무선 센서 네트워크에서 상화연관성 기반의 정보수집체계
Data Aggregation Based on Correlation for Wireless Sensor Networks
초록/요약
With the improvement of the MEMS (micro-electro-mechanical-systems), processor, radio and memory technologies, it’s possible to produce micro sensor nodes. These nodes capable of wireless communication, sensing and computation are extremely small, low-power and cheap price. Wireless sensor networks represent a significant improvement over traditional sensors. A network of sensors can be used to obtain state-bases data from the area in which they are deployed. To reduce the costs, the data, sent via intermediate sensors to a sink, is often aggregated. And this aggregation is done by a subset of the sensors called aggregators. In this paper, we set the aggregation model constructed by clusters which is divided into grids. Then we apply two different correlation algorithms that are spatial correlation and temporal correlation on different levels of aggregation in order to optimize the aggregation process. The experiments show that our algorithms improve our aggregation as expected.
more목차
0 ABSTRACT 3
1 INTRODUCTION 4
2 DATA AGGREGATION TECHNOLOGIES AND RELATED WORKS 9
2.1 IN-NETWORK DATA AGGREGATION PRINCIPLES 9
2.2 PROPERTIES OF AGGREGATION 12
2.3 RELIABILITY DATA AGGREGATION 13
2.4 SOME RELATED WORKS 15
3 SYSTEM MODEL 19
3.1 THE PROTOCOL ARCHITECTURE FOR DATA AGGREGATION 19
3.1.1 The architecture of the protocol stack for WSN 19
3.1.2 The Protocol Architecture for Data aggregation 22
3.2 SYSTEM MODEL FOR AGGREGATION 23
3.2.1 Data aggregation framework 23
3.2.2 Underlying routing and clustering protocols 24
3.2.3 Aggregation in Each Cluster 28
3.2.4 Time Synchronization 30
3.2.5 Correlation Optimization 32
4 DATA MODELING AND EXPERIMENT 35
4.1 PARAMETER ESTIMATION USING THE SAMPLE MEAN 35
4.2 DATA ESTIMATION TECHNIQUE - LINEAR ESTIMATION OF X GIVEN Y 36
4.3 ACCURACY EVALUATION 40
4.4 LINEAR ESTIMATION OF RANDOM VARIABLES FROM RANDOM VECTOR 40
4.5 RESULT ANALYSIS 43
5 CONCLUSION 48
REFERENCES 50
목차
Fig 1-1 Wireless sensor network 4
Fig 1-2 Components in node of sensor network 5
Fig 2-1 Temperature data aggregation example 10
Fig 2-2 Data aggregation with lost messages 14
Fig 3-1 The wireless sensor networks Protocol Stack 21
Fig 3-2 Data aggregation software architecture 23
Fig 3-3 Data aggregation framework 24
Fig 3-4 Structure of our WSN System 27
Fig 3-5 Grid-cluster Structure of our WSN system 29
Fig 3-6 Spatial correlations among sensor readings 33
Fig 4-1 Error correction detail 44
Fig 4-2 Spatial data prediction 45
Fig 4-3 Spatial aggregation accuracy 45
Fig 4-4 Temporal data prediction 46
Fig 4-5 Temporal aggregation accuracy 47
