無線感測網路的諸多應用，例如環境監控、戰場監測及災難救助，無線感測節點通常被佈置在險惡且無人維護的環境，在這樣的環境可能存在影響無線感測網路正常運作的事件。為了讓無線感測網路確實地完成任務，在設計無線感測網路系統時應該預先考慮此類情況的發生，以降低其影響。因此，本篇論文討論其中兩種會對無線感測網路系統造成影響的議題：覆蓋漏洞及虛擬節點的存在。

在無線感測網路所部署的地區中可能存在各種不同形式的障礙物，這些障礙物會降低無線感測網路的功能性，例如造成以位置為基礎的繞路協定中無效終點錯誤、進行導航時與障礙物發生碰撞，如果可以預先偵測障礙物的大小及位置，就能減少他們所造成的影響。本篇論文提出一種以偵測覆蓋漏洞為基礎的標示障礙物之方法，此方法不需要無線感測節點的絕對位置資訊，也不需要額外的硬體元件就能標示出障礙物，如此一來可以大幅地降低無線感測網路的佈署成本。透過網路模擬器ns-2的實作，可以發現所提出的機制能達到相當精確的偵測效果。

無線感測節點通常配置電池進行運作，為節點替換電力耗盡的電池並不容易，因此，電力對於無線感測網路系統是一項珍貴且稀少的資源。為了延長無線感測網路的運作時間，有研究提出僅使用部份可以完整覆蓋監測區域的節點，讓其他的節點進入休眠狀態以節省電力的消耗。先前提出的方法節點需要知道自己的位置，但是為了得到節點的位置資訊，將會造成額外的成本。因此，本篇論文提出一種不需要位置資訊的運作節點選擇機制，此機制可以偵測目前存在於監測區域中的覆蓋漏洞，進而選擇出適當的工作節點。透過網路模擬器ns-2的模擬結果顯示，此機制可以提供監測區域的完整覆蓋率，並且跟需要位置資訊的機制相比，所需要的運作節點不會比較多。

隨著無線感測網路在軍事與民間用途的廣泛運用，安全已經成為一項非常重要的議題。Sybil攻擊是藉由單一的惡意節點非法的偽造許多虛擬的節點資訊來欺騙週遭的鄰居，使得它們誤認這些虛擬節點為它們的鄰居，進而擾亂整個網路。本篇論文提出了一個利用每個節點周遭鄰居的差異性來偵測虛擬節點的機制。在數學分析的結果顯示若在節點密度足夠的情況下，本方法可以達到非常理想的偵測率；模擬的結果顯示即使平均每個節點只有九個鄰居節點的情況，此方法仍能達到95% 的偵測率。

In the monitoring and surveillance applications of wireless sensor networks (WSNs), such as environment monitoring, battlefield surveillance and diaster relief coordination, the sensor nodes could be deployed in hostile environments and operate untethered. Therefore, there might be various hazards occurred in the network and these hazards could wreak havoc on the functionality of the system. Two kinds of hazards, which are the existence of coverage holes and Sybil attacks, are discussed in this thesis. In the detection of coverage holes, the existing obstacles can be identified and a density control scheme is also developed to prolong the system time of WSNs.

It is likely that a deployed area will contain obstacles of some form in WSNs. These obstacles may potentially degrade the functionality of the WSN, e.g. the occurrence of deadends due to obstacles in geographic routing protocols, or objects might crash into obstacles when moving in WSNs. If the size and location of the obstacles can be detected, their influence can be reduced. Accordingly, this thesis describes a scheme for detecting obstacles in WSNs. The scheme identifies the obstacles by marking the sensor nodes around the obstacle boundaries. The scheme does not require the absolute position of individual nodes in the sensing field nor any additional hardware, and thus can significantly reduce the deployment costs. The efficiency of the scheme is demonstrated via simulations performed using the network simulator ns2. The results show that the detection scheme needs much less overhead compared to previous research while still marking the nodes close to the obstacles precisely.

WSNs are typically expected to work for a long time. However, the power supply of a sensor node is usually a battery that cannot provide long operation time or be replaced easily due to hostile environments. Therefore, energy is an important and scarce resource in WSNs. For prolonging the lifetime of WSNs, some research maintained adequate degree of node density instead of the higher degree of that. Only a partial set of the sensor nodes, active nodes, are required for providing the full coverage of the interested area while others are inoperative. Previous schemes for active node selection needed location information for each sensor node. This thesis presents an algorithm for density control without position information of sensors. The simulation results show that the scheme guaranteed the 100% coverage of the target area and the number of active nodes was competitive to the algorithms that require location data.

As the prevalence of WSNs grows in the military and civil domains, the need for network security has become a critical concern. In a Sybil attack, the WSN is subverted by a malicious node which forges a large number of fake identities in order to disrupt the network’s protocols. In attempting to protect WSNs against such an attack, this thesis develops a scheme in which the node identities are verified simply by analyzing the neighboring node relation of each node. The analytical results confirm the efficacy of the approach given a sufficient node density within the network. The simulation results demonstrate that for a network in which each node has an average of 9 neighbors, the detection rate is around 95% when multiple malicious nodes are considered.

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