Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

There is a growing need for smart methodologies to enable rapid and reliable sensing, analysis and presentation of information regarding emergency preparedness, public safety, public health, environment and quality of life in urban environments. These methodologies are needed to improve urban sensing and ensure a smart crowdsensing infrastructure. Examples include implicit recruitment of smartphone users to sense traffic conditions, environmental pollution, noise levels or temperature, capture images at certain locations, and analyze collected data rapidly so that immediate reports can be prepared for citizens and/or authorities. However, in crowdsensing, data sources are noisy, unreliable, erroneous, and largely unknown. In our previous work, we addressed the user recruitment problem via auction-theoretic and game-theoretic approaches with the objective of maximizing trustworthiness of the recruited user set in crowdsensing applications. We have shown that the use of statistical and collaborative reputation systems can significantly improve the usefulness of acquired data. Despite the progress in crowdsensing research, a reliable, secure, scalable and robust framework that can be used to effectively collect, quantify and analyze data is lacking. As anyone is allowed to participate in urban sensing upon downloading a mobile app, the pool of sources is not examined thoroughly, and the reliability of individual participants is generally unknown to the data collector. Hence, particularly in mission critical applications such as public safety or emergency preparedness, the problem of refining reliable information becomes challenging. The objective of this research program is to lay the foundations for mobile social network-based data acquisition in urban settings and address its reliability, scalability, and privacy challenges. Indeed, mobile social network denotes interaction links between users, which can be in various forms such as data communications, co-location and mobility. The research will leverage methods and results from the fields of network science, data analytics, modelling and optimization, and multidisciplinary analysis and design. The research findings will be transformative for social network-assisted urban sensing systems, especially in sustainability and public safety areas owing to the following novel components: 1) continuous social identification and authentication algorithms for effective user recruitment, 2) novel data fusion algorithms to improve reliability, 3) scalable implementation of fog computing architecture to handle unexpectedly increasing volume and velocity of crowd-sensed data, 4) cloaked contextual patterns to improve privacy of the participants. The research findings will lead to a novel framework consisting of social network-driven urban sensing system offering scalability, trustworthiness and privacy.