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Sensors 2018, 18(11), 3806; https://doi.org/10.3390/s18113806

D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks

Department of Electronics and Computer Engineering, Edificio Leonardo da Vinci, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, Spain
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Received: 17 September 2018 / Revised: 23 October 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2018)
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Abstract

The 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 76 % of network packages with an error of less than 1%. In addition, experiments show that the methods designed under the proposed D2R–TED model outperform the original event–triggered SoD and PS methods by 10 % and 16 % of the traffic savings, respectively. This model is useful to avoid network bottlenecks by applying the optimal configuration in each situation. View Full-Text
Keywords: WSN; event detection; data compression WSN; event detection; data compression
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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.

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