks rule. In order to capture and quantify the hierarchical level for studying heterogeneous scales of cities, ht-index derived from head/tail breaks rule was employed. Furthermore, the validation of Zipf’s law was examined. The result revealed that the natural cities had deviations in subtle patterns when different social media data were examined. By employing head/tail breaks method, the result calculated the ht-index and detected that hierarchy levels were not largely influenced by spatial-temporal changes but rather data itself. On the other hand, the study found that Zipf’s law is not universal in the case of using location-based social media data. Compared to city numbers extracted from nightlight imagery, the study found out the reason why Zipf’s law does not hold for location-based social media data, i.e. due to bias of customer behavior. The bias mainly resulted in the emergence of natural cities were much more frequent than others in certain regions and countries so that making the emergence of natural cities was not exhibited objectively. Furthermore, the study showed whether Zipf’s law could be well observed depends not only on the data itself and man-made limitations but also on calculation methods, data precisions and scales and the idealized status of observed data.

人智運算(Human Computation)是近年來最熱門的研究領域之一，而適地性服務(Location-based service)也因此衍生出許多研究議題。由於傳統上在搜集資訊時往往會浪費過多的人力資源，所以我們希望使用者藉由玩遊戲的方式背後完成我們想要的事情。在此篇論文中，我們利用GWAP(Games with a Purpose)的概念設計一套行動地理標籤系統，系統採用玩家分享自身附近景點資訊再透過其他玩家來到相同地點做驗證的方式，讓玩家在一邊玩遊戲的過程中搜集相關地理資訊，另一方面達到景點資訊是可信賴的目的。 然而，對系統而言要如何選題給玩家驗證才能提升整體效能，為了解決這些問題，我們在選題策略上提出三種不同的任務分配演算法，再透過一連串的實驗模擬來證明我們所設計的系統確實能夠在選題上達到好的效能，最後我們考慮到現實生活中可能的情形後將此系統實作在智慧型手機上(Android Phone)。 / Human Computation is popular recently and have become one of the hottest research topics, therefore Location-based service also drives a lot of issues. Due to collect information always cause too many manpower- wasted in tradition , so we hoped that the users to complete the things which we want by play game’s way .In this thesis, we based on the concept of ‘Games with a Purpose’(GWAP) to develop a mobile geospatial tagging system, system adopt player share self nearby scenic spots information and then through another players come the same place do the verification, allowing the player to play the game in the side of the process of collecting the relevant geographic information, on the other hand to achieve the purpose of scenic spots information is reliable. However, how to choose topics for player verification can effectively evaluate the system performance, to solve these problems we propose three different selection strategies on the task assignment algorithms, then through a series of simulation experiments designed to prove that our system can indeed achieve good performance on the topics, finally we take into account real life situations and implement in the smart phone (Android Phone).