Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2018-03-12T13:20:57Z No. of bitstreams: 1 JulioCesarMeloGomesDeOliveira_DISSERT.pdf: 3345480 bytes, checksum: 4a29c5363f621c0fa11407514ac869fa (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2018-03-14T14:35:59Z (GMT) No. of bitstreams: 1 JulioCesarMeloGomesDeOliveira_DISSERT.pdf: 3345480 bytes, checksum: 4a29c5363f621c0fa11407514ac869fa (MD5) / Made available in DSpace on 2018-03-14T14:35:59Z (GMT). No. of bitstreams: 1 JulioCesarMeloGomesDeOliveira_DISSERT.pdf: 3345480 bytes, checksum: 4a29c5363f621c0fa11407514ac869fa (MD5) Previous issue date: 2017-12-19 / Historicamente as cidades seguem modelos de planejamento reativos onde o gestor toma decis?es conforme a ocorr?ncia dos problemas. Por outro lado, o crescimento exponencias das Tecnologias da Informa??o e Comunica??o (TIC) tem permitido que diversos sensores, dispositivos, sistemas e objetos se conectem, gerando dados que podem ser transformados em informa??o e usados em um paradigma de planejamento urbano mais eficiente onde as decis?es podem ser tomadas antes que os problemas ocorram. Assim, nesse trabalho ser? desenvolvido um software capaz de estimar a quantidade de di?xido de carbono, a partir de leituras de sensores existentes em ve?culos, que ir? contribuir para o planejamento proativo das cidades a partir do monitoramento da polui??o veicular. T?cnicas de CrowdSensing e um leitor On-Board Diagnostic (OBD-II) ser?o utilizados para extrair dados dos ve?culos em tempo real, armazenados localmente nos aparelhos que foram coletados. Por fim, podemos ver que a partir das informa??es obtidas, existe a possibilidade de se extrair informa??es a respeito do funcionamento do ve?culo e at? mesmo realizar infer?ncias sobre a din?mica dos ve?culos nas cidades, mostrando potencial para o desenvolvimento de ferramentas auxiliares a gest?o dos centros urbanos. / Traditionally, cities planning has followed reactive decision models based on the occurrence of problems. With the development and spread of communication and information technologies, the interconnection of electronic devices has opened a new era of data exchanging and processing, potentially supporting more efficient decisions in modern cities. This paper then proposes a software, capable of estimating the amount of carbon dioxide from existing sensor readings in vehicles, which aims to support more proactive planning and management of modern cities, addressing the problem of vehicular pollution monitoring. For that, a crowdsensing approach and the OBD-II standard are exploited to dynamically extract data from vehicles to be processed and delivered. Finally, we can see that with the information obtained, it is possible to extract information about the operation of the vehicle and even make inferences about the dynamics of the vehicles in the cities, showing potential for the development of auxiliary tools for the management of urban centers .

[EN] This thesis project has a different and innovative approach to detect seismic events in real time gaining knowledge of the community through a hierarchical architecture in 3 layers: The first layer, a low-cost distributed network which takes advantage of the current huge trend; the smartphone; a multi-sensor, multi-network, multi-task device embedded into a small processing computer able to be reprogrammed, for example, in an "accelerograph" through an efficient in precision and power consumption Android application. The second layer called Intermediate Server, corresponds to a computer with sufficient hardware to handle incoming messages from users of the first layer and deduce on these samples, if a seismic peak has occurred, and if appropriate, notify in real-time to the users (smartphones) gaining extra time in making a better decision involving harm reduction, as well as economic and structural losses, and most importantly human lives losses. It considers spatial and temporal analyzes obtaining a customizable server to the specific characteristics of the area. Finally, the third layer called the Control Center is the place where all the information from the lower levels makes sense, being the leader in the post-event emergency management; it can extend to a bidirectional help: first, users (smartphones) attend to Control Center with multimedia information from their environment (comments, videos and images) thus achieving an overview of the disaster in order to efficiently manage the various aid-centers; and second, it assists users through their own smartphones, with information that a particular user unknown but the Control Center knows by other users: roads destroyed, landslides, roads offline support centers, places of danger, etc.). All validated through an extensive evaluation of each layer through seismic data obtained from the National Geophysical Institute of the National Polytechnic School in Ecuador (IGEPN), data which part of this research is based; obtaining promising and relevant results alerting until 12 seconds ahead at the epicenter, reducing false positives and this time could be increased by further afield. / [ES] Este proyecto de Tesis presenta una diferente e innovadora pro-puesta para detectar eventos sísmicos en tiempo real obteniendo conocimiento de la comunidad mediante una arquitectura jerárquica en 3 capas: La primera capa, una red de sensores de bajo costo distribuida que aprovecha el gran boom electrónico actual, el smartphone; un equipo multi-sensor, multi-red, multi-procesamiento dentro de un pequeño ordenador capaz de ser reprogramado convirtiéndolo por ejemplo, en un "acelerógrafo" por medio de una aplicación en Android eficiente en precisión y consumo de energía. La segunda capa llamada Servidor Intermedio, corresponde a un ordenador con las capacidades suficientes para gestionar la llegada de mensajes provenientes de usuarios de la primera capa y deducir con estas muestras, si ha existido un pico sísmico o no, y si es el caso, notificar en tiempo real a los usuarios (smartphones) ganando tiempo extra en la toma de una mejor decisión que implique la reducción de daños y pérdidas tanto económicas, estructurales y lo más importante, vidas humanas. Este considera análisis tanto espaciales como temporales obteniendo un servidor personalizable a las características específicas de la zona. Por último, la tercera capa