We consider the design of distributed algorithms that govern the manner in which agents contribute to a social sensing platform. Specifically, we are interested in situations, where fairness among the agents contributing to the platform is needed. A notable example is the platforms operated by public bodies, where fairness is a legal requirement. The design of such distributed systems is challenging due to the fact that we wish to simultaneously realize an efficient social sensing platform but also deliver a predefined quality of service to the agents (for example, a fair opportunity to contribute to the platform). In this article, we introduce iterated function systems (IFSs) as a tool for the design and analysis of systems of this kind. We show how the IFS framework can be used to realize systems that deliver a predictable quality of service to agents, can be used to underpin contracts governing the interaction of agents with the social sensing platform, and which are efficient. To illustrate our design via a use case, we consider a large, high-density network of participating parked vehicles. When awoken by an administrative center, this network proceeds to search for moving missing entities of interest using RFID-based techniques. We regulate which vehicles are actively searching for the moving entity of interest at any point in time. In doing so, we seek to equalize vehicular energy consumption across the network. This is illustrated through simulations of a search for a missing Alzheimer’s patient in Melbourne, Australia. The experimental results are presented to illustrate the efficacy of our system and the predictability of access of agents to the platform independent of initial conditions.

Poincaré's geometric representation, while historically fundamental in dynamical system analysis, faces challenges with high-dimensional and uncertain systems in modern engineering and data analysis. This article extensively explores Koopman Operator Theory (KOT) and Dynamic Mode Decomposition (DMD) within data-driven science and engineering and advocates for a conceptual shift toward observable dynamics, emphasizing KOT's capacity to capture nonlinear dynamics in infinite-dimensional space. The potential practical applications of Koopman-based methods are highlighted. Leveraging Poincaré's framework, the limitations of traditional methods are discussed. The review also addresses the growing significance of data-driven methodologies for modelling, predicting, and controlling complex systems.

This study introduces artificial intelligence models for the first time in the Republic of Ireland to forecast particulate matter (PM₁₀). By integrating local environmental data with cutting-edge predictive algorithms, this research specifically targets high-impact urban areas in Ireland. The models not only account for complex air quality temporal patterns and meteorological variables but also introduce region-specific calibrations that have not been explored in previous air quality studies. Several state-of-the-art machine learning models: such as XGBoost, CatBoost, ANN, LSTM, Bi LSTM, and an adaptable Ensemble model are trained using available data. Interestingly, climatic variables have a minimum impact on the PM₁₀ concentrations in the tested areas of Ireland. However, they show good correlations with PM_2.5 and NO₂ concentration levels. The performance of the models is tested using widely accepted performance indicators (Coefficient of Determination, Root Mean Square Error, Mean Squared Error, and Mean Absolute Error). Based on the outcome of the models, the PM₁₀ forecast for the next 24 h is presented. The findings of this research can be effectively used to enhance policy decisions to minimize the expected rising pollution levels in the future.