Currently, mobile computing is mandating or influencing the direction of new developments in information technology. The high level of adoption that mobile devices have among individuals allows for multiple opportunities for new developments applicable to academic communities, governments and businesses. Data of various types can be collected in a crowd-sourced manner. As such, this thesis examines the collection and application of data collected through a purpose-designed app relying on Bluetooth and geo-location technologies on mobile devices. Through three distinct development iterations and using Bluetooth connectivity, information about connectivity to other mobile devices can be obtained, and in this way the number, type, and device names of “connecting” devices are gathered and stored. Another interesting aspect associated with this type of data collection is that the mobile device may be either moving or stationary during the data collection process. Information can be collected and mined to help map real-life events such as traffic patterns or crowd movement within mass gatherings, as well as ethereal social interactions, and these data can in turn be used as input to various models and simulators. When geo-location technologies are incorporated, a higher level of detail can be obtained on the location of devices. This technology allows for mapping movement and contacts made between people, allowing for the gathering of more detailed social patterns of individuals. As part of this study, the technology developed using Bluetooth connectivity and geo-location is then taken to an additional iteration to develop a mobile system that is able to find and establish direct connections with other individuals and initiate real-life interactions. The work demonstrates that mobile technologies can provide a broad framework of action for the generation and collection of valuable data that can be used for behavioural studies, simulations and other type of research that involves real-life social interactions.

Le Web des données offre un environnement de partage et de diffusion des données, selon un cadre particulier qui permet une exploitation des données tant par l’humain que par la machine. Pour cela, le framework RDF propose de formater les données en phrases élémentaires de la forme (sujet, relation, objet) , appelées triplets. Les bases du Web des données, dites bases RDF, sont des ensembles de triplets. Dans une base RDF, l’ontologie – données structurelles – organise la description des données factuelles. Le nombre et la taille des bases du Web des données n’a pas cessé de croître depuis sa création en 2001. Cette croissance s’est même accélérée depuis l’apparition du mouvement du Linked Data en 2008 qui encourage le partage et l’interconnexion de bases publiquement accessibles sur Internet. Ces bases couvrent des domaines variés tels que les données encyclopédiques (e.g. Wikipédia), gouvernementales ou bibliographiques. L’utilisation et la mise à jour des données dans ces bases sont faits par des communautés d’utilisateurs liés par un domaine d’intérêt commun. Cette exploitation communautaire se fait avec le soutien d’outils insuffisamment matures pour diagnostiquer le contenu d’une base ou pour interroger ensemble les bases du Web des données. Notre thèse propose trois méthodes pour encadrer le développement, tant factuel qu’ontologique, et pour améliorer l’interrogation des bases du Web des données. Nous proposons d’abord une méthode pour évaluer la qualité des modifications des données factuelles lors d’une mise à jour par un contributeur. Nous proposons ensuite une méthode pour faciliter l’examen de la base par la mise en évidence de groupes de données factuelles en conflit avec l’ontologie. L’expert qui guide l’évolution de cette base peut ainsi modifier l’ontologie ou les données. Nous proposons enfin une méthode d’interrogation dans un environnement distribué qui interroge uniquement les bases susceptibles de fournir une réponse. / The web of data is a mean to share and broadcast data user-readable data as well as machine-readable data. This is possible thanks to rdf which propose the formatting of data into short sentences (subject, relation, object) called triples. Bases from the web of data, called rdf bases, are sets of triples. In a rdf base, the ontology – structural data – organize the description of factual data. Since the web of datacreation in 2001, the number and sizes of rdf bases have been constantly rising. This increase has accelerated since the apparition of linked data, which promote the sharing and interlinking of publicly available bases by user communities. The exploitation – interrogation and edition – by theses communities is made without adequateSolution to evaluate the quality of new data, check the current state of the bases or query together a set of bases. This thesis proposes three methods to help the expansion at factual and ontological level and the querying of bases from the web ofData. We propose a method designed to help an expert to check factual data in conflict with the ontology. Finally we propose a method for distributed querying limiting the sending of queries to bases that may contain answers.

Participatory Sensing (PS) systems rely on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated in their cellular phones. Users agree to use their cellular phone resources to sense and transmit the data of interest because these data will be used to address a collective problem that otherwise would be very difficult to assess and solve. However, this new data collection paradigm has not been very successful yet mainly because of the lack of incentives for participation and privacy concerns. Without adequate incentive and privacy guaranteeing mechanisms most users will not be willing to participate. This thesis concentrates on incentive mechanisms for user participation in PS system. Although several schemes have been proposed thus far, none has used location information and imposed budget and coverage constraints, which will make the scheme more realistic and efficient. A recurrent reverse auction incentive mechanism with a greedy algorithm that selects a representative subset of the users according to their location given a fixed budget is proposed. Compared to existing mechanisms, GIA (i.e., Greedy Incentive Algorithm) improves the area covered by more than 60 percent acquiring a more representative set of samples after every round, i.e., reduces the collection of unnecessary (redundant) data, while maintaining the same number of active users in the system and spending the same budget.

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