This work proposes a data sampling algorithm for smart cities applications based on sensor network infrastructure. Our algorithm identifies the sensor data behavior through the Causality Complexity-Entropy Plane and performs data reduction by removing redundant data without losing the system’s properties. For this, we recognize the systems’ dynamic changes in real-time through a delimiter, named Maximum Complexity Point (MCP). Thus, we determine when to update the sampling period to maximize the system’s information content, i.e., the statistical complexity quantifier. To confirm the sampling adaptability, we apply our method in three different chaotic attractors: Rossler, Lorenz, and ${B}_{7}$ . We compared our solution with two other sampling algorithms: (i) random histogram-based sampling and the ${L}$ algorithm. We use the K-S test, the average Data Error, and the Causality Complexity-Entropy Plane to compare the results. Using our sampling approach, we observed K-S test distances less than 3% for chaotic maps and 1% for natural environments data. The best results were in the Data Error, showing an average error rate up to 13.4% lower when evaluating chaotic data and 15.7% lower when evaluating natural environments. Regarding the dispersion of points in the Causality Complexity-Entropy Plane, the sampled time-series reached regions of higher statistical complexity, indicating that they preserved information content, hence the original data’s dynamics.

中文翻译：

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基于复杂熵平面的智能传感应用数据采样算法


这项工作提出了一种基于传感器网络基础设施的智慧城市应用数据采样算法。我们的算法通过因果复杂性熵平面识别传感器数据行为，并通过删除冗余数据来执行数据缩减，而不会丢失系统的属性。为此，我们通过一个称为最大复杂度点（MCP）的分隔符实时识别系统的动态变化。因此，我们确定何时更新采样周期以最大化系统的信息内容，即统计复杂度量词。为了确认采样适应性，我们将我们的方法应用于三种不同的混沌吸引子：Rossler、Lorenz 和 ${B}_{7}$ 。我们将我们的解决方案与其他两种采样算法进行了比较：(i) 基于随机直方图的采样和 ${L}$ 算法。我们使用 KS 检验、平均数据误差和因果复杂度-熵平面来比较结果。使用我们的采样方法，我们观察到混沌地图的 KS 测试距离小于 3%，自然环境数据的 KS 测试距离小于 1%。最好的结果是数据错误，显示评估混沌数据时平均错误率降低了 13.4%，评估自然环境时平均错误率降低了 15.7%。关于因果复杂性-熵平面中点的分散性，采样的时间序列达到了统计复杂性较高的区域，表明它们保留了信息内容，因此保留了原始数据的动态。