llamada el Centro de Control es el lugar donde toda la información de los niveles inferiores toma sentido siendo el líder en la gestión post-evento de la emergencia; permite extender a una ayuda bidireccional: primero cada smartphone asiste a éste con información multimedia de su entorno (comentarios, videos e imágenes) logrando así una visión global del desastre, y con esta, gestionar eficientemente a los diferentes cuerpos de ayuda; y segundo asistir a los usuarios por medio de su smartphone con información que un usuario en particular desconoce y que el Centro de Control conoce por otros usuarios: Carreteras destruidas, deslaves, caminos sin conexión, centros de apoyo, lugares de peligro, etc.). Todo validado mediante una extensa evaluación de cada una de las capas con información sísmica obtenida del Instituto Geofísico Nacional de la Escuela Politécnica Nacional del Ecuador (IGEPN), lugar donde se basa parte de esta investigación, obteniendo prometedores y relevantes resultados alertando hasta con 12 segundos de antelación en el lugar del epicentro, reduciendo los falsos positivos, y pudiendo incrementar este tiempo en lugares más alejados. / [CAT] Aquest projecte de Tesi presenta una diferent i innovadora proposta per a detectar esdeveniments sísmics en temps real obtenint coneixement de la comunitat mitjançant una arquitectura jeràrquica en 3 capes: La primera capa, una xarxa de sensors de baix cost distribuïda que aprofita el gran boom electrònic actual, el l' smartphone; un equip multi-sensor, multi-xarxa, multi-processament dins d'un petit ordinador capaç de ser reprogramat convertint-ho per exemple, en un accelerògraf per mitjà d'una aplicació en Android eficient en precisió i consum d'energia. La segona capa anomenada Servidor Intermedi, correspon a un ordinador amb les capacitats suficients per a gestionar l'arribada de missatges provinents d'usuaris de la primera capa i deduir amb aquestes mostres, si ha existit un pic sísmic o no, i si és el cas, notificar en temps real als usuaris (smartphones) guanyant temps extra en la presa d'una millor decisió que implique la reducció de danys i pèrdues tant econòmiques, estructurals i el més important, vides humanes; aquest considera anàlisi tant espacials com a temporals obtenint un servidor personalitzable a les característiques específiques de la zona. Finalment, la tercera capa anomenada el Centre de Control és el lloc on tota l'informació dels nivells inferiors pren sentit sent el líder en la gestió post-esdeveniment de l'emergència; permet estendre a una ajuda bidireccional, primer cada smartphone assisteix a aquest amb l'informació multimèdia del seu entorn (comentaris, videos i imatges) aconseguint així una visió global del desastre i amb aquesta, gestionar eficientment als diferents cossos d'ajuda; i posteriorment assistir als usuaris per mitjà del seu smartphone amb informació que un usuari en particular desconeix i que el Centre de Control coneix per altres usuaris: Carreteres destruïdes, esllavissades, camins sense connexió, centres de suport, llocs de perill, etc.). Tot validat mitjançant una extensa avaluació de cadascuna de les capes amb informació sísmica obtinguda de l'Institut Geofísic Nacional de l'Escola Politècnica Nacional de l'Equador (IGEPN), lloc on es basa part d'aquesta recerca, obtenint prometedors i rellevants resultats alertant fins a amb 12 segons d'antelació en el lloc de l'epicentre, reduint els falsos positius i podent incrementar-se en llocs més allunyats. / Zambrano Vizuete, AM. (2015). ARQUITECTURA DE UN SISTEMA DISTRIBUIDO PARA GESTIÓN DE EMERGENCIAS SÍSMICAS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/56002 / TESIS

Mobile Crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of the smart devices to improve the sensing capacity and reduce the sensing costs in large regions. Due to the ubiquitous nature of MCS, smart devices require cyber protection against adversaries that are becoming smarter with the objective of clogging the resources and spreading misinformation in such a non-dedicated sensing environment. In an MCS setting, one of the various adversary types has the primary goal of keeping participant devices occupied by submitting fake/illegitimate sensing tasks so as to clog the participant resources such as the battery, sensing, storage, and computing. With this in mind, this thesis proposes a systematical study of fake task injection in MCS, including modeling, detection, and mitigation of such resource clogging attacks. We introduce modeling of fake task attacks in MCS intending to clog the server and drain battery energy from mobile devices. We creatively grant mobility to the tasks for more extensive coverage of potential participants and propose two take movement patterns, namely Zone-free Movement (ZFM) model and Zone-limited Movement (ZLM) model. Based on the attack model and task movement patterns, we design task features and create structured simulation settings that can be modified to adapt different research scenarios and research purposes. Since the development of a secure sensing campaign highly depends on the existence of a realistic adversarial model. With this in mind, we apply the self-organizing feature map (SOFM) to maximize the number of impacted participants and recruits according to the user movement pattern of these cities. Our simulation results verify the magnified effect of SOFM-based fake task injection comparing with randomly selected attack regions in terms of more affected recruits and participants, and increased energy consumption in the recruited devices due to the illegitimate task submission. For the sake of a secure MCS platform, we introduce Machine Learning (ML) methods into the MCS server to detect and eliminate the fake tasks, making sure the tasks arrived at the user side are legitimate tasks. In our work, two machine learning algorithms, Random Forest and Gradient Boosting are adopted to train the system to predict the legitimacy of a task, and Gradient Boosting is proven to be a more promising algorithm. We have validated the feasibility of ML in differentiating the legitimacy of tasks in terms of precision, recall, and F1 score. By comparing the energy-consuming, effected recruits, and impacted candidates with and without ML, we convince the efficiency of applying ML to mitigate the effect of fake task injection.

