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研究生:王丕獅
研究生(外文):Pi-shih Wang
論文名稱:針對移動性目標物的有效率無線感測網路
論文名稱(外文):Energy-Efficient Wireless Sensor Networks for Mobile Target Detections
指導教授:孫敏德
指導教授(外文):Ming-Te Sun
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:42
中文關鍵詞:移動目標無線感測網路網格網路
外文關鍵詞:data disseminationMobile TargetWireless Sensor Networks
相關次數:
  • 被引用被引用:0
  • 點閱點閱:159
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  • 收藏至我的研究室書目清單書目收藏:0
無線感測網路有許多應用,戰場便是其中之一。無線感測網路可以用於戰場上的目標物偵測及資料收集。然而,目標物和資料收集點都會移動,這對設計與實作網路系統帶來許多挑戰。雖然目前已經有一些處理多移動資料收集點的研究,但他們假設資料收集點位置已知或是利用耗費能源的方式取得資料收集點的位置。為了改善此方面的問題,我們提出一個在無線感測網路下的資料收集架構,叫動態自適應性的網狀網路。我們分別利用樹狀和網狀網路的優點使得資料收集與遞送更加地有效率。此外,我們介紹的兩種方法來平衡網路負載量,第一個是將資料遞送的負載平均分攤,第二個是調整網路結構平衡網格間的負載量。最後,我們提出改良式的查詢聚集方法,使得可以減少資料回傳等待時間及降低封包流量。模擬結果顯示出動態自適應性的網狀網路在多移動資料收集點的無線感測網路中是已知中最好的資料收集方案。
In battlefields, the wireless sensor network can be used for target detection and data collection. However, the mobility of targets as well as the sinks creates challenges for the design and implementation of the system. While there are research proposals capable of handling multiple mobile sinks, these protocols either assume the knowledge of sink location or create too many flooding, leading to a quicker energy consumption. To deal with this issue, we propose a framework for data collection in wireless sensor networks, namly Dynamic and Adaptive Grid (DAG). In our framework, we take the advantages of both grid and tree data structures to route queries and data efficiently. In addition, two mechanisms are introduced to balance the load in the network. The first one helps to distribute the tasks more evenly; and the second one adjust the size of grids to balance the traffic load in each grid. Last, an improved query aggregation is proposed to reduce the query response time and the traffic associated with each query. The simulation results show that DAG outperforms the best known data collection solutions for wireless sensor networks with multiple mobile targets and sinks.
Contents
1 Introduction 1
2 Literature Review 3
3 Dynamic and adaptive grid 5
3.1 Network construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1.1 Grid construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1.2 The shortest path tree construction . . . . . . . . . . . . . . . . . . 12
3.2 Query and data forwarding . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3 Mobility maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3.1 Sink mobility maintenance . . . . . . . . . . . . . . . . . . . . . . . 14
3.3.2 Target mobility maintenance . . . . . . . . . . . . . . . . . . . . . . 15
3.4 Dissemination node energy consumption balancing . . . . . . . . . . . . . . 16
3.5 Dynamic grid adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.6 Query aggregation improvement . . . . . . . . . . . . . . . . . . . . . . . . 18
4 Simulation Results 19
4.1 Simulation setting and metrics . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Impact of the numbers of sinks . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3 Impact of sink mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.4 Impact of node density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5 Conclusion 28
6 Reference 29
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