本文選題：無線傳感網(wǎng)絡(luò)　切入點：多級分簇　出處：《江蘇大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：無線通信技術(shù)、傳感器技術(shù)與嵌入式技術(shù)的不斷進步,促進了低成本、低功耗、多功能的傳感器節(jié)點快速發(fā)展,從而這種由微型傳感器節(jié)點組成的無線傳感網(wǎng)絡(luò)(Wireless Sensor Networks——WSN)迅速普及。在WSN中,傳感器節(jié)點采集監(jiān)控區(qū)域內(nèi)感知對象的信息,然后通過無線通信將這些信息發(fā)送給基站(Base Station——BS),然而由于WSN所處的環(huán)境復(fù)雜、且涉及的傳感器種類數(shù)量眾多,所以通常需要對BS接收的數(shù)據(jù)進行可信性評估,只有通過評估的數(shù)據(jù)才能用于關(guān)鍵決策。一般使用溯源(Provenance)評估數(shù)據(jù)的可信性。Provenance記錄了一個數(shù)據(jù)從產(chǎn)生、轉(zhuǎn)發(fā)至到達BS涉及的所有節(jié)點,以及在這些節(jié)點上對該數(shù)據(jù)實施的操作。因此Provenance會隨著數(shù)據(jù)傳輸鏈路長度的增加而快速膨脹,當數(shù)據(jù)量較大時通常采用分段傳輸以克服Provenance的數(shù)據(jù)量過載問題。針對現(xiàn)有的Provenance分段傳輸方法要求BS必須正確收到全部分段數(shù)據(jù)之后才能實施Provenance解壓縮、且平均壓縮比較低的問題,本文設(shè)計了一種基于逐級重建的Provenance增量壓縮方法,即通過對WSN進行多級分簇管理并應(yīng)用整數(shù)具有唯一的素因子分解這一性質(zhì),實現(xiàn)了在編碼時計算節(jié)點之間ID的增量信息,在解碼時BS能夠按粒度從粗到細逐級精化重建Provenance,由此較為完善地解決了上述問題。理論分析和實驗數(shù)據(jù)均表明,與傳統(tǒng)的Provenance分段傳輸方法相比,該方法不僅具有更高的Provenance壓縮比,可有效節(jié)省傳輸能耗,而且由于引入了增量傳輸,所以魯棒性也更好。
[Abstract]:The continuous progress of wireless communication technology, sensor technology and embedded technology has promoted the rapid development of low-cost, low-power, multi-function sensor nodes. Thus, this wireless sensor network composed of micro-sensor nodes, Wireless Sensor Networks, is rapidly popularized. In WSN, sensor nodes collect information about perceived objects in monitoring areas. Then the information is sent to the base station by wireless communication. However, due to the complexity of the environment and the large number of sensors involved in the WSN, it is usually necessary to evaluate the credibility of the data received by the BS. Only evaluated data can be used in key decisions. (generally speaking, traceability is used to assess the credibility of the data .Provenance records a data that is generated, forwarded to all nodes involved in the BS, And the operation of the data on these nodes. Therefore, the Provenance expands rapidly as the length of the data transmission link increases, In order to overcome the problem of Provenance data overload, the existing Provenance segmental transmission methods require BS to receive all segmented data correctly before Provenance decompression is implemented. And the average compression is low. In this paper, we design a Provenance incremental compression method based on stepwise reconstruction, that is, by multi-level clustering management of WSN and the application of integer, we have the property of unique prime factor decomposition. The incremental information of the ID between nodes is calculated in the coding process, and the BS can reconstruct the Provenance from coarse to fine granularity step by step in decoding. The above problems are solved perfectly. The theoretical analysis and experimental data show that, Compared with the traditional Provenance segmented transmission method, this method not only has higher Provenance compression ratio, but also has better robustness because of the introduction of incremental transmission.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9;TN929.5

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