An efficient environmental monitoring system adopting data fusion, prediction, & fuzzy logic

Environmental monitoring plays an important role in the identification of abnormalities in the environment's characteristics. Abnormalities are related to negative effects that, consequently, heavily affect human lives. A number of sensors could be placed in a specific area and undertake the responsibility of monitoring environment's characteristics for specific phenomena. Sensors report back their measurements to a central system that is capable of situational reasoning. Accordingly, the system, through decision making, responds to any event related to the observed phenomena. In this paper, we propose a mechanism that builds on top of the sensors measurements and derives the appropriate decisions for the immediate identification of events. The proposed system adopts data fusion and prediction (time series regression) statistical learning methods for efficiently aggregating sensors measurements. We also adopt Fuzzy Logic for handling the uncertainty on the decision making on the derived alerts. We perform a set of simulations over real data and report on the advantages and disadvantages of the proposed system.