In a networked control system, several nodes exchange information through a network, to achieve specific control goals and thus increasing network traffic. This affects the overall system performance. Several approaches try to satisfy requirements of both control and communication performance. Particularly, some methodologies have been proposed to save bandwidth. One of such methodologies has been scheduling, which has been studied in depth through the last decade. Commonly, the objective of using scheduling to save bandwidth is to accurately use the computing resources. This paper shows two scheduling strategies, one performing static scheduling and the other carrying out dynamic scheduling, in order to expose the advantages of using dynamic scheduling in an ad hoc implementation. Both strategies execute on a real-time distributed system, and both are able to modify the frequency of transmission as well as the periods of tasks in individual components. Hence, both of them tend to impact on the quality of performance of the system, due to network use. The first scheduling strategy modifies the periods of task, and network access is assigned through a static scheduling algorithm. On the other hand, the second strategy, schedulability, is dynamically achieved by controlling the rate of frequency transmission into a frequency region, bounded by minimum and maximum transmission rates. Numerical simulations are used as implementations of both strategies.

The proliferation of pollutants affects the world’s population, mainly those who live in large cities. Neurological and cardiovascular dysfunctions have a correlation with air particulate matter concentration, among other chronic diseases. Therefore, it is important to utilize different methods of analysis to build predictive models that can identify possible concentrations of contaminants in metropolitan areas. This work presents a methodology that will enable the forecasting of severe pollution contingencies using weather measurements as input variables. This predictive analytical technique combines several mathematical and statistical tools, which we refer to as predictive factor association (PFA). We perform principal component analysis on the samples to determine possible causal relationships and reduce dimensionality, resulting in orthogonal linear combinations of the variables called sample scores. For categorical variables, each linear combination probability is estimated using a window-based estimation technique or a machine learning algorithm to determine the class of a particular sample. For continuous variables, regression over the scores is carried out. The technique is used to predict environmental contingencies in Monterrey’s metropolitan area based on meteorological data with ${\mathrm{R}}^2$ between 0.7 and 0.8 and classification accuracies between 0.74 and 0.98.