Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to\r\nthe fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit\r\nlong-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a\r\npoint-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously\r\nsample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce\r\nthe estimation error while conserving the networkââ?¬â?¢s energy. In this paper, we present a novel method for sensor data\r\nacquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The\r\nmethod, using a concept of ââ?¬Ë?virtual clusters,ââ?¬â?¢ forms groups of sensor nodes with the same spatial and temporal\r\nproperties. Two algorithms are used to provide functionality. The ââ?¬Ë?distributed formationââ?¬â?¢ algorithm automatically\r\nforms and classifies the virtual clusters. The ââ?¬Ë?round robin sample schemeââ?¬â?¢ schedules the virtual clusters to sample the\r\nevent signals in turn. The estimation error and the energy consumption of the method, when used with a generalized\r\nsensing model, are evaluated through analysis and simulation. The results show that this method can achieve an\r\nimproved signal estimation while reducing and balancing energy consumption.
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