In this paper we introduce a framework for detecting anomalies in the clocks of the different components of a network of sensor stations connected with a central server for measuring of air quality. Local clocks of sensor stations can be advanced/delayed with respect to the central server clock and this situation provokes the inaccuracy in the interpretation of the collected data. We propose a novel approach, supported by a formal representation of the network using fuzzy-timed automata, to precisely represent the expected behaviour of each component of the network. Using fuzzy logic concepts, we can specify admissible mismatches between the clocks. In addition, we apply complex event processing (CEP) technology in order to automatically detect situations of interest while processing the massive amount of data transferred across the network. Specifically, we have designed a collection of CEP patterns that trigger alarms when unexpected differences are observed. We also report the results obtained from the application of our approach to the network during December 2016.