Fault Tolerant Data Aggregation for Wireless Sensor Networks
- 발행기관 亞洲大學校 大學院
- 지도교수 Yoon, Won-Sik
- 발행년도 2005
- 학위수여년월 2005. 8
- 학위명 석사
- 학과 및 전공 일반대학원 전자공학과
- 본문언어 영어
초록/요약
Recent advances in wireless communications and electronics have enabled the development of wireless sensor networks. Spatial and temporal data correlation makes Wireless Sensor Networks (WSNs) unique compared to other ad hoc networks. In-network aggregation as a power-efficient mechanism for collecting data in wireless sensor networks has been emphasized with the development of other WSN network protocols. Communication errors during data aggregation may have an untrivial impact on aggregation algorithms. In this paper, we firstly propose a general architecture to seamlessly integrate data aggregation into wireless sensor networks. Secondly we present one approach to provide fault tolerant to data aggregation algorithms by exploiting the inherent correlations that exist between sensor nodes. Our approach enhances the reliability of aggregation algorithms instead of using traditional forward error correction (FEC) or autonomic resend request (ARQ) methods.
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Content
Content = ⅰ
List of Symbols= ⅲ
List of Figures = ⅳ
Abstract = ⅴ
1 Introduction = 1
1.1 Wireless Sensor Networks = 1
1.2 In-Network Data Aggregation = 3
1.2.1 Properties of Aggregates = 5
1.2.2 Reliability Data Aggregation = 6
1.3 Data Correlation = 8
2 Approaches for In-Network Aggregation = 11
2.1 Routing Trees = 11
2.1.1 Grouping = 11
2.1.2 Tree Optimization = 12
2.1.3 Establishing Data Flow Paths = 12
2.2 Synchronization = 13
2.2.1 Classification of Periodic Aggregation Protocols = 14
2.2.2 TiNA = 14
2.3 Prediction and Compression = 15
3 Data Aggregation Design and Implementation = 17
3.1 Data Aggregation Protocol Architecture = 17
3.2 Data Aggregation Framework = 18
4 Data Modeling and Experiment = 20
4.1 Linear Regression = 20
4.2 Exponential Smoothing = 23
4.3 Result Analysis = 24
5 Conclusion = 28
5.1 Multi-Query Optimization = 29
5.2 Metadata Management = 29
5.3 Query Languages = 30
References = 31
