WSN中利用XGBoost和加权自适应HFLMS的数据约减组合预测方法

WSN 中利用XGBoost 和加权自适应HFLMS 的数

据约减组合预测方法

1.Introduction

Wireless sensor networks (WSNs) are widely used in a variety of

applications such as environmental monitoring, industrial automation,

and healthcare. In WSNs, sensor nodes are responsible for sensing, processing, and communicating data to a central node. However, due to

limited resources such as energy, memory, and processing power, the

amount of data that can be transmitted from the sensor nodes to the

central node is restricted. Therefore, data reduction techniques are required for efficient and effective communication in WSNs.

In this paper, we propose a data reduction technique that

combines the XGBoost algorithm and weighted adaptive HFLMS

algorithm to predict missing or erroneous data values in WSNs. The proposed method is based on ensemble learning, where multiple models are combined to improve the accuracy of the prediction.

2.XGBoost Algorithm

XGBoost is a supervised learning algorithm that is used for classification and regression tasks. It is a tree-based model that optimizes the gradient boosting algorithm. XGBoost uses a series of decision trees to make predictions by combining the predictions of each tree. The model is trained using a gradient descent optimization algorithm that minimizes the loss function.

The advantage of XGBoost is its ability to handle missing values

and outliers, which are common in WSNs. XGBoost also has the ability to handle different types of data such as categorical, numerical, and binary data.

3.Weighted Adaptive HFLMS Algorithm

The weighted adaptive HFLMS algorithm is a recursive least-

squares algorithm that is used to estimate the parameters of a linear

model. It updates the model parameters as new samples become available and uses a weighting function to give more importance to recent samples.

In WSNs, the weighted adaptive HFLMS algorithm can be used to estimate the missing or erroneous data values by using the available

data from the neighboring nodes. The algorithm takes into account the spatial and temporal correlations of the data and adapts the weighting function accordingly.

4.Data Reduction Technique by Ensemble Learning

The proposed data reduction technique combines the strengths of the XGBoost algorithm and the weighted adaptive HFLMS algorithm by using ensemble learning. Ensemble learning is a machine learning technique that combines multiple models to improve the accuracy of the prediction.

In this technique, XGBoost is used to generate the initial predictions for the missing or erroneous data values. These predictions are then fed into the weighted adaptive HFLMS algorithm, which uses the available data from the neighboring nodes to refine the initial predictions. The weighted adaptive HFLMS algorithm adapts the weighting function based on the spatial and temporal correlations of the data.

The final prediction is obtained by combining the predictions of XGBoost and weighted adaptive HFLMS using a weighted average. The weights are assigned based on the accuracy of each model and are updated dynamically to ensure that the most accurate models are given more weight.

5.Experimental Results

To evaluate the performance of the proposed data reduction technique, we conducted experiments on a WSN dataset. The dataset consists of temperature and humidity readings from sensor nodes in a greenhouse.

We compared the performance of the proposed method with other state-of-the-art data reduction techniques such as linear regression, support vector regression, and k-nearest neighbor regression.

The results show that the proposed method outperforms the other techniques in terms of prediction accuracy, root mean squared error, and mean absolute error. The proposed method also has the advantage of being able to handle missing values and outliers, which are common in WSNs.

6.Conclusion

In this paper, we proposed a data reduction technique that combines the XGBoost algorithm and weighted adaptive HFLMS algorithm to predict missing or erroneous data values in WSNs. The