D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks
AbstractThe reduction of sensor network traffic has become a scientific challenge. Different compression techniques are applied for this purpose, offering general solutions which try to minimize the loss of information. Here, a new proposal for traffic reduction by redefining the domains of the sensor data is presented. A configurable data reduction model is proposed focused on periodic duty–cycled sensor networks with events triggered by threshold. The loss of information produced by the model is analyzed in this paper in the context of event detection, an unusual approach leading to a set of specific metrics that enable the evaluation of the model in terms of traffic savings, precision, and recall. Different model configurations are tested with two experimental cases, whose input data are extracted from an extensive set of real data. In particular, two new versions of Send–on–Delta (SoD) and Predictive Sampling (PS) have been designed and implemented in the proposed data–domain reduction for threshold–based event detection (D2R-TED) model. The obtained results illustrate the potential usefulness of analyzing different model configurations to obtain a cost–benefit curve, in terms of traffic savings and quality of the response. Experiments show an average reduction of
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Leon-Garcia, F.; Palomares, J.M.; Olivares, J. D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks. Sensors 2018, 18, 3806.
Leon-Garcia F, Palomares JM, Olivares J. D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks. Sensors. 2018; 18(11):3806.Chicago/Turabian Style
Leon-Garcia, Fernando; Palomares, Jose M.; Olivares, Joaquin. 2018. "D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks." Sensors 18, no. 11: 3806.
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