本文關(guān)鍵詞： 無(wú)線通信 傳感器 數(shù)據(jù)壓縮 BF506 帶寬　出處：《燕山大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著嵌入式技術(shù)、傳感器技術(shù)以及無(wú)線通信技術(shù)的不斷發(fā)展,具有數(shù)據(jù)采集、處理以及無(wú)線傳感器網(wǎng)絡(luò)技術(shù)被廣泛應(yīng)用于當(dāng)今社會(huì)的各個(gè)領(lǐng)域。在橋梁健康監(jiān)測(cè)方面,通常在橋體上鋪設(shè)大量的無(wú)線傳感器節(jié)點(diǎn),通過(guò)采集橋梁結(jié)構(gòu)狀態(tài)的相關(guān)數(shù)據(jù)來(lái)為橋梁在特殊氣候、復(fù)雜交通環(huán)境和橋梁結(jié)構(gòu)狀況異常時(shí)發(fā)布預(yù)警信號(hào),為橋梁的管理、養(yǎng)護(hù)與維修提供科學(xué)的依據(jù)。無(wú)線傳感器網(wǎng)絡(luò)需要實(shí)時(shí)的采集并發(fā)送大量數(shù)據(jù)信息,這對(duì)整個(gè)網(wǎng)絡(luò)的通信帶寬帶來(lái)很大的壓力。另外,無(wú)線數(shù)據(jù)的傳輸占整個(gè)節(jié)點(diǎn)能耗的絕大部分,因此要想盡可能延長(zhǎng)傳感器節(jié)點(diǎn)壽命就必須減少數(shù)據(jù)傳輸量,而數(shù)據(jù)壓縮就是解決上述問(wèn)題的有效手段。本文首先針對(duì)橋梁健康監(jiān)測(cè)問(wèn)題介紹了基于結(jié)構(gòu)健康監(jiān)測(cè)的無(wú)線傳感器拓?fù)浣Y(jié)構(gòu),并且從節(jié)點(diǎn)自身能耗和網(wǎng)絡(luò)帶寬受限等多方面論證了采用數(shù)據(jù)壓縮算法的必要性。其次,針對(duì)橋梁振動(dòng)數(shù)據(jù)的采集、處理和傳輸需求,本文設(shè)計(jì)了基于嵌入式技術(shù)的無(wú)線傳感器前端節(jié)點(diǎn),該節(jié)點(diǎn)包含振動(dòng)數(shù)據(jù)采集模塊、DSP數(shù)據(jù)處理模塊以及無(wú)線收發(fā)模塊。該模塊對(duì)振動(dòng)數(shù)據(jù)的采樣精度達(dá)到16位,采樣速率最高達(dá)到250Kbps。另外,本文提出了一種基于采樣數(shù)據(jù)相關(guān)性的壓縮算法,該算法能夠?qū)φ駝?dòng)數(shù)據(jù)進(jìn)行無(wú)損壓縮。要內(nèi)容包含兩部分,分別為數(shù)據(jù)格式的定義和壓縮算法的實(shí)現(xiàn)。數(shù)據(jù)格式采用了可變長(zhǎng)度幀格式獲得最優(yōu)壓縮效果。壓縮算法主要分為三步,分別為計(jì)算差值、相鄰數(shù)據(jù)按位異或以及數(shù)據(jù)分組組合。通過(guò)實(shí)驗(yàn)仿真表明,該種方法能夠有效的對(duì)數(shù)據(jù)進(jìn)行壓縮處理,并且相較于經(jīng)典的霍夫曼壓縮編碼以及ZLIB壓縮編碼具有明顯的優(yōu)勢(shì)。為了進(jìn)一步驗(yàn)證該壓縮方法的性能,在設(shè)計(jì)的傳感器節(jié)點(diǎn)平臺(tái)上進(jìn)行實(shí)際驗(yàn)證,通過(guò)實(shí)際平臺(tái)驗(yàn)證,該算法能夠在保證對(duì)數(shù)據(jù)有效壓縮的基礎(chǔ)上能夠?qū)崿F(xiàn)數(shù)據(jù)的實(shí)時(shí)性傳輸。
[Abstract]:With the continuous development of embedded technology, sensor technology and wireless communication technology, data acquisition, processing and wireless sensor network technology are widely used in all fields of society. Usually, a large number of wireless sensor nodes are laid on the bridge body. By collecting the relevant data of the bridge structure state, early warning signals are issued for the bridge when the special climate, complex traffic environment and the bridge structure condition are abnormal, so as to manage the bridge. Maintenance and maintenance provide scientific basis. Wireless sensor networks need to collect and send a large amount of data in real time, which brings great pressure on the communication bandwidth of the whole network. In addition, Wireless data transmission accounts for most of the energy consumption of the whole node, so if we want to extend the lifetime of sensor nodes as much as possible, we must reduce the amount of data transmission. Data compression is an effective method to solve the above problems. Firstly, the topology of wireless sensor based on structural health monitoring is introduced in this paper. The necessity of adopting data compression algorithm is demonstrated from the aspects of node energy consumption and network bandwidth limitation. Secondly, aiming at the need of collecting, processing and transmitting bridge vibration data, In this paper, a wireless sensor front-end node based on embedded technology is designed. The node includes vibration data acquisition module, DSP data processing module and wireless transceiver module. The sampling rate is up to 250kbps.In addition, this paper presents a compression algorithm based on the correlation of sampled data, which can compress the vibration data without loss. The data format adopts the variable length frame format to obtain the optimal compression effect. The compression algorithm is divided into three steps, the difference is calculated. The experimental results show that the proposed method can effectively compress and process the data. And compared with the classical Hoffman compression coding and ZLIB compression coding, it has obvious advantages. In order to further verify the performance of the compression method, a practical verification is carried out on the sensor node platform designed, and verified by the actual platform. The algorithm can achieve real-time data transmission on the basis of data compression.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U446;TN929.5;TP212.9

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本文編號(hào)：1494819