This paper discusses the architecture of a low cost Unmanned Surface Vehicle (USV) to be employed for the collection of crucial parameters about water quality in rivers, lakes or sea. The vehicle, called WeMo (Water Environmental Mobile Observer) has been realized exploiting off-the-shelf components and is provided with a modular array of sensors to measure chemical and physical parameters as well as to perform bathymetry. The low cost requirement is crucial since the vehicle is expected to be replicated in large quantities and then used for pervasive monitoring operations by providing it to local communities, administrations or even private stakeholders, in order to set up a sort of “Social Sensor Network”. In this sense, data analytics tools have also been introduced in order to automatically drive the vehicle along desired and suitable trajectories, and to process the collected data. These data can be used to estimate parameters of a mathematical model describing the ecological status of the monitored system. In particular, we apply an estimation procedure to a simple mathematical model of oxygen concentration in the water with explicit dependence on biophysical inputs. The estimation provides very satisfying performances, indeed the relative square error is less that 4∙10 -2. Moreover, once the vehicle is moving along a given trajectory, the status in the spatial domain can be reconstructed also in non monitored locations. The whole paper aims then at developing a complete monitoring ecosystem covering all the tasks of data collection, storage and analysis.

A new mathematical formulation of evolutionary game dynamics on networked populations is proposed. The model extends the standard replicator equation to a finite set of players organized on an arbitrary network of connections (graph). Classical results of multipopulation evolutionary game theory are used in combination with graph theory to obtain the mathematical model. Specifically, the players, located at the vertices of the graph, are interpreted as subpopulations of a multipopulation dynamical game. The members of each subpopulation are replicators, engaged at each time instant into 2-player games with the members of other connected subpopulations. This idea allows us to write an extended equation describing the game dynamics of a finite set of players connected by a graph. The obtained equation does not require any assumption on the game payoff matrices nor graph topology. Stability of steady states, Nash equilibria and the relationship of the proposed model to the standard replicator equation are discussed. The dynamical behavior of the model over different graphs is also investigated by means of extended simulations.

Our brain is a complex system of interconnected regions spontaneously organized into distinct networks. The integration of information between and within these networks is a continuous process that can be observed even when the brain is at rest, i.e. not engaged in any particular task. Moreover, such spontaneous dynamics show predictive value over individual cognitive profile and constitute a potential marker in neurological and psychiatric conditions, making its understanding of fundamental importance in modern neuroscience. Here we present a theoretical and mathematical model based on an extension of evolutionary game theory on networks (EGN), able to capture brain's interregional dynamics by balancing emulative and non-emulative attitudes among brain regions. This results in the net behavior of nodes composing resting-state networks identified using functional magnetic resonance imaging (fMRI), determining their moment-to-moment level of activation and inhibition as expressed by positive and negative shifts in BOLD fMRI signal. By spontaneously generating low-frequency oscillatory behaviors, the EGN model is able to mimic functional connectivity dynamics, approximate fMRI time series on the basis of initial subset of available data, as well as simulate the impact of network lesions and provide evidence of compensation mechanisms across networks. Results suggest evolutionary game theory on networks as a new potential framework for the understanding of human brain network dynamics.