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研究生:Zulhaydar Fairozal Akbar
研究生(外文):Zulhaydar Fairozal Akbar
論文名稱:分散式範圍天際線查詢處理協定於移動無線感測環境
論文名稱(外文):A Distributed Protocol for Processing Continuous Range-Skyline Queries in Mobile WSNs
指導教授:劉傳銘劉傳銘引用關係
指導教授(外文):Chuan-Ming Liu
口試委員:陳震宇王正豪劉傳銘
口試委員(外文):Jen-Yeu ChenJenq-Haur WangChuan-Ming Liu
口試日期:2016-07-15
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:資訊工程系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
中文關鍵詞:Safe TimeWSNContinuous Range-SkylineRange-Skyline
外文關鍵詞:Skyline Query
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Continuous Range-Skyline Query (CRSQ) is a variation of Range-Skyline Query (RSQ) that the system persistently reports the moving objects that are skyline results to a query in a given search range. In this works, we focus on the continuous range-skyline query, which will monitor the skyline within a specific range during a time period in mobile wireless sensors networks (MWSNs). Note that each sensor can move and communicate with neighboring sensors. For example, system continuously returns moving sensors, which are range-skyline, to the user. Many query processing approaches for CRSQ search have been proposed in traditional environments. However, the existing server-client approaches for CRSQ are sensitive to the number of moving objects. When the moving objects quickly move, the processing load on the server will be heavy due to the overwhelming data. We propose an effective and non-centralized approaches, Distributed Continuous Range-Skyline Query (DCRSQ), for supporting CRSQ query in a mobile environment. This work uses safe time approach to predict the time when the object falling in the range query. Then, each mobile sensor node derives local skyline with its neighboring information. The query point will collect all the local skyline and computes the final results of CRSQ. This process may lead to reducing the transferred data since some unnecessary data objects are discarded. In the simulation experiments, it shows that distributed way perform better than centralized in term of number of messages, number of accessed nodes and accuracy.
Continuous Range-Skyline Query (CRSQ) is a variation of Range-Skyline Query (RSQ) that the system persistently reports the moving objects that are skyline results to a query in a given search range. In this works, we focus on the continuous range-skyline query, which will monitor the skyline within a specific range during a time period in mobile wireless sensors networks (MWSNs). Note that each sensor can move and communicate with neighboring sensors. For example, system continuously returns moving sensors, which are range-skyline, to the user. Many query processing approaches for CRSQ search have been proposed in traditional environments. However, the existing server-client approaches for CRSQ are sensitive to the number of moving objects. When the moving objects quickly move, the processing load on the server will be heavy due to the overwhelming data. We propose an effective and non-centralized approaches, Distributed Continuous Range-Skyline Query (DCRSQ), for supporting CRSQ query in a mobile environment. This work uses safe time approach to predict the time when the object falling in the range query. Then, each mobile sensor node derives local skyline with its neighboring information. The query point will collect all the local skyline and computes the final results of CRSQ. This process may lead to reducing the transferred data since some unnecessary data objects are discarded. In the simulation experiments, it shows that distributed way perform better than centralized in term of number of messages, number of accessed nodes and accuracy.
Contents
ABSTRACT ii
ACKNOWLEDGMENTS iv
List of Figures vii
List of Tables viii
Chapter 1 Introduction 1
1.1 Background 1
1.3 Previous Related Works 6
1.4 Research Objectives 7
1.5 Contribution 8
1.6 Thesis Development 9
Chapter 2 Literature Review 10
2.1 Mobility Models 10
2.2 Wireless Sensor Network 12
2.2.1 Military applications 12
2.2.2 Environment applications 13
2.2.3 Indoor Applications 13
2.3 Range-Skyline Queries 14
2.4 Continuous Range-Skyline Query 17
Chapter 3 Proposed Methods (DCRSQ) 19
3.1 Preliminaries 20
3.2 Assumptions and Notations 21
3.3 Initialization 21
3.4 Transmitting Information 22
3.5 Local Processing 24
3.5.1 Finding Candidates 26
3.5.2 Deriving Local Skyline 31
3.6 Final Skyline 32
3.7 Comparison Between Two Approaches 33
3.7.1 Centralized Approach 34
3.7.2 Server Side Approach 35
Chapter 4 Simulation Experiments 36
4.1 Impact of Number of Mobile Sensors 38
4.2 Impact of Transmission Range 41
4.3 Impact of Query Range 44
4.4 Impact of Number of Queries 47
4.4 Impact of Speed of Mobile Sensors 50
Chapter 5 Conclusions 53
5.1 Conclusions 53
References 54
References

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