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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/74610


    Title: 無線視覺感測網路下具監控品質保證之環繞覆蓋防衛線建構
    Other Titles: Encircled β-breadth belt-barrier coverage in wireless visual sensor networks
    Authors: 蔡國堂;Tasi, Kuo-Tang
    Contributors: 淡江大學資訊工程學系資訊網路與通訊碩士班
    鄭建富
    Keywords: 防禦線覆蓋;影像厚度;監控品質;影像重要性;環繞覆蓋;無線視覺感測網路;Barrier Coverage;Breadth of Image;Quality of Monitor;Importance of Image;encircled coverage;Wireless Visual Sensors Network
    Date: 2011
    Issue Date: 2011-12-28 19:02:46 (UTC+8)
    Abstract: 無線視覺感測網路(Wireless Visual Sensor Networks,WVSNs)是由許多分散在場景中的視覺感測節點所構成。相較於傳統的無線感測網路(Wireless Sensor Networks,WSNs)而言,無線視覺感測網路不僅能達到監控之功用,並且能夠進一步取得影像資訊,如此一來將更能有效地立即掌控監控場景之現況。這也使得無線視覺感測網路中之防禦線覆蓋(barrier coverage)問題受到許多研究學者的重視。然而,此問題有著許多的挑戰,原因在於WVSNs所收集的資料為影像資訊,若使用過多的視覺感測節點佈建防線,將收集到過多且不必要之影像。如此一來,將增加影像回傳時所需的頻寬負載。並且,過多重覆影像也將造成後端影像處理之困難度。因此,如何挑選較少數量的視覺感測節點來佈建防線,已成為無線視覺感測網路中一項非常重要之議題。此外,過往針對無線視覺感測網路下之防禦線覆蓋研究,並未考慮到其所收集到的每一片段影像應具有一定大小之影像厚度,如此一來將難以辨視其所收集到的影像。因此,本研究將加入影像厚度之考量,藉此提供較佳之監控品質(Quality of Monitor, QoM)。
    此外,當有入侵者穿越防禦線時,過往的視覺防禦線只能確保該入侵者在穿越防禦線時其影像資訊能夠被偵測並擷取下來,但並無法保證其所擷取到的影像資訊為入侵者之重要部分。因此,為了更進一步的提升QoM,我們將加入影像重要性(Importance of Image, IoI)之考量,藉由加入環繞覆蓋(encircled coverage),來提供入侵者之環繞影像資訊,如此一來,將可確保重要角度之影像能夠被擷取下來。綜合上述各點,本研究將針對無線視覺感測網路中之防禦線覆蓋問題重新做一探討,並提出分散式的視覺防禦線建構演算法,藉以找尋較少數量之視覺感測節點來佈建具備一定影像監控厚度及環繞覆蓋能力之防禦線。
    Wireless visual sensor networks (WVSNs) can not only provide monitoring functions like wireless sensor networks (WSNs) but also capture real-time images of the monitored scene. This capability has made the barrier coverage of WVSNs an issue of interest to many researchers. For instance, in the construction of a barrier, using too many camera sensors may produce an excessive number of redundant and overlapping images, which in turn increase the bandwidth of data transmission, occurrence of packet collisions, and difficulty of post image processing. Therefore, how to use a minimum number of camera sensors to construct a barrier has become an important issue in WVSNs. Besides, previous research of barrier coverage did not consider breadth of coverage, i.e. the width of collected images. In this paper, we consider breadth and Importance of Image (IoI) to increase the Quality of Monitor (QoM) of WVSNs. Finally, the proposed algorithms’ successful rate of barrier construction under different conditions, including β requirement, sensor distribution, and rotation capability is also tested through simulations.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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