本文選題：無線傳感器網(wǎng)絡(luò)　切入點(diǎn)：數(shù)據(jù)融合　出處：《西南大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在無線傳感器網(wǎng)絡(luò)(WSNs,Wireless Sensor Networks)中,為了對(duì)感知區(qū)域進(jìn)行實(shí)時(shí)有效地監(jiān)測(cè)以及讓采集的信息更加精準(zhǔn)與全面,網(wǎng)絡(luò)往往會(huì)布置大量的不同種類的傳感器節(jié)點(diǎn)。因此,無線傳感器網(wǎng)絡(luò)中感知的數(shù)據(jù)具有大量、多樣和高速的特點(diǎn)。此外,無線傳感器節(jié)點(diǎn)是隨機(jī)分布在感知區(qū)域內(nèi),所以網(wǎng)絡(luò)中大部分節(jié)點(diǎn)存在感知區(qū)域重疊,這造成了感知數(shù)據(jù)冗余。其次,由于無線傳感器節(jié)點(diǎn)的能源大多來源于微型電池,電池的能量有限,而冗余數(shù)據(jù)的傳輸會(huì)造成額外能量的消耗,所以消除傳輸中的數(shù)據(jù)冗余,降低能量消耗顯得非常重要。數(shù)據(jù)融合是一種有效消除數(shù)據(jù)冗余的方法,因此,為解決無線傳感器網(wǎng)絡(luò)傳輸中數(shù)據(jù)冗余會(huì)消耗大量能量的問題,本文主要采用主成分分析方法來消除簇內(nèi)融合度高的數(shù)據(jù),減少能量消耗,提高能量效率。本文的主要內(nèi)容如下:針對(duì)無線傳感器網(wǎng)絡(luò)中數(shù)據(jù)傳輸?shù)娜哂鄦栴},本文提出了一種基于數(shù)據(jù)相似性的數(shù)據(jù)融合算法;其原理是數(shù)據(jù)集越相似,數(shù)據(jù)的壓縮比越小。首先,為把數(shù)據(jù)相似度高的節(jié)點(diǎn)劃分到一個(gè)簇內(nèi),提高數(shù)據(jù)的融合度,本文提出了一種新的分簇方法,同時(shí)為避免簇內(nèi)節(jié)點(diǎn)的傳輸沖突,提出了限定簇內(nèi)節(jié)點(diǎn)數(shù)量的能量模型,實(shí)現(xiàn)了簇內(nèi)能量均衡的消耗。隨后,為了最大限度地消除傳輸過程中的數(shù)據(jù)冗余,本文在每個(gè)簇的簇頭采用了基于主成分分析的數(shù)據(jù)融合算法,最終減少了冗余數(shù)據(jù)的傳輸以及能量的消耗。最后,實(shí)際仿真結(jié)果表明:與LEACH-PCA與Kmeans-PCA算法相比,本文的算法能分別有效減少21.1%和13.4%的數(shù)據(jù)傳輸量。其次,相比LEACH-PCA算法,本文的算法能有效延長(zhǎng)7.8%的網(wǎng)絡(luò)生存時(shí)間。當(dāng)節(jié)點(diǎn)數(shù)目大于300時(shí),算法的能量消耗明顯低于其它兩種算法。因此,所提出的數(shù)據(jù)融合算法和分簇算法能明顯提高網(wǎng)絡(luò)的性能。
[Abstract]:In wireless sensor networks (WSN), in order to effectively monitor the perceptual region and make the collected information more accurate and comprehensive, the network often has a large number of sensor nodes of different types. In addition, wireless sensor nodes are randomly distributed in the perceptual region, so most of the nodes in the network have overlaps of perceptual regions. This results in sensing data redundancy. Secondly, because most of the energy of wireless sensor nodes comes from microbatteries, the energy of the batteries is limited, and the transmission of redundant data results in additional energy consumption, so the data redundancy in transmission is eliminated. It is very important to reduce energy consumption. Data fusion is an effective method to eliminate data redundancy. Therefore, in order to solve the problem that data redundancy will consume a lot of energy in wireless sensor network transmission, In this paper, the principal component analysis (PCA) method is used to eliminate the data with high degree of convergence, reduce energy consumption and improve energy efficiency. The main contents of this paper are as follows: aiming at the redundancy of data transmission in wireless sensor networks, In this paper, a data fusion algorithm based on data similarity is proposed. The principle is that the more similar the data set, the smaller the compression ratio of data. Firstly, in order to divide the nodes with high data similarity into a cluster, the fusion degree of data can be improved. In this paper, a new clustering method is proposed. In order to avoid the transmission conflict of nodes in the cluster, an energy model is proposed to limit the number of nodes in the cluster, which realizes the consumption of energy balance within the cluster. In order to eliminate the data redundancy in the transmission process to the maximum extent, this paper adopts a data fusion algorithm based on principal component analysis in the cluster heads of each cluster, which ultimately reduces the transmission of redundant data and the consumption of energy. Finally, The simulation results show that compared with the LEACH-PCA and Kmeans-PCA algorithms, the proposed algorithm can effectively reduce the data transmission by 21.1% and 13.4% respectively. Secondly, compared with the LEACH-PCA algorithm, the proposed algorithm is more efficient than the LEACH-PCA algorithm. When the number of nodes is greater than 7.8%, the energy consumption of the algorithm is obviously lower than that of the other two algorithms. Therefore, the proposed data fusion algorithm and clustering algorithm can obviously improve the performance of the network.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN929.5;TP212.9

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本文編號(hào)：1606716