本文關(guān)鍵詞：面向傳感器網(wǎng)絡(luò)大數(shù)據(jù)傳輸應(yīng)用的數(shù)據(jù)壓縮與傳輸優(yōu)化算法的研究與應(yīng)用　出處：《電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 無線傳感器網(wǎng)絡(luò) 相似性分簇 最小傳輸代價 分簇數(shù)據(jù)壓縮

【摘要】：無線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,WSNs)是由大量節(jié)點(diǎn)自組織而成,集信息采集、處理和傳輸為一體的網(wǎng)絡(luò),被廣泛應(yīng)用于環(huán)境監(jiān)測、棲息地監(jiān)控和地震探測等諸多領(lǐng)域。WSNs中節(jié)點(diǎn)的能量是有限的,而數(shù)據(jù)傳輸占用了大部分能耗。因此,如何減少網(wǎng)絡(luò)數(shù)據(jù)傳輸能耗成為了WSNs的關(guān)鍵問題。本文在研究了WSNs中分簇路由協(xié)議和數(shù)據(jù)壓縮兩種節(jié)約數(shù)據(jù)傳輸能耗技術(shù)的基礎(chǔ)上,結(jié)合WSNs環(huán)境分布中呈現(xiàn)的區(qū)域特性,提出了基于節(jié)點(diǎn)相似性的分簇算法(Sensor Similarity-based Clustering,SSC)和基于最小傳輸代價的路由算法(Minimum Transmission Cost-based Routing,MTCR),并結(jié)合這兩者設(shè)計了基于節(jié)點(diǎn)相似性的分簇壓縮傳輸方案(SSC-based Data Compression and Transmission,SSCDCT)。本文的主要工作如下:1)提出了基于節(jié)點(diǎn)相似性的分簇算法(SSC)。通過對WSNs工作環(huán)境的區(qū)域性進(jìn)行研究,提出了節(jié)點(diǎn)相似性的度量模型,并依據(jù)該模型提出了SSC算法。SSC算法將區(qū)域內(nèi)的相似節(jié)點(diǎn)劃分到同一個簇中,使得簇內(nèi)節(jié)點(diǎn)具有較高的相似性,從而利于數(shù)據(jù)的壓縮工作。壓縮算法能挖掘和消除更多數(shù)據(jù)中的冗余,減少數(shù)據(jù)的傳輸量。2)提出了基于最小傳輸代價的路由算法(MTCR)。通過分析現(xiàn)有分簇協(xié)議在簇內(nèi)、簇間數(shù)據(jù)路由方式在大規(guī)模傳感器網(wǎng)絡(luò)中的不足,結(jié)合傳感器節(jié)點(diǎn)的能量消耗模型,提出了MTCR。MTCR通過控制簇首覆蓋范圍和節(jié)點(diǎn)度大小實(shí)現(xiàn)網(wǎng)絡(luò)中單跳與多跳的混合路由方式,最大限度地節(jié)約網(wǎng)絡(luò)傳輸能耗和平衡節(jié)點(diǎn)的能量消耗。3)提出了基于節(jié)點(diǎn)相似性分簇的壓縮方案(SSCDCT)。SSCDCT利用SSC將相似節(jié)點(diǎn)的聚集起來,并用壓縮算法對它們的數(shù)據(jù)進(jìn)行壓縮,然后利用MTCR路由協(xié)議傳輸壓縮后的數(shù)據(jù)。SSCDCT將WSNs數(shù)據(jù)壓縮傳輸分為了三層,層次之間相互協(xié)作,以實(shí)現(xiàn)最大限度地減少數(shù)據(jù)傳輸量和網(wǎng)絡(luò)的傳輸能耗,實(shí)現(xiàn)延長網(wǎng)絡(luò)生存壽命的目的。實(shí)驗(yàn)和案例研究表明,相對LEACH等分簇協(xié)議而言,SSC使簇內(nèi)節(jié)點(diǎn)的平均幅度相似性提高了15%左右,趨勢相似性提升了8%左右;MTCR相比LEACH,LEACH-C首節(jié)點(diǎn)死亡FND時間延遲了至少14.3%以上,半數(shù)節(jié)點(diǎn)死亡HND時間延遲了25.5%以上;SSCDCT相比使用LEACH-C的分簇壓縮網(wǎng)絡(luò),其FND時間延遲了12.1%以上,HND時間延遲了14.5%以上。
[Abstract]:Wireless Sensor Networks (WSNs) is a network composed of a large number of nodes, which collect, process and transmit information. It is widely used in many fields, such as environmental monitoring, habitat monitoring and seismic detection. The energy of nodes in WSNs is limited, and data transmission takes up most of the energy consumption. How to reduce the energy consumption of network data transmission has become the key problem of WSNs. In this paper, we study the clustering routing protocol and data compression in WSNs to reduce the energy consumption of data transmission. Considering the regional characteristics of WSNs environment distribution, a clustering algorithm based on node similarity is proposed, which is called Sensor Similarity-based Clustering. SSCs and the minimum Transmission Cost-based routing algorithm (MTCRs). Combining these two schemes, a cluster compression transmission scheme based on node similarity is designed, which is based on SSC-based Data Compression and Transmission. The main work of this paper is as follows: 1) A clustering algorithm based on node similarity is proposed. The region of WSNs working environment is studied. According to this model, the SSC algorithm is proposed to divide the similar nodes in the region into the same cluster, which makes the nodes in the cluster have high similarity. The compression algorithm can mine and eliminate the redundancy of more data. To reduce the amount of data transmission. (2) A routing algorithm based on minimum transmission cost is proposed. By analyzing the existing clustering protocols in the cluster, the data routing between clusters in large-scale sensor networks is insufficient. Combined with the energy consumption model of sensor nodes, a hybrid routing scheme of single-hop and multi-hop is proposed by MTCR.MTCR, which controls the coverage of cluster heads and the size of nodes. In this paper, we propose a compression scheme based on node similarity clustering (SSCDCT). SSCDCT uses SSC to aggregate similar nodes. The compression algorithm is used to compress their data, and then the compressed data is transmitted by MTCR routing protocol. The compressed data is divided into three layers, which cooperate with each other. In order to minimize the amount of data transmission and network transmission energy consumption, to achieve the purpose of prolonging the network lifetime. Experiments and case studies show that compared to the LEACH equal clustering protocol. SSC increased the average amplitude similarity of cluster nodes by about 15%, and increased the trend similarity by about 8%. Compared with LEACH-C, MTCR delayed the FND time of the first node of LEACH-C by more than 14.3%, and the HND time of half of the nodes was delayed by more than 25.5%. Compared with the clustering compression network using LEACH-C, the FND time of SSCDCT is delayed by more than 12.1% and more than 14.5%.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212.9;TN929.5
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本文編號：1431689