Event-Driven Top-k Queries in Sensor Networks with Multi Microenvironments

In the emerging area of sensor-based systems, a significant challenge is to develop reliable, energy efficient methods to extract useful information from the distributed sensory data. Existing top-k query algorithms of sensor networks are only applicable to the sensor networks with single Microenvironment (ME). Besides, these approaches rely on using raw sensor readings, which handling these raw sensory data requires large amount of data transmission and is memory-consuming. In this paper we highlight the multiple MEs in sensor networks and propose a novel event-driven approximate top-k query framework based on fuzzy method. Firstly, membership function of fuzzy method is introduced to describe the global potential event confidence. Subsequently, non-uniform membership degree subranges based linguistic variables instead of numeric sensor readings are used for in-network top-k event fusion. Also two distributed event-driven approximate top-k query algorithms are devised. Theoretical analysis as well as extensive simulations based on synthetic and real data sets show that our framework reduces the data transmission significantly and the query results are more interpretable with quality guarantees.