Zhang Ming-Zheng, Wang Liang-Min, Xiong Shu-Ming
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2020 Mar 26;20(7):1836. doi: 10.3390/s20071836.
The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users' requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the -means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes.
传感器云技术的出现缓解了传统无线传感器网络（WSN）在能量、存储和计算方面的局限性，这在各种农业物联网（IoT）应用中具有巨大潜力。在传感器云环境中，虚拟传感器配置是一项重要任务。它根据用户请求选择物理传感器来创建虚拟传感器。考虑到户外多变的气象环境，本文利用机器学习进行数据分析，提出了一种基于测量相似度的虚拟传感器配置方案。首先，为了区分变化趋势，我们使用历史数据将所有物理传感器分类为几个类别。然后，对每个类别利用K均值聚类算法对相似度高的物理传感器进行聚类。最后，从每个聚类中选择一个代表性物理传感器来创建相应的虚拟传感器。实验结果表明，与基准方案相比，我们的方案在能源效率、网络寿命和数据准确性方面有所改进。
