生活中許多實際應用程序由各種網絡組成。在這些網絡的運行過程中，它們可能會受到無法預測或無法控制的破壞性事件的干擾。這些破壞性事件包括但不限於人為錯誤，自然災害，或是惡意攻擊。就網絡體系結構而言，破壞性事件造成的損失主要是組件損壞或組件之間的連接路徑故障。當運行中的網絡遭到破壞時，往往會造成自然的生態或經濟損失。衡量網絡抵禦破壞和恢復能力的績效指標正是網絡的彈性。因此，系統性且有效地測量網絡彈性是一個值得討論的話題。基於對網絡可靠性的評估方法，即二進制加法樹算法（binary addition tree algorithm, BAT），提出了一種基於二進制加法樹的彈性評估（binary addition tree-based resilience assessment, BAT-RA）和時間相關的BAT-RA（time-related BAT-RA, t-BAT-RA），以分析非循環二進制狀態網絡的彈性。本文還提供了通過擬議的BAT-RA和 t-BAT-RA分析的野火無線檢測傳感器網絡的網絡彈性的案例研究。BAT-RA考慮了破壞性事件的隨機性，並基於最佳可行技術全面列出了所有可能的破壞性情景和相應的整體修復策略，然後計算了網絡的靜態彈性。t-BAT-RA 比 BAT-RA 進一步考慮了動態恢復策略，並專注於更容易發生的破壞性事件、更多的參數和決策變量、仍然包括保護和恢復策略的成本、三階段策略制定（即保護，攻擊和恢復），以及用於量化網絡彈性的網絡可靠性恢復程度的新性能指標。此外，對於選擇具有成本約束（即恢復受損網絡的預算）和網絡彈性要求的恢復策略的決策者，建議的BAT-RA可以以不同的成本獲得網絡彈性，從而幫助決策者確定恢復受損網絡的預算，並獲得所需的網絡彈性性能。

Many real-world applications consist of various networks. During the operation of these networks, they are possibly disturbed by unpredictable or uncontrollable devastating events. These devastating events include but not limit to human error, natural disasters, or malicious attacks. As far as the network architecture is concerned, the losses caused by destructive events are mainly component damage or failure of the connection path between components. When the operating network is damaged, it tends to cause natural ecological or economic losses. The ability for a network to withstand damage and recover is defined as network resilience. Therefore, systematically and effectively measuring network resilience is a topic worth discussion. Based on the evaluation method on network reliability, binary addition tree algorithm (BAT), this study proposes binary addition tree-based resilience assessment (BAT-RA) and time-related BAT-RA (t-BAT-RA) to analyze the resilience of the acyclic binary-state network. This study also provides a case study of the network resilience of the wildfire wireless detection sensor network analyzed by the proposed BAT-RA and t-BAT-RA. BAT-RA considers the randomness of destructive events and lists all possible devastating scenarios and the whole of the corresponding restoration strategies (also called recovery strategies in this study), and then computes the static resilience of the network. t-BAT-RA further considers dynamic recovery strategies than BAT-RA, and focuses on the more prone to destructive events. More parameters, decision variables, three-stage strategy development (i.e. protection, attack, and restoration) and a new dynamic resilience indicator are included and developed in t-BAT-RA. In addition, the proposed BAT-RA and t-BAT-RA can provide network resilience with different costs and thus help decision makers determine their action and budget for protecting or recovering the damaged network.

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