||Power-Saving Scheduling for Sensing Configuration based on Global Error Minimization
||Institute of Computer Science and Information Engineering
wireless sensor network
Haar wavelet transform
linear regression model
The Internet of Things (IoT) is an essential application today. Its development in recent years has made the topic of wireless sensing networks and sensors more and more prevalent. In the field of wireless communication research, we pay great attention to the energy consumption of wireless sensors, especially. Because there are usually a large number of sensors in the network, but the energy supply is relatively limited (sensors are mostly powered by replaceable batteries). Also, the action of frequent battery replacement or charging batteries for depleted sensors is currently quite labor-intensive. Therefore, this study hopes to use data analysis techniques to schedule sensors effectively for reducing power consumption by only activating some of the sensors while it still effectively monitoring the surrounding environmental variables without causing severe negligence or error.
In this thesis, we proposed a greedy algorithm based on Haar wavelet transform and use this as a priority basis for selecting whether the sensor will be active or not within a certain period of duration. Haar wavelet transform has the characteristics of low complexity and multi-resolution, combining with our need to solving power-saving consumption issue, and can observe data changes and properties of different scales according to the situation in the time, making the feature extraction more convenient. Besides, we also try to use other features as the basis for priority and combine with our proposed algorithm. We found that our method is more effective than random selection or other methods when choosing to turn off some sensors. This method makes it more similar to the best sensor set with the lowest error.
中文摘要 . . . i
Abstract . . . ii
Acknowledgment . . . iii
Contents . . . iv
List of Tables . . . vi
List of Figures . . . vii
1 Introduction . . . 1
2 Related Works . . . 5
2.1 Time Series Forecasting . . . 5
2.1.1 Forecasting Models . . . 5
2.1.2 Signal Processing . . . 5
2.2 Sensor Scheduling . . . 7
2.2.1 Sensors with Spatial Attribute . . . 7
2.2.2 Sensor Selection Problem . . . 8
2.3 Power Consumption Issue . . . 8
3 Problem Formulation . . . 11
3.1 Framework Overview . . . 11
3.2 Power-saving Scheduling Problem . . . 13
4 Greedy-based Wavelet Score Selecting . . . 15
4.1 Wavelet Coefficients . . . 16
4.2Greedy-based Active Priority . . . 17
4.3 Wavelet Score . . . 20
4.4 Combine with Other Features . . . 20
5 Experimental Study . . . 22
5.1 Experimental Setup and Data Description . . . 22
5.2 Experimental Results . . . 24
6 Conclusions and Future Works . . . 34
Bibliography . . . 35
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