by Valérie Issarny

The Inria Project Lab CityLab@Inria which is currently under creation, studies information and communications technology (ICT) solutions that promote social and environmental sustainability and facilitate the transition to Smart Cities. The Lab places a strong emphasis on multi-disciplinary research, integrating relevant scientific and technology studies from sensing up to analytics and advanced applications. The idea is that the research environment will mirror the predicted Smart City Systems of Systems. A central concern of the Lab is running experiments so that we are able to investigate proposed approaches in real-life settings.

Our Motivation
The world is in the midst of an immense population shift, as people move from rural to urban areas. This has led governments, businesses and communities to rely on technologies, in particular, information and communication technologies (ICT), to overcome the challenges posed by this rapid urbanization. As a result, various academic, industry and city-led ICT initiatives have been launched in recent years with a view to building “smart urban infrastructures”. These provide detailed information about the functioning of a city to its citizens and businesses, thereby enabling them to better understand their infrastructures and resources and thus, improve the management of them.

While environmental and economical sustainability have been on the ICT research agenda for some time, the equally important social sustainability has been overlooked in the context of Smart Cities. Indeed, cities are first and foremost places for people, and thus building cohesive, inclusive and flourishing communities should be at the forefront of our research agenda. Without the right social infrastructure in place, problems such as isolation, mental health problems, anti-social behaviours and crime are more likely to arise, pushing communities into decline.

Research Themes and Challenges
The objective of the CityLab@Inria is to study ICT-based Smart City systems from supporting “sensing” systems up to advanced data analytics and new services for the citizens that promote social and environmental sustainability.

Specifically, CityLab@Inria brings together Inria project teams in networking (FUN and URBANET), distributed software systems (ARLES-MiMove and MYRIADS), data management (DICE, OAK and SMIS) and data analytics (CLIME and WILLOW) to investigate the following research questions:

  • How can urban-scale sensing that needs to combine both physical and social sensing be effectively sustained while accounting for the requirements associated with the target network? These include scalability, energy-efficiency and privacy preservation. Sensing the ‘city pulse’ brings challenges for the supporting data management which must scale-up, as well as integrate highly heterogeneous data of various qualities. The literature is rich with papers addressing these concerns individually. However, they are seldom tackled together, especially while simultaneously considering the urban scale. Our approach to overcome these challenges lies in the study of scalable protocols from the networking up to the middleware layers, together with advanced techniques for privacy enhancement and semantic-aware data management.
  • How can the data be aggregated so that the evolution of a city can be not only understood, but also anticipated and perhaps even influenced? Data analytics is at the core of Smart Cities, making big data available to us through sensing. Based on the open data trend, this can become very useful in providing knowledge on the cities. It is a very active area of research. However, numerous open problems remain regarding how large-scale data is analyzed and the uncertainty associated with urban-scale, crowd-sourced data collection must also be overcome. Our contribution in this area leverages advanced research results on data assimilation and machine learning.
  • While city-scale sensing and data analytics are two complementary aspects of Smart City systems, they are also inter-related as one may adequately inform the design of the other. Therefore, it is essential to design crosscutting architectures for Smart City systems based on the comprehensive integration of the custom data sensing and analytics that we will investigate.
  • Finally, the Smart City vision will only come true if it is accompanied by concrete urban services that do make our (future) cities sustainable and agile. A number of application areas have been suggested and these include smart energy, smart health and smart transportation. However, we are still lacking disruptive services that will contribute to making our cities better places to live while also addressing the central challenge of growth. One important question is how the use of city-scale sensing can impact city governance, particularly its social dimension? Our research will be guided by the study of new urban services which will be undertaken in close collaboration with external partners (especially city representatives) as well as researchers from the social science field.

While the scientific focus of CityLab@Inria is broad, the Lab’s research leverages relevant effort within Inria project-teams that is further revisited as well as integrated to meet the challenges of smart cities.
An International Lab

CityLab@Inria research builds on the collaborative effort of the international research community, especially the Inria@SiliconValley program. Indeed, a key characteristic of the CityLab@Inria Lab is its international dimension which began with the Paris-San Francisco cooperation agreement toward smarter cities . This agreement, signed on March 20, 2013, is dedicated to developing smarter cities and includes support for targeted research programs among which is the Joint Inria-CITRIS CityLabs Program.

Links:
CityLab@Inria Project Lab on Smart Cities: http://citylab.Inria.fr
Inria@Silicon Valley program: https://project.inria.fr/siliconvalley/

Please contact:
Valérie Issarny, Inria@Silicon Valley, E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Next issue: January 2025
Special theme:
Large-Scale Data Analytics
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