Group signature schemes enable anonymous-yet-accountable communications. Such a capability is extremely useful for modern applications such as smartphone-based crowdsensing and citizen science. A prototype named GROUPSENSE was developed to support anonymous-yet-accountable crowdsensing with SRBE in Android devices. From this prototype, an Android crowdsensing application was implemented to support privacy in citizen science. In this thesis, we will evaluate the usability of our privacy-preserving crowdsensing application for citizen science projects. An in person user study with 22 participants has been performed showing that participants understood the importance of privacy in citizen science and were willing to install privacy-enhancing applications, yet over half of the participants did not understand the privacy guarantee. Based on these results, modifications to the crowdsensing application have been made with the goal of improving the participants' understanding of the privacy guarantee. / Master of Science

Smart devices have become one of the fundamental communication and computing devices in people's everyday lives over the past decade. Their various sensors and wireless connectivity have paved the way for a new application area called mobile crowdsensing where sensing services are provided by using the sensor outputs collected from smart devices. A mobile crowdsensing system's service quality heavily depends on the participation of smart device users who probably expect to be compensated in return for their participation. Therefore, mobile crowdsensing applications need incentive mechanisms to motivate such people into participating. In this thesis, we first defined a reverse auction based incentive mechanism for a representative mobile crowdsensing system. Then, we integrated the Vickrey-Clarke- Groves mechanism into the initial incentive mechanism so as to investigate whether truthful bidding would become the dominant strategy in the resulting incentive mechanism. We demonstrated by theoretical analysis that overbidding was the dominant strategy in the base incentive mechanism, whereas truthful bidding was the dominant strategy in the derived incentive mechanism when the VCG mechanism was applicable. Finally, we conducted simulations of both incentive mechanisms in order to measure the fairness of service prices and the fairness of cumulative participant earnings using Jain's fairness index. We observed that both the fairness of service prices and the fairness of cumulative participant earnings were generally better in the derived incentive mechanism when the VCG mechanism was applied. We also found that at least 70% of service requests had fair prices, while between 5% and 85% of participants had fair cumulative earnings in both incentive mechanisms.

L’ubiquité des terminaux intelligents équipés de capteurs a donné naissance à un nouveau paradigme de collecte participative des données appelé Crowdsensing. Pour mener à bien les tâches de collecte, divers défis relatifs à l’implication des participants et des demandeurs de services doivent être relevés. Dans ce contexte, nous abordons quatre questions majeures inhérentes à ce problème: Comment affecter les tâches de collecte afin de maximiser la qualité des données d’une façon éco-énergétique ? Comment minimiser le temps nécessaire à la collecte et au traitement des tâches? Comment inciter les participants à dédier une partie de leurs ressources pour la collecte? et Comment protéger la vie privée des participants tout en préservant la qualité des données reportées ? Tout d’abord, nous nous intéressons au fait que les ressources énergétiques des terminaux mobiles restent limitées. Nous introduisons alors des modèles de déploiement de tâches qui visent à maximiser la qualité des données reportées tout en minimisant le coût énergétique global de la collecte. Ainsi, notre première contribution se matérialise en un modèle d’allocation appelé, QEMSS. QEMSS définit des métriques de qualité de données et cherche à les maximiser en se basant sur des heuristiques utilisant la recherche taboue. De plus, afin de rendre le processus d’allocation résultante plus équitable, nous faisons appel à un deuxième algorithme, F-QEMSS, extension de QEMSS. Les deux solutions ont permis d’obtenir des niveaux de qualité de données compétitifs principalement dans les situations défavorables des zones de faible densité ou de ressources limitées. En outre, afin de minimiser le temps moyen de collecte et de traitement des données, une deuxième phase d’allocation distribuée est ajoutée. Plus précisément, nous proposons dans cette deuxième contribution de désigner des participants responsables de déléguer des tâches. Ces derniers prédisent le comportement d’autres utilisateurs en termes de mobilité et de préférences de collecte. Par conséquent, nous développons deux types d’allocation; MATA qui ne tient compte que de la mobilité et P-MATA qui tient compte à la fois de la mobilité et des préférences des participants. Les deux allocations démontrent que l’estimation des préférences des utilisateurs minimise le temps de collecte et évite le rejet des tâches. La troisième contribution de cette thèse, IP-MATA+, propose des incitations aux participants, ce qui favorise leur engagement aux campagnes de collecte notamment quand le budget dédié est partagé en fonction de la qualité des contributions. Pour finir, nous considérons la problématique de la vie privée des participants au crowdsensing. Particulièrement, nous ciblons la minimisation du risque de divulgation de la vie privée durant la phase du déchargement tout en veillant à l’utilité des données collectées. Ainsi, la quatrième contribution de cette thèse vise à assurer simultanément deux objectifs concurrents, à savoir assurer l’utilité des données nécessaire aux demandeurs et protéger les informations sensibles des participants. Pour ce faire, nous introduisons une entité de confiance dans le système de collecte ayant pour rôle d’exécuter un mécanisme qui génère une version altérée de la donnée collectée qui répond au compromis de protection et d’utilité. La solution développée, appelée PRUM, a été évaluée sur des datasets de collecte participative en variant les scénarios d’attaque et de déchargement des données. Les résultats obtenus prouvent qu’une altération limitée des données collectées peut assurer une protection des informations sensibles des participants tout en préservant environ 98% de l’utilité des données obtenue pour les demandeurs. Pour conclure, nos contributions abordent diverses problématiques complémentaires inhérentes à la collecte participative des données ouvrant la voie à des mises en œuvre réelles et facilitant leur déploiement / The ubiquity of sensors-equipped mobile devices has enabled people to