A New Fuzzy Query Processing System in Wireless Sensor Networks

The task of acquiring information from sensor networks through generating queries is one of the most important issues in wireless sensor networks. The structure of traditional query processing systems requires defining query criteria in the form of crisp predicates with explicit and numerical thresholds, leading them to be processed in a certain manner. The inherent uncertainty and imprecision of sensor data call for a new approach towards them. Since fuzzy theory provides a toolbox to capture the imprecision associated with both data and query, in this paper, a new system for processing fuzzy queries in wireless sensor networks is introduced. In this system, in addition to presenting a new structure for fuzzy queries, a new algorithm is introduced for processing fuzzy queries in sensor networks. Simulation results indicate that accuracy and precision of the results obtained from fuzzy queries are higher than traditional ones, whereas there is no significant difference between the two regarding their energy consumption

Bayesian Reasoning for Sensor Group-Queries and Diagnosis

Abstract. As large-scale sensor networks are being deployed with the objective of collecting quality data to support user queries and decision-making, the role of a scalable query model becomes increasingly critical. An effective query model should scale well with large network deployments and address user queries at specified confidence while maximizing sensor resource conservation. In this paper, we propose a group-query processing scheme using Bayesian Networks (BNs). When multiple sensors are queried, the queries can be processed collectively as a single group-query that exploits inter-attribute dependencies for deriving cost-effective query plans. We show that by taking advantage of the Markov-blanket property of BNs, we can generate resource-conserving group-query plans, and also address a new class of diagnostic queries. Through empirical studies on synthetic and real-world datasets, we show the effectiveness of our scheme over existing correlation-based models.