In the traditional agricultural wireless sensor networks (WSNs), there is a large amount of redundant data and high latency on critical events (CEs) for data collection systems, which increases the time and energy consumption. In order to overcome these problems, an effective edge computing (EC) enabled data collection approach for CE in smart agriculture is proposed. First, the key features data types (KFDTs) are extracted from the historical dataset to keep the main information on CEs. Next, the KFDTs are selected as the collection data type of the software-defined wireless sensor network (SDWSN). Then, the event types are decided by searching the minimum average variance between the sensing data of active nodes and the average value of the key feature data obtained by EC. Furthermore, the sensing nodes are driven to sense the event-related data with a consideration of latency constraints by the SDWSN servers. A real-world testbed was set up in a smart greenhouse for experimental verification of the proposed approach. The results showed that the proposed approach could reduce the number of needed sensors, sensing time, collection data volume, communication time, and provide the low latency agricultural data collection system. Thus, the proposed approach can improve the efficiency of CE sensing in smart agriculture.

中文翻译：

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基于SDWSN的农业物联网中关键事件的有效边缘辅助数据收集方法

在传统的农业无线传感器网络（WSN）中，数据收集系统存在大量冗余数据，并且在关键事件（CE）上存在较高的延迟，从而增加了时间和能耗。为了克服这些问题，提出了一种有效的边缘计算（EC）的智能农业中用于CE的数据收集方法。首先，从历史数据集中提取关键要素数据类型（KFDT），以将主要信息保留在CE上。接下来，选择KFDT作为软件定义的无线传感器网络（SDWSN）的收集数据类型。然后，通过搜索活动节点的感测数据与EC获得的关键特征数据的平均值之间的最小平均方差来确定事件类型。此外，SDWSN服务器在考虑延迟约束的情况下驱动感应节点感应与事件相关的数据。在智能温室中建立了一个真实世界的试验台，以对所提出的方法进行实验验证。结果表明，该方法可减少所需传感器的数量，传感时间，采集数据量，通信时间，并提供低延迟的农业数据采集系统。因此，提出的方法可以提高智能农业中CE感测的效率。通讯时间，并提供低延迟的农业数据收集系统。因此，提出的方法可以提高智能农业中CE感测的效率。通讯时间，并提供低延迟的农业数据收集系统。因此，提出的方法可以提高智能农业中CE感测的效率。