Extracting semantic event information from distributed sensing devices using fuzzy sets

Event detection is a central task for distributed sensor systems and detecting forthcoming events in a timely manner is the main way of minimizing their possibly damaging effects. The state-of-the-art methods for event description and detection always rely on using crisp raw sensory data, which requires huge data transmission as well as is time-consuming. However, even a centralized processing manner cannot ensure accurate event decision due to the imprecision and uncertainty of raw sensor readings. In many cases, users do not care about the raw sensory data or the data format used for in-network processing, but instead they are concerned with the semantic event information, such as “how serious is it?” and “where will it occur?” In addition, the main technique employed by the existing solution for detecting problems is collaboration with neighbors, which requires massive data exchange between neighbors that is highly intensive in terms of wireless communication. In this paper, we introduce an energy-efficient, reliable semantic event information extraction framework using fuzzy sets. Linguistic event variables instead of raw sensor data are used for event information transmission and fusion, and fuzzy method-based semantic event information filtering and fusion algorithms are proposed. Extensive evaluations based on both real-life and synthetic data sets demonstrated that our framework only incurs a small communication cost and it returns interpretable event information with guaranteed accuracy.