contribute data via crowdsensing systems. This emergent paradigm comes with various applications. However, new challenges arise given users involvement in data collection process. In this context, we introduce collaborative sensing schemes which tackle four main questions: How to assign sensing tasks to maximize data quality with energy-awareness? How to minimize the processing time of sensing tasks? How to motivate users to dedicate part of their resources to the crowdsensing process ? and How to protect participants privacy and not impact data utility when reporting collected sensory data ? First, we focus on the fact that smart devices are energy-constrained and develop task assignment methods that aim to maximize sensor data quality while minimizing the overall energy consumption of the data harvesting process. The resulting contribution materialized as a Quality and Energy-aware Mobile Sensing Scheme (QEMSS) defines first data quality metrics then models and solves the corresponding optimization problem using a Tabu-Search based heuristic. Moreover, we assess the fairness of the resulted scheduling by introducing F-QEMSS variant. Through extensive simulations, we show that both solutions have achieved competitive data quality levels when compared to concurrent methods especially in situations where the process is facing low dense sensing areas and resources shortcomings. As a second contribution, we propose to distribute the assignment process among participants to minimize the average sensing time and processing overload com- pared to a fully centralized approach. Thus, we suggest to designate some participants to carry extra sensing tasks and delegate them to appropriate neighbors. The new assign- ment is based on predicting users local mobility and sensing preferences. Accordingly, we develop two new greedy-based assignment schemes, one only Mobility-aware (MATA) and the other one accounting for both preferences and mobility (P-MATA), and evaluate their performances. Both MATA and P-MATA consider a voluntary sensing process and show that accounting for users preferences minimize the sensing time. Having showing that, our third contribution in this thesis is conceived as an Incentives-based variant, IP-MATA+. IP-MATA+ incorporates rewards in the users choice model and proves their positive impact on enhancing their commitment especially when the dedicated budget is shared function of contributed data quality. Finally, our fourth and last contribution addresses the seizing of users privacy concerns within crowdsensing systems. More specifically, we study the minimization of the incurred privacy leakage in data uploading phase while accounting for the possible quality regression. That is, we assess simultaneously the two competing goals of ensuring queriers required data utility and protecting participants’ sensitive information. Thus, we introduce a trust entity to the crowdsensing traditional system. This entity runs a general privacy-preserving mechanism to release a distorted version of sensed data that responds to a privacy-utility trade-off. The proposed mechanism, called PRUM, is evaluated on three sensing datasets, different adversary models and two main data uploading scenarios. Results show that a limited distortion on collected data may ensure privacy while maintaining about 98% of the required utility level.The four contributions of this thesis tackle competing issues in crowdsensing which paves the way at facilitating its real implementation and aims at broader deployment

[pt] A área das telecomunicações tem presenciado consideráveis avanços tecnológicos dos dispositivos móveis (e. g. telefones inteligentes, relógios inteligentes, tablets, reprodutores de música, entre outros) e da sua crescente popularidade. Dado que esses equipamentos possuem uma série de sensores embutidos como o sistema de posicionamento global, câmera, microfone, bússola, acelerômetro entre outros, e ao mesmo tempo mantêm acesso contínuo às redes de comunicações, eles apresentam uma oportunidade para realizar sensoriamento em grande escala de possíveis eventos do mundo físico e compartilhar os dados obtidos através da internet. Este novo tipo de sensoriamento é conhecido como sensoriamento coletivo móvel ou MCS (Mobile crowd sensing) por sua sigla em inglês, e recentemente tem sido o foco de diversas pesquisas. O maior potencial do MCS é a opção de desenvolver um inúmero de funcionalidades a partir dos próprios recursos internos dos dispositivos móveis e aproveitar seu modelo de mobilidade, baseado no comportamento humano. Em contrapartida existem questões que devem ser consideradas na hora de desenvolver uma rede baseada em MCS. Neste trabalho, apresenta-se a análise realizada sobre os pontos críticos para a criação de uma rede MCS e a partir dos mesmos são apresentadas soluções que permitam criar uma implementação o mais próximo possível da realidade. Foi desenvolvido um modelo de mobilidade para a cidade de Rio de Janeiro baseado na teoria dos grafos, considerando que as atividades diárias das pessoas serão as que definam seu padrão de movimento. A cooptação de usuários é outro dos principais problemas que tem que ser abordado quando se pensa no sensoriamento coletivo móvel. Propõe-se dois modelos de incentivo que consideram e modelam como variáveis as motivações intrínsecas e extrínsecas dos usuários na decisão de participação em uma rede MCS. Consideram também distintos graus de motivação para cada usuário com a finalidade de demonstrar que a resposta dos participantes aos incentivos não é homogênea. O primeiro modelo baseia-se nas respostas consecutivas dos usuários e o segundo baseia-se na teoria de jogos. Em ambos, as decisões tomadas pelos usuários só consideram informações locais ou próprias. Os resultados obtidos permitiram comprovar que os modelos de incentivo propostos conseguem estimar satisfatoriamente o tipo de usuário com o qual está se interagindo e a quantidade de incentivo que deve ser oferecido a cada um deles, além de demonstrar as vantagens de um sistema de incentivo que considera pagamentos variáveis. Também foram analisadas as vantagens de considerar a mobilidade humana neste tipo de abordagem e como a mesma reflete nos modelos de incentivo. / [en] The world of telecommunications has witnessed the growing popularity of mobile devices and its huge technological advancements and innovations (e.g. smartphones, smartwatches, tablets, music players among others). These devices have a series of built-in sensors that measure motion, orientation, and various environmental conditions (e.g. Global Positioning System, camera, microphone, compass, accelerometer, among others). In addition, these devices have continuous network connectivity. So these devices can be seen as a huge opportunity to carry out