Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Faculty of Languages and Linguistics, University of Malaya, Kuala Lumpur, Malaysia.
PLoS One. 2024 Nov 7;19(11):e0308845. doi: 10.1371/journal.pone.0308845. eCollection 2024.
The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor's own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks.
农业无线传感器网络（WSN）具有运行周期长、覆盖范围广的特点。为了尽可能覆盖更大的区域，农场通常在同一区域的不同位置部署多个监测设备。由于设备类型不同，监测数据会有很大差异，而且过多的监测节点也会降低网络的效率。尽管已经有一些关于数据融合算法的研究，但它们存在忽略时间序列动态变化、抗干扰能力弱、数据波动处理能力差等问题。因此，本研究设计了一种用于农业无线传感器网络中最优节点跟踪的数据融合算法。通过在动态时间规整算法中引入动态弯曲距离来代替模糊关联算法中的绝对距离，并结合传感器自身的可靠性和关联度作为加权融合权重，对模糊关联算法进行了改进。最后，对三种算法进行了多温度传感器数据融合测试。与卡尔曼滤波、算术平均值和模糊关联算法相比，改进后的数据融合算法的平均值为 29.5703，接近其他三种算法的平均值，表明数据分布更加均匀。其极差为 8.9767，比其他三种算法小 10.04%、1.14%和 9.85%，表明在处理异常值时更稳健。其方差为 2.6438，比其他三种算法小 2.82%、0.65%和 0.27%，表明其更稳定，数据波动更小。结果表明，本研究提出的算法具有更高的融合精度和更好的鲁棒性，可以获得真正反馈农业环境条件的融合值。它通过减少冗余监测设备来降低生产成本，提高了无线传感器网络的数据采集效率。
