Projet de recherche doctoral numero :4296


Date depot: 1 janvier 1900
Titre: In-network collaborative crowd-Xing
Encadrante : Françoise SAILHAN (CEDRIC)
Directrice de thèse: Valérie ISSARNY (Inria-Paris (ED-130))
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini

Resumé: Sensor and actuator networks, possibly wireless, have drastically evolved over the last few years, with rich sensors being embedded in most devices and deployed everywhere, as highlighted by the Internet of Things vision. However, in spite of significant advances, the key challenges of these systems arise from largely the same attributes as those of early-envisioned mobile systems: relative resource-poverty in terms of computation and communication, variable and unreliable connectivity, and limitations imposed by a finite energy source. These remain true even though modern mobile devices are significantly more powerful compared to their ancestors; the work we expect them to do has increased and will keep increasing, and the computation and storage abilities available through fixed infrastructure such as the cloud are larger by order of magnitudes than any single mobile device. Following, the design of algorithms and protocols to efficiently coordinate the sensing, processing, and actuation capabilities of the large number of mobile devices in future systems is a core area of MiMove's research. The focus of MiMove's research interests then lies mostly in the systems resulting from the increased popularity of sensor-equipped smart devices that are carried by people, which has led to the promising field of mobile phone sensing or mobile crowd-sensing. The paradigm is powerful, as it allows overcoming the inherent limitation of traditional sensing techniques that require the deployment of dedicated fixed sensors. In this context, we are specifically interested in the challenges rising from the potentially very large scale of mobile crowd-sensing, combined with the openness, heterogeneity and dynamicity of the related sensing and actuation environment. Our target is to raise mobile crowd-sensing to a reliable means of sensing world phenomena, with a special focus on urban phenomena as part of the development of digital cities. We believe that the right way to achieve this is by enabling scalable and quality data collection, i.e., maximizing the effectiveness of collective sensing, rather than gathering massive raw data that require costly post-processing for producing meaningful knowledge. To this end, we focus on data and control coordination among the sensing actors.

Doctorant.e: Du Yifan