large-scale sensing of events in the physical world and have the ability of sharing the data obtained through the internet. This new kind of sensor application is known as Mobile crowd sensing (MCS) and it has been a research focus lately. The greatest potential of MCS is found on the versatility that the embedded resources of the mobile devices offer in the development of innumerable functionalities and its mobility model that is based on human behavior. On the other hand, there are issues that must be considered when a MCS-based network is developed. This work presents the analysis performed in order to define issues that are considered critical for the creation and development of an MCS network. From these definitions solutions are proposed that allow to create an implementation as close as possible to reality. A mobility model was developed for the Rio de Janeiro city based on graph theory, and assuming that daily activities of the people will define their movement pattern. Attracting and convincing users is another problem that has to be addressed. Two user incentive models are proposed. Both consider and model the decision of a user to participate in an MCS network based on the intrinsic and extrinsic motivations of the user. The idea is to comprise different levels of motivation for each user in order to demonstrate that the response of the participants to the incentives is not homogeneous. Thus, the first model is based on the consecutive answers of the users and the second model is based on game theory. The results obtained allowed us to prove that the proposed incentive models can satisfactorily estimate the type of user with which we are interacting and the amount of incentive that should be offered to each one of them, besides demonstrating the advantages of an incentive system that considers variable payments. The advantages of considering human mobility in this type of approach and how it affects the incentive models was also analyzed.

A incorporação das tecnologias de computação e de comunicação nos veículos modernos está viabilizando uma nova geração de automóveis conectados. Com a capacidade de se organizarem em rede, nas chamadas redes veiculares ad hoc (VANETs), eles poderão, num futuro próximo, (i) tornar o trânsito mais seguro para os motoristas, passageiros e pedestres e/ou (ii) promover uma experiência de transporte mais agradável, com maior conforto. É neste contexto que se destaca o Sensoriamento Veicular Colaborativo (VCS), um paradigma emergente e promissor que explora as tecnologias já embarcados nos próprios veículos para a obtenção de dados in loco. O VCS tem demonstrado ser um modelo auspicioso para o desenvolvimento e implantação dos Sistemas Inteligentes de Transporte (ITSs). Ocorre, todavia, que, em grandes centros urbanos, dependendo do fenômeno a ser monitorado, as aplicações de VCS podem gerar um tráfego de dados colossal entre os veículos e o centro de monitoramento. Considerando que as informações dos automóveis são geralmente enviadas para um servidor remoto usando as infraestruturas das redes móveis, o número massivo de transmissões geradas durante as atividades de sensoriamento pode sobrecarregá-las e degradar consideravelmente a Qualidade de Serviço (QoS) que elas oferecem. Este documento de tese descreve e analisa uma abordagem de clusterização geográfica que se apoia no uso de comunicações Veículo-para-Veículo (V2V) para promover o descarregamento de dados do VCS em redes celulares, de forma a minimizar os impactos supracitados. Os resultados experimentais obtidos mostraram que o uso das comunicações V2V como método complementar de aquisição de dados in loco foi capaz de diminuir consideravelmente a quantidade transmissões realizadas sobre as redes móveis, sem a necessidade de implantação de novas infraestruturas de comunicação no ambiente, e com um reduzido atraso médio adicional fim a fim na obtenção das informações. A abordagem desenvolvida também se apresenta como uma plataforma de software flexível sobre a qual podem ser incorporadas técnicas de agregação de dados, o que possibilitaria aumentar ainda mais a preservação dos recursos de uplink das redes celulares. Considerando que a era da Internet das Coisas (IoT) e das cidades inteligentes está apenas começando, soluções para o descarregamento de dados, tal como a tratada nesta pesquisa, são consideradas imprescindíveis para continuar mantendo a rede móvel de acesso à Internet operacional e capaz de suportar uma demanda de comunicação cada vez maior por parte das aplicações. / The incorporation of computing and communication technologies into modern vehicles is enabling a new generation of connected cars. With the ability to get into a network formation, in the so-called ad hoc networks (VANETs), these vehicles might, in the near future, (i) make the traffic safer for drivers, passengers and pedestrians and/or (ii) promote a more pleasant transportation experience, with greater comfort. It is in this context that emerges the Vehicle CrowdSensing (VCS), a novel and promising paradigm for performing in loco data collection from the vehicles embedded technologies. VCS has proved to be an auspicious scheme for the development and deployment of the Intelligent Transport Systems (ITSs). However, in large urban areas, depending on the phenomenon to be monitored, the VCS applications can generate a colossal data traffic between vehicles and the monitoring center. Considering that all the vehicles information is generally sent to the remote server by using mobile network infrastructures, this massive amount of transmissions generated during the sensing activities can overload them and degrade the Quality of Service (QoS) they offer. This thesis document describes and analyzes a geographic clustering approach that relies on the use of Vehicle-to- Vehicle (V2V) communications to promote the VCS data offloading in cellular networks, in order to minimize the above impacts. The experimental results obtained showed that the use of V2V communications as a complementary data acquisition method was able to considerably reduce the number of transmissions carried out on mobile networks, without the need for deploying new communication infrastructures in the environment, and with a reduced additional delay. The created approach also stands itself as a flexible software platform on which data aggregation techniques can be incorporated, in order to maximize the network resources preservation already provided by the proposal. Considering that we are just entering in the Internet of Things (IoT) and smart cities era, creating data offloading solutions, such as that treated in this research, is considered an essential task to keep the Internet access network operational and able to support the growing demand for mobile communications.

