Department of Electronics and Computer Engineering, Edificio Leonardo da Vinci, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, Spain.
Sensors (Basel). 2018 Nov 6;18(11):3806. doi: 10.3390/s18113806.
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.
传感器网络流量的减少已成为一个科学挑战。为此应用了不同的压缩技术，提供了尝试最小化信息丢失的通用解决方案。在这里，提出了一种通过重新定义传感器数据域来减少流量的新方法。提出了一种可配置的数据减少模型，该模型侧重于具有由阈值触发的事件的周期性占空比传感器网络。本文分析了该模型在事件检测方面产生的信息丢失，这是一种不常见的方法，导致了一组特定的指标，这些指标可以根据流量节省、精度和召回率来评估模型。使用两个实验案例测试了不同的模型配置，其输入数据是从大量实际数据中提取的。特别是，已经为基于阈值的事件检测(D2R-TED)模型设计并实现了两种新版本的Send-on-Delta (SoD)和Predictive Sampling (PS)。所获得的结果说明了分析不同模型配置以获得成本效益曲线的潜在有用性，特别是在流量节省和响应质量方面。实验表明，网络数据包的平均减少了 76%，误差小于 1%。此外，实验表明，在所提出的 D2R-TED 模型下设计的方法在流量节省方面分别比原始的事件触发的 SoD 和 PS 方法高出 10%和 16%。该模型通过在每种情况下应用最佳配置，有助于避免网络瓶颈。
