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무선 센서 네트워크에서 상화연관성 기반의 정보수집체계
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 김용득 | - |
| dc.contributor.author | 장풍 | - |
| dc.date.accessioned | 2018-11-08T07:38:01Z | - |
| dc.date.available | 2018-11-08T07:38:01Z | - |
| dc.date.issued | 2006-08 | - |
| dc.identifier.other | 1654 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/6125 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :전자공학과,2006. 8 | - |
| dc.description.tableofcontents | 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|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 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 무선 센서 네트워크에서 상화연관성 기반의 정보수집체계 | - |
| dc.title.alternative | Zhang Feng | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Zhang Feng | - |
| dc.contributor.department | 일반대학원 전자공학과 | - |
| dc.date.awarded | 2006. 8 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 565428 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000001654 | - |
| dc.subject.keyword | WSN | - |
| dc.description.alternativeAbstract | 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. | - |
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