La contaminación ambiental es uno de los principales problemas que afecta a nuestro planeta. El crecimiento industrial y los aglomerados urbanos, entre otros, están contribuyendo a que dicho problema se diversifique y se cronifique. La presencia de contaminantes ambientales en niveles elevados afecta la salud humana, siendo la calidad del aire y los niveles de ruido ejemplos de factores que pueden causar efectos negativos en las personas tanto psicológicamente como fisiológicamente. Sin embargo, la ubiquidad de los microcomputadores, y el aumento de los sensores incorporados en nuestros smartphones, han hecho posible la aparición de nuevas estrategias para medir dicha contaminación. Así, el Mobile Crowdsensing se ha convertido en un nuevo paradigma mediante el cual los teléfonos inteligentes emergen como tecnología habilitadora, y cuya adopción generalizada proporciona un enorme potencial para su crecimiento, permitiendo operar a gran escala, y con unos costes asumibles para la sociedad. A través del crowdsensing, los teléfonos inteligentes pueden convertirse en unidades de detección flexibles y multiuso que, a través de los sensores integrados en dichos dispositivos, o combinados con nuevos sensores, permiten monitorizar regiones de interés con una buena granularidad tanto espacial como temporal. En esta tesis nos centramos en el diseño de soluciones de crowdsensing usando smartphones donde abordamos problemas de contaminación ambiental, específicamente del ruido y de la contaminación del aire. Con este objetivo, se estudian, en primer lugar, las propuestas de crowdsensing que han surgido en los últimos años. Los resultados de nuestro estudio demuestran que todavía hay mucha heterogeneidad en términos de tecnologías utilizadas y métodos de implementación, aunque los diseños modulares en el cliente y en el servidor parecen ser dominantes. Con respecto a la contaminación del aire, proponemos una arquitectura que permita medir la contaminación del aire, concretamente del ozono, dentro de entornos urbanos. Nuestra propuesta utiliza smartphones como centro de la arquitectura, siendo estos dispositivos los encargados de leer los datos de un sensor móvil externo, y de luego enviar dichos datos a un servidor central para su procesamiento y tratamiento. Los resultados obtenidos demuestran que la orientación del sensor y el período de muestreo, dentro de ciertos límites, tienen muy poca influencia en los datos capturados. Con respecto a la contaminación acústica, proponemos una arquitectura para medir los niveles de ruido en entornos urbanos basada en crowdsensing, y cuya característica principal es que no requiere intervención del usuario. En esta tesis detallamos aspectos tales como la calibración de los smartphones, la calidad de las medidas obtenidas, el instante de muestreo, el diseño del servidor, y la interacción cliente-servidor. Además, hemos validado nuestra solución en escenarios reales para demostrar el potencial de la solución alcanzada. Los resultados experimentales muestran que, con nuestra propuesta, es posible medir niveles de ruido en diferentes zonas urbanas o rurales con un grado de precisión comparable al de los dispositivos profesionales, todo ello sin requerir intervención del usuario, y con un consumo reducido en cuanto a recursos del sistema. En general, las diferentes contribuciones de esta tesis doctoral ofrecen un punto de partida para nuevos desarrollos, ofreciendo estrategias de calibración y algoritmos eficientes de cara a realizar medidas representativas. Además, una importante ventaja de nuestra propuesta es que puede ser implementada de forma directa tanto en instituciones públicas como no gubernamentales en poco tiempo, ya que utiliza tecnología accesible y soluciones basadas en código abierto. / La contaminació ambiental és un dels principals problemes que afecten el nostre planeta. El creixement industrial i els aglomerats urbans, entre altres, estan contribuint al fet que aquest problema es diversifique i es cronifique. La presència de contaminants ambientals en nivells elevats afecta la salut humana, sent la qualitat de l'aire i els nivells de soroll exemples de factors que poden causar efectes negatius en les persones, tant psicològicament com fisiològicament. No obstant això, la ubiqüitat de les microcomputadores i l'augment dels sensors incorporats als nostres telèfons intel·ligents han fet possible l'aparició de noves estratègies per a mesurar aquesta contaminació. Així, el mobile crowdsensing s'ha convertit en un nou paradigma mitjançant el qual els telèfons intel·ligents emergeixen com a tecnologia habilitadora, i l'adopció generalitzada d'aquest proporciona un enorme potencial per al seu creixement, ja que permet operar a gran escala i amb uns costos assumibles per a la societat. A través del crowdsensing, els telèfons intel·ligents poden convertir-se en unitats de detecció flexibles i multiús que, a través dels sensors integrats en els esmentats dispositius, o combinats amb nous sensors, permeten monitoritzar regions d'interès amb una bona granularitat, tant espacial com temporal. En aquesta tesi ens centrem en el disseny de solucions de crowdsensing usant telèfons intel·ligents, on abordem problemes de contaminació ambiental, específicament del soroll i de la contaminació de l'aire. Amb aquest objectiu, s'estudien, en primer lloc, les propostes de crowdsensing que han sorgit en els últims anys. Els resultats del nostre estudi demostren que encara hi ha molta heterogeneïtat en termes de tecnologies utilitzades i mètodes d'implementació, encara que els dissenys modulars en el client i en el servidor semblen ser dominants. Pel que fa a la contaminació de l'aire, proposem una arquitectura que permeta mesurar la contaminació d'aquest, concretament de l'ozó, dins d'entorns urbans. La nostra proposta utilitza telèfons intel·ligents com a centre de l'arquitectura, sent aquests dispositius els encarregats de llegir les dades d'un sensor mòbil extern, i d'enviar després aquestes dades a un servidor central per al seu processament i tractament. Els resultats obtinguts demostren que l'orientació del sensor i el període de mostratge, dins de certs límits, tenen molt poca influència en les dades capturades. Pel que fa a la contaminació acústica, proposem una arquitectura per a mesurar els nivells de soroll en entorns urbans basada en crowdsensing, i la característica principal de la qual és que no requereix intervenció de la persona usuària. En aquesta tesi detallem aspectes com ara el calibratge dels telèfons