Nowadays, climate change is one of the numerous factors affecting the agricultural sector. Optimising the usage of natural resources is one of the challenges this sector faces. For this reason, it could be necessary to locally monitor weather data and soil conditions to make faster and better decisions locally adapted to the crop. Wireless sensor networks (WSNs) can serve as a monitoring system for these types of parameters. However, in WSNs, sensor nodes suffer from limited energy resources. The process of sending a large amount of data from the nodes to the sink results in high energy consumption at the sensor node and significant use of network bandwidth, which reduces the lifetime of the overall network and increases the number of costly interference. Data reduction is one of the solutions for this kind of challenges. In this paper, data correlation is investigated and combined with a data prediction technique in order to avoid sending data that could be retrieved mathematically in the objective to reduce the energy consumed by sensor nodes and the bandwidth occupation. This data reduction technique relies on the observation of the variation of every monitored parameter as well as the degree of correlation between different parameters. This approach is validated through simulations on MATLAB using real meteorological data-sets from Weather-Underground sensor network. The results show the validity of our approach which reduces the amount of data by a percentage up to 88% while maintaining the accuracy of the information having a standard deviation of 2 $$^{\circ }$$ for the temperature and 7% for the humidity.

中文翻译：

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基于 K 预测的 WSN 智能农业数据缩减方法

如今，气候变化是影响农业部门的众多因素之一。优化自然资源的使用是该部门面临的挑战之一。因此，可能有必要在当地监测天气数据和土壤条件，以便更快、更好地做出适应当地作物的决策。无线传感器网络 (WSN) 可以作为这些类型参数的监控系统。然而，在 WSN 中，传感器节点的能源资源有限。从节点向接收器发送大量数据的过程导致传感器节点的高能耗和网络带宽的大量使用，从而缩短了整个网络的寿命并增加了代价高昂的干扰数量。数据缩减是应对此类挑战的解决方案之一。在本文中，研究数据相关性并结合数据预测技术，以避免发送可以通过数学方式检索的数据，以减少传感器节点消耗的能量和带宽占用。这种数据缩减技术依赖于对每个监测参数的变化以及不同参数之间相关程度的观察。这种方法通过使用来自 Weather-Underground 传感器网络的真实气象数据集在 MATLAB 上的模拟得到验证。结果显示了我们的方法的有效性，该方法将数据量减少了 88%，同时保持了信息的准确性，温度的标准偏差为 2 $$^{\circ }$$，温度的标准偏差为 7%湿度。