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In this paper, we propose an integrated cross-layer data reporting scheme focusing on communication protocols in wireless sensor networks. The scheme operates based on the various application demands and available resources to provide an adaptive quality of service (QoS). The goal of this work is to achieve the expected information quality at the end system by efficiently controlling data reporting...
In this paper, asynchronous sampling is proposed as a novel approach to improve the information quality of sensory data through shifting the sampling moments of sensors from each other. The exponential correlation model and the entropy model for the sensory data are introduced to quantify their information quality. An asynchronous sampling strategy, EASS, is presented accordingly to assign equal time...
Combining different machine learning models (decision fusion) has been shown to be an effective method for estimating the underlying physical mechanism by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. To be effective, decision fusion requires that the different models provide some degree of complementary information...
Intensive research has focused on redundance reduction in wireless sensor networks among sensory data due to the spatial and temporal correlation embedded therein. In this paper, we propose a novel approach termed asynchronous sampling that complements existing study. The key idea of asynchronous sampling is to spread the sampling times of the sensor nodes over the time line instead of performing...
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