intel·ligents, la qualitat de les mesures obtingudes, l'instant de mostratge, el disseny del servidor i la interacció client-servidor. A més, hem validat la nostra solució en escenaris reals per a demostrar el potencial de la solució assolida. Els resultats experimentals mostren que, amb la nostra proposta, és possible mesurar nivells de soroll en diferents zones urbanes o rurals amb un grau de precisió comparable al dels dispositius professionals, tot això sense requerir intervenció de l'usuari o usuària, i amb un consum reduït quant a recursos del sistema. En general, les diferents contribucions d'aquesta tesi doctoral ofereixen un punt de partida per a nous desenvolupaments, i ofereixen estratègies de calibratge i algorismes eficients amb vista a realitzar mesures representatives. A més, un important avantatge de la nostra proposta és que pot ser implementada de forma directa tant en institucions públiques com no governamentals en poc de temps, ja que utilitza tecnologia accessible i solucions basades en el codi obert. / Environmental pollution is one of the main problems that affect our planet. Industrial growth and urban agglomerations, among others, are contributing to the diversification and chronification of this problem. The presence of environmental pollutants at high levels affect human health, with air quality and noise levels being examples of factors that can cause negative effects on people both psychologically and physiologically. Traditionally, environmental pollution is measured through monitoring centers, which are usually fixed and have a high cost. However, the ubiquity of microcomputers and the increase in the number of sensors embedded in our smartphones, have paved the way for the appearance of new strategies to measure such pollution. Thus, Mobile Crowdsensing has become a new paradigm through which smartphones emerge as an enabling technology, and whose widespread adoption provides enormous potential for growth, allowing large-scale operations, and with costs acceptable to our society. Through crowdsensing, smartphones can become flexible and multipurpose detection units that, through the sensors integrated into these devices, or combined with new sensors, allow monitoring regions of interest with good spatial and temporal granularity. In this thesis, we focus on the design of crowdsensing solutions using smartphones. We deal with environmental pollution problems, specifically noise and air pollution. With this objective, the crowdsensing proposals that have emerged in recent years are studied in the first place. The results of our study show that there is still a lot of heterogeneity in terms of technologies used and implementation methods, although modular designs at both client and server seem to be dominant. Concerning air pollution, we propose an architecture that allows measuring air pollution, specifically ozone, in urban environments. Our proposal uses smartphones as the center of the architecture, being these devices responsible for reading the data obtained by an external mobile sensor, and then sending such data to a central server for processing and analysis. In this proposal, several problems have been analyzed with regard to the orientation of the external sensor and the sampling time, and the proposed solution has been validated in real scenarios. The results obtained show that the orientation of the sensor and the sampling period, within certain limits, have very little influence on the captured data. Also, by comparing the heat maps generated by our solution with the data from the existing monitoring stations in the city of Valencia, we demonstrate that our approach is capable of providing greater data granularity. Concerning noise pollution, we propose an architecture to measure noise levels in urban environments based on crowdsensing, and whose main characteristic is that it does not require user intervention. In this thesis, we detail aspects such as the calibration of smartphones, the quality of the measurements obtained, the sampling instant, the server design, and the client-server interaction. Besides, we have validated our solution in real scenarios to demonstrate the potential of the proposed solution. Experimental results show that, with our proposal, it is possible to measure noise levels in different urban or rural areas with a degree of precision comparable to that of professional devices, all without requiring the intervention of the user, and with reduced consumption of system resources. In general, the different contributions of this doctoral thesis provide a starting point for new developments, offering efficient calibration strategies and algorithms to make representative measurements. Besides, a significant advantage of our proposal is that it can be implemented straightforwardly by both public and non-governmental institutions in a short time, as it relies on accessible technology and open source software / Zamora Mero, WJ. (2018). Crowdsensing solutions for urban pollution monitoring using smartphones [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/115483 / TESIS

De nos jours, il y a une demande croissante pour fournir de l'information temps réel à partir de l'environnement, e.g. état infectieux de maladies, force du signal, conditions de circulation, qualité de l'air. La prolifération des dispositifs de capteurs et la mobilité des personnes font de la Mobile Collaborative Sensing (MCS) un moyen efficace de détecter et collecter l'information à un faible coût. Dans MCS, au lieu de déployer des capteurs statiques dans une zone, les personnes disposant d'appareils mobiles jouent le rôle de capteurs mobiles. En général, une application MCS exige que l'appareil de chacun ait la capacité d'effectuer la détection et retourne les résultats à un serveur central, mais également de collaborer avec d'autres dispositifs. Pour que les résultats puissent représenter l'information physique d'une région cible et convenir, quel type de données peut être utilisé et quel type d'information doit être inclus dans les données collectées? Les données spatio-temporelles peuvent être utilisées par des applications pour bien représenter la région cible. Dans des applications différentes, l'information de localisation et de temps sont 2 types d'information communes, et en les utilisant la région cible d'une application est sous surveillance complète du temps et de l'espace. Différentes applications nécessitent de l'information différente pour atteindre des objectifs différents. E.g. dans cette thèse: