# A Hybrid Prediction Model for Energy-Efficient Data Collection in Wireless Sensor Networks

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## Abstract

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## 1. Introduction

- 1
- We designed a model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods for data prediction in order to reduce unnecessary data transmissions and as a result decrease energy consumption. This model employs a minimal set of sensor nodes for data collection based on intra-cluster prediction and processing of data. In the proposed model, DT is used to filter data associated with each node in order to derive a tree for clustering the sensor data. Additionally, a self-tuning approach based on KF is utilized to optimize estimation while minimizing covariance errors.
- 2
- We provide the MATLAB simulation-based practical demonstration of the proposed model to measure the data packet transmission and energy consumption in sensor nodes under different numbers of distributed sensor nodes in the network.

## 2. Related Works

## 3. The Proposed Model

#### 3.1. The Algorithms Employed

- KF is an algorithm that provides estimates of some unknown variables given the measurements observed over time. Kalman filter is used to estimate states based on linear dynamical systems in state-space format. It has a relatively simple form and requires small computational power.
- DT is a popular classification algorithm to understand and interpret. The goal of DT is to create a training model that can be used to predict the class or value of the target variable by learning simple decision rules inferred from prior data.
- ARIMA is an analysis model that uses time series data to predict future trends. It is a hybrid autoregressive model with the moving average model.

#### 3.2. The Hybrid Method

Algorithm 1: Hybrid algorithm. |

#### 3.3. Adaptive Update of Clustering by DT

#### 3.4. ARIMA Prediction Model

## 4. Experiment Evaluation and Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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ARIMA | Coefficients | St. Dev. |
---|---|---|

AR (1) | −0.7144 | 7.0748 |

AR (2) | −0.4466 | 4.0677 |

AR (3) | 1.2873 | 5.0433 |

AR (1) | −0.7144 | 7.0748 |

AR (2) | −0.4466 | 4.0677 |

AR (3) | 1.2873 | 5.0433 |

AR (1) | 0.1765 | 4.753 |

AR (2) | 0.1106 | 4.043 |

AR (3) | 0.1076 | 7.753 |

MA (1) | −0.4131 | 7.0293 |

MA (2) | 1.7011 | 5.0988 |

MA (3) | −0.1510 | 5.0728 |

MA (1) | 0.4355 | 7.0981 |

MA (2) | 1.2788 | 5.1067 |

MA (3) | 0.1314 | 4.0433 |

MA (1) | −0.3081 | 6.233 |

MA (2) | 0.2944 | 4.053 |

MA (3) | 0.8733 | 5.012 |

Number of Nodes | |||
---|---|---|---|

500 | 1000 | 1500 | |

ARE | 0.4 | 4.5 | 5.5 |

MAE | 0.5 | 1.05 | 0.55 |

RMSE | 0.8 | 1.2 | 0.52 |

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**MDPI and ACS Style**

Soleymani, S.A.; Goudarzi, S.; Kama, N.; Adli Ismail, S.; Ali, M.; MD Zainal, Z.; Zareei, M.
A Hybrid Prediction Model for Energy-Efficient Data Collection in Wireless Sensor Networks. *Symmetry* **2020**, *12*, 2024.
https://doi.org/10.3390/sym12122024

**AMA Style**

Soleymani SA, Goudarzi S, Kama N, Adli Ismail S, Ali M, MD Zainal Z, Zareei M.
A Hybrid Prediction Model for Energy-Efficient Data Collection in Wireless Sensor Networks. *Symmetry*. 2020; 12(12):2024.
https://doi.org/10.3390/sym12122024

**Chicago/Turabian Style**

Soleymani, Seyed Ahmad, Shidrokh Goudarzi, Nazri Kama, Saiful Adli Ismail, Mazlan Ali, Zaini MD Zainal, and Mahdi Zareei.
2020. "A Hybrid Prediction Model for Energy-Efficient Data Collection in Wireless Sensor Networks" *Symmetry* 12, no. 12: 2024.
https://doi.org/10.3390/sym12122024