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| 折扣與優(yōu)惠：團(tuán)購最低可5折優(yōu)惠 - 了解詳情 | 論文格式：Word格式(*.doc) |  |
摘要：在普適計(jì)算環(huán)境中，由傳感器收集到的情境信息刻畫了物理世界的屬性與特征，但情境信息之間有可能產(chǎn)生沖突。于是情境不一致性的檢測成為重要的一個(gè)課題。本文首先分析了已有的基于事件間happen-before關(guān)系的并發(fā)事件偵測算法，其運(yùn)用矢量時(shí)鐘來判斷區(qū)間是否重疊，從而偵測有無情境不一致發(fā)生。通過詳細(xì)分析，該算法有三個(gè)缺陷：采用中央集中式結(jié)合的網(wǎng)絡(luò)架構(gòu)，中心檢測節(jié)點(diǎn)數(shù)據(jù)流量過大；在網(wǎng)絡(luò)中節(jié)點(diǎn)數(shù)量多的情況下，邏輯時(shí)鐘數(shù)組使算法空間復(fù)雜度過高；算法檢測精確度不高，一些沖突情況無法偵測到。 針對(duì)CEDA的缺陷，我們提出了快照時(shí)鐘算法SCA。算法是依據(jù)邏輯時(shí)鐘來判斷區(qū)間的關(guān)系。SCA的網(wǎng)絡(luò)舍棄了中心節(jié)點(diǎn)，使網(wǎng)絡(luò)不再是中心集中式的。SCA中的每個(gè)進(jìn)程不用維護(hù)時(shí)鐘數(shù)組，只需單個(gè)時(shí)鐘值。于是將時(shí)鐘的空間復(fù)雜度由O(n)降低至O(1)，將每個(gè)事件的偵測時(shí)間復(fù)雜度由O(n2)降低至O(n)。SCA也能解決一些CEDA無法偵測的情況。 最后我們給出了SCA的實(shí)驗(yàn)驗(yàn)證與性能分析，無論是從實(shí)驗(yàn)還是理論方面分析，快照時(shí)鐘算法都有良好且穩(wěn)定的表現(xiàn)，適用于各種節(jié)點(diǎn)數(shù)，消息延遲時(shí)間不同和事件持續(xù)時(shí)間各異的情境感知應(yīng)用。 關(guān)鍵字：普適計(jì)算 情境不一致性偵測 邏輯時(shí)鐘 happen-before
Abstract：In pervasive computing environment, context collected by sensors is a piece of information that captures the characteristics of physical world. Since there are always conflicts among the contexts, the detection of context inconsistency becomes an important issue. Firstly, we analyze the CEDA (Concurrent Events Detection for Asynchronous consistency checking algorithm), which is based on happen-before relationship. CEDA uses Vector Clock to check if two intervals overlap which means local events concurrent happen. The algorithm is limited by three problems. CEDA’s centralized network makes the clustering head overflow; CEDA suffers a large space complexity and message complexity when there are a great number of nodes in network; the accuracy of algorithm is low because it cannot find many other conflicts. To this end, we propose the Snapshot Clock Algorithm (SCA). SCA also checks the relationship between two intervals by comparing the logical clock timestamp. However, SCA discards clustering head, and uses single clock value instead of the vector clock in SCA. Thus SCA is decentralized and has less complexity. SCA’s space complexity decreases from O(n) to O(1) and time complexity decreases from O(n2) to O(n). Moreover, SCA can find conflict situations which CEDA cannot. At last, we conduct extensive experiments and theoretical analysis to further evaluate SCA. Both experiments and performance analysis show that SCA accurately detects context inconsistency in pervasive computing environment. SCA is desirable for different context-aware applications which vary in the number of nodes, the time of message delay and the duration of events. Keywords：Pervasive Computing , Detection of Context Inconsistency,Logical Clock, Happen-before Relationship
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