i- MCS-Locating application: l'information de résistance du signal doit être incluse dans les données détectées par des dispositifs mobiles à partir d'émetteurs de signaux ; ii- MCS-Prédiction application : la relation entre les cas d'infection et les cas infectés doit être incluse dans les données par les dispositifs mobiles provenant des zones de flambée de la maladie ; iii- MCS-Routing application : l'information routière en temps réel provenant de différentes routes de circulation doit être incluse dans les données détectées par des dispositifs embarqués. Avec la détection de l'information physique d'une région cible, et la mise en interaction des dispositifs, 3 thèmes d'optimisation basés sur la détection sont étudiés et 4 travaux de recherche menés: -Mobile Collaboratif Détection Cadre : un cadre mobile de détection collaborative est conçu pour faciliter la coopérativité de la collecte, du partage et de l'analyse des données. Les données sont collectées à partir de sources et de points temporels différents. Pour le déploiement du cadre dans les applications, les défis clés pertinents et les problèmes ouverts sont discutés. -MCS-Locating : l'algorithme LiCS (Locating in Collaborative Sensing based Data Space) est proposé pour atteindre la localisation de la cible. LiCS utilise la puissance du signal reçu dans tous les périphériques sans fil comme empreintes digitales de localisation pour les différents emplacements. De sorte LiCS peut être directement pris en charge par l'infrastructure sans fil standard. Il utilise des données de trace d'appareils mobiles d'individus, et un modèle d'estimation d'emplacement. Il forme le modèle d'estimation de localisation en utilisant les données de trace pour atteindre la localisation de la cible collaborative. Cette collaboration entre périphériques est au niveau des données et est supportée par un modèle. -MCS-Prédiction: un modèle de reconnaissance est conçu pour acquérir dynamiquement la connaissance de structure de la RCN pertinente pendant la propagation de la maladie. Sur ce modèle, un algorithme de prédiction est proposé pour prédire le paramètre R. i.e. le nombre de reproduction qui est utilisé pour quantifier la dynamique de la maladie pendant sa propagation. -MCS-Routing : un algorithme de navigation écologique ‘eRouting’ est conçu en combinant l'information de trafic temps réel et un modèle d'énergie/émission basé sur des facteurs représentatifs. Sur la base de l'infrastructure standard d'un système de trafic intelligent, l'information sur le trafic est collectée / Nowadays, there is an increasing demand to provide real-time information from the environment, e.g., the infection status of infectious diseases, signal strength, traffic conditions, and air quality, to citizens in urban areas for various purposes. The proliferation of sensor-equipped devices and the mobility of people are making Mobile Collaborative Sensing (MCS) an effective way to sense and collect information at a low deployment cost. In MCS, instead of just deploying static sensors in an interested area, people with mobile devices play the role of mobile sensors to sense the information of their surroundings, and the communication network (3G, WiFi, etc.) is used to transfer data for MCS applications. Typically, a MCS application not only requires each participant's mobile device to possess the capability of performing sensing and returning sensed results to a central server, but also requires to collaborate with other mobile and static devices. In order to make sensed results well represent the physical information of a target region, and well be suitable to a certain application, what kind of data can be used for different applications, and what kind of information needs to be included into the collected sensing data? Spatio-temporal data can be used by different applications to well represent the target region. In different applications, location and time information is two kinds of common information, and by using such information, the target region of an application is under comprehensive monitoring from the view of time and space. Different applications require different information to achieve different sensing purposes. E.g. in this thesis: i- MCS-Locating application: signal strength information needs to be included into the sensed data by mobile devices from signal transmitters; ii- MCS-Prediction application: the relationship between infecting and infected cases needs to be included into the sensed data by mobile devices from disease outbreak areas; iii- MCS-Routing application: real-time traffic and road information from different traffic roads, e.g., traffic velocity and road gradient, needs to be included into the sensed data by road-embedded and vehicle-mounted devices. With sensing the physical information of a target region, and making mobile and static devices collaborate with each other in mind, in this thesis three sensing based optimization applications are studied, and following four research works are conducted: - a MCS Framework is designed to facilitate the cooperativity of data collection, sharing, and analysis among different devices. Data is collected from different sources and time points. For deploying the framework into applications, relevant key challenges and open issues are discussed. - MCS-Locating: an algorithm LiCS (Locating in Collaborative Sensing based Data Space) is proposed to achieve target locating. It uses Received Signal Strength that exists in any wireless devices as location fingerprints to differentiate different locations, so it can be directly supported by off-the-shelf wireless infrastructure. LiCS uses trace data from individuals' mobile devices, and a location estimation model. It trains the location estimation model by using the trace data to achieve collaborative target locating. Such collaboration between different devices is data-level, and model-supported. - MCS-Prediction: a recognition model is designed to dynamically acquire the structure knowledge of the relevant RCN during disease spread. On the basis of this model, a prediction algorithm is proposed to predict the parameter R. R is the reproductive number which is used to quantify the disease dynamics during disease spread. - MCS-Routing: an eco-friendly navigation algorithm, eRouting, is designed by combining real-time traffic information and a representative factor based energy/emission model. Based on the off-the-shelf infrastructure of an intelligent traffic system, the traffic information is collected