My interests are in computer networks and mobile analytics with emphasis on domains related to mobile wireless networking, human behavior modeling, Internet of Everything (IoE), and smart cities. My research addresses the design of suitable-by-design and perceptive networking solutions, leveraging in-network human behavior (e.g., mobility, content demand, interactions) for self-organization and mobile networking.
More recently, I have been interested in studying the way people connect to, interact with, or impact mobile internet edge networks. I believe the understanding of human behavior and their network interactions have to become part integrant of networking solutions’ design. My main goals here are (1) to improve network perception on social norms/structure shaping the behavior of individuals within the network and (2) to accordingly provide the required support for smarter and human-aware decision-making in protocol/architecture/service design. My current contributions rely on the characterization and modeling of human behaviors (i.e., mobility, interactions or content demand) as well as on the leveraging of patterns of behaviors in data offloading or trajectory reconstruction solutions.
This brings the idea of a perceptive networking design practice where the network is assigned with the human like capability of observation, interpretation, and reaction to daily life features and entities involving IoT devices.
One fascinating behaviour of human beings attracting several research fields is their mobility. Understanding human mobility has many applications in diverse area (spread of diseases, city planning, targeted product advertisements, personalized travel itineraries, epidemic propagation,etc), but the biggest potential of human mobility prediction lays in its use to solve major issues our society faces today, in areas related to: e.g., urban planning, cybersecurity, and public safety. Related to this latter, refer to the SafeCityMap project description hereafter.
Related to COVID-19 crisis and the Inria COVID-19 mission, I am proud to coordinate and to announce the initial scientific contributions of the SafeCityMap project, produced in collaboration with brilliant researchers I have the opportunity to work with:
Haron C. Fantecele (LNCC and Inria TRiBE) and Solohaja Rabenjamina (Inria AGORA), Razvan Stanica (INSA Lyon and Inria AGORA), and Artur Ziviani (LNCC, Brazil).
SafeCityMap overview. Covid-19 related lockdown restrictions highly perturbed our mobility patterns and use of urban spaces. In SafeCityMap 2020, we investigated, under different perspectives, the impacts caused by harsh lockdown conditions in Spring 2020. As such, SafeCityMap worked toward the tracking of the evolution in space and time of population habits in mobility. This study allowed us to investigate the impact the 1st lockdown had in the city of Paris and neighbor departments.
We believe the modeling of such mobility patterns and the corresponding lockdown impact understanding may provide useful intuitions on the epidemic spread, on different areas of a city. This latter is related to the observation that high population concentration at certain hours and geographical areas intuitively increases the probability of agglomeration (in special, in transports or public spaces) and consequently, the contamination and propagation risks. Although this risk is particularly probable in small and highly dense geographical zones, this claim requires deep investigations from the epidemiological point of view: This is the scope of SafeCityMap 2021-2022, currently on-going.
(Photo: Reuters)
