Smart city is a constantly reshaped concept, embracing the future of dense metropolitan areas, with references to efficient and sustainable infrastructure, improving citizens’ quality of life and protecting the environment. A consensus on the Smart City philosophy is however that it will be primarily achieved by leveraging a clever integration of Information and Communication Technologies (ICT) in the urban tissue.
Indeed, ICTs are enabling an evolution from the current duality between the real world and its digitized counterpart to a continuum in which digital contents and applications are seamlessly interacting with classical infrastructures and services. Smart Cities are often described by the digital services that they provide, which are inherently dependent on dense measurements of the city environment and activities, the collection of these data, their processing into information, and their redistribution. The networking infrastructure plays therefore a critical role in enabling advanced services, in particular the wireless infrastructure supporting high user density and mobility.
From a wireless networking viewpoint, the digitization of cities can be seen as a paradigm shift extending the Internet of Things (IoT) to a citizen-centric model in order to leverage the massive data collected by pervasive sensors, connected mobiles or fixed devices, and social applications.
The Agora research team aims at contributing to the following consequent challenges of data collection wireless networks.
The deployment of dense networks is challenged by the scale of the problems and the versatility of the environment, with consequences on the optimization of the placement of both components and functionalities.
Data collection and distribution communication protocols, designed for IoT network architectures, need a coherent rethinking to face issues on both saturated cellular networks and multi-hop networks unable to cover large areas.
Exploiting the data carried by the network opens new questions on the network deployment and usage, by understanding the spatio-temporal dynamics of the users, and on in-network computations in order to reduce the traffic load or enhance the quality of the data.