本文關(guān)鍵詞：無(wú)線傳感器網(wǎng)絡(luò)的數(shù)據(jù)聚合研究　出處：《西安電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 無(wú)線傳感器網(wǎng)絡(luò) 數(shù)據(jù)聚合 非均勻網(wǎng)格 統(tǒng)計(jì)量

【摘要】：無(wú)線傳感器網(wǎng)絡(luò)是由隨機(jī)分布在部署地區(qū)內(nèi)的大量微型傳感器節(jié)點(diǎn)組成,以無(wú)線通信方式構(gòu)成的一個(gè)多跳的自組織網(wǎng)絡(luò)系統(tǒng),其目標(biāo)是互相協(xié)作地感知、采集和處理部署地區(qū)內(nèi)感知對(duì)象的信息,并且實(shí)時(shí)地發(fā)送給用戶。由于無(wú)線傳感器網(wǎng)絡(luò)中的節(jié)點(diǎn)是密集分布在監(jiān)測(cè)地區(qū)中,相鄰節(jié)點(diǎn)收集到的數(shù)據(jù)具有較大的相關(guān)性,所以網(wǎng)絡(luò)中存在冗余信息。另外,環(huán)境對(duì)傳感器節(jié)點(diǎn)收集到的數(shù)據(jù)的準(zhǔn)確性有較大影響,節(jié)點(diǎn)有可能收集到誤差較大的數(shù)據(jù)。然而,傳感器節(jié)點(diǎn)的能量是有限的,所以網(wǎng)絡(luò)的生存周期有限,對(duì)冗余和誤差數(shù)據(jù)進(jìn)行適當(dāng)?shù)奶幚韥?lái)延長(zhǎng)網(wǎng)絡(luò)壽命并提高數(shù)據(jù)的準(zhǔn)確性是非常必要的。傳感器節(jié)點(diǎn)可以直接從環(huán)境中收集數(shù)據(jù),也可以從其他傳感器節(jié)點(diǎn)接收數(shù)據(jù)。數(shù)據(jù)聚合指的是傳感器節(jié)點(diǎn)對(duì)收集的數(shù)據(jù)進(jìn)行適當(dāng)?shù)奶幚?去除冗余或者誤差較大的信息,合理地把多個(gè)分組合并成為一組,并向下一跳節(jié)點(diǎn)進(jìn)行轉(zhuǎn)發(fā)。這樣不僅可以減少網(wǎng)絡(luò)中的數(shù)據(jù)傳輸量,減少Sink節(jié)點(diǎn)接收到的數(shù)據(jù)量,而且可以有效地提高監(jiān)測(cè)結(jié)果的準(zhǔn)確性,能更好地降低節(jié)點(diǎn)的能量消耗。數(shù)據(jù)聚合算法的基本思想是充分發(fā)揮傳感器節(jié)點(diǎn)自身的數(shù)據(jù)處理能力,將其收集的大量原始數(shù)據(jù)篩選、聚合并且傳輸。采用高效的數(shù)據(jù)聚合算法來(lái)增加網(wǎng)絡(luò)的壽命是當(dāng)前的研究熱點(diǎn)之一�；诜蔷鶆虼氐臄�(shù)據(jù)聚合是一種高效可靠的延長(zhǎng)網(wǎng)絡(luò)壽命的算法。本文提出了兩種數(shù)據(jù)聚合算法:(1)針對(duì)同構(gòu)的無(wú)線傳感器網(wǎng)絡(luò),提出了基于非均勻網(wǎng)格成簇的數(shù)據(jù)聚合新算法。算法根據(jù)Sink節(jié)點(diǎn)的位置把網(wǎng)絡(luò)劃分為大小不等的網(wǎng)格,在每個(gè)網(wǎng)格中選取剩余能量最高的節(jié)點(diǎn)作為簇首節(jié)點(diǎn),其余節(jié)點(diǎn)根據(jù)就近原則選擇性的加入簇,進(jìn)而形成基于Sink節(jié)點(diǎn)位置的非均勻簇。每個(gè)簇內(nèi)的所有節(jié)點(diǎn)對(duì)收集到的數(shù)據(jù)進(jìn)行格拉布斯預(yù)處理,采用自適應(yīng)加權(quán)數(shù)據(jù)聚合算法將有效數(shù)據(jù)以單跳的形式傳輸?shù)酱厥坠?jié)點(diǎn)。然后,簇首節(jié)點(diǎn)以多跳的方式給別的簇首節(jié)點(diǎn)或者Sink節(jié)點(diǎn)傳輸數(shù)據(jù)。(2)針對(duì)非同構(gòu)的無(wú)線傳感器網(wǎng)絡(luò),我們提出一種新的基于數(shù)據(jù)濾波的雙層數(shù)據(jù)聚合算法。該算法與上一個(gè)算法使用相同的非均勻網(wǎng)格成簇方法,簇內(nèi)的成員節(jié)點(diǎn)在傳輸數(shù)據(jù)之前根據(jù)統(tǒng)計(jì)模型進(jìn)行濾波處理,刪除誤差較大的數(shù)據(jù)和冗余數(shù)據(jù),簇內(nèi)節(jié)點(diǎn)通過(guò)順序加權(quán)數(shù)據(jù)聚合算法以單跳的方式給簇首節(jié)點(diǎn)發(fā)送數(shù)據(jù),簇首節(jié)點(diǎn)以多跳的形式將聚合后的數(shù)據(jù)傳輸至Sink節(jié)點(diǎn)進(jìn)行下一層次的數(shù)據(jù)聚合。我們將提出的算法與其他的數(shù)據(jù)聚合算法通過(guò)仿真進(jìn)行了對(duì)比,結(jié)果顯示所提出的數(shù)據(jù)聚合算法可以明顯降低傳感器節(jié)點(diǎn)的能耗,實(shí)現(xiàn)最大化的數(shù)據(jù)聚合,并且簇首節(jié)點(diǎn)的選取機(jī)制更有效地均衡了網(wǎng)絡(luò)的能耗,減少了節(jié)點(diǎn)的死亡數(shù)目,進(jìn)而有效地延長(zhǎng)了傳感器網(wǎng)絡(luò)的壽命,同時(shí)也提高了數(shù)據(jù)的準(zhǔn)確性和網(wǎng)絡(luò)的魯棒性。
[Abstract]:Wireless sensor network is composed by the random distribution in the area of deployment in a large number of micro sensor nodes, wireless communication mode to constitute a multi hop self-organizing network system, its goal is to collaborate perception, acquisition and processing of the deployment area perceived objects and real-time information sent to the user. Because the nodes in WSN are densely distributed in the monitoring area, the data collected by the neighboring nodes are highly correlated, so redundant information exists in the network. In addition, the environment has a great impact on the accuracy of the data collected by the sensor nodes, and the nodes may be able to collect data with larger error. However, the energy of the sensor nodes is limited, so the lifetime of the network is limited. It is very necessary to process the redundant and error data appropriately to prolong the network life and improve the accuracy of the data. Sensor nodes can collect data directly from the environment, or receive data from other sensor nodes. Data aggregation refers to the proper processing of data collected by sensor nodes, removing redundant or error information, reasonably merging multiple groups into one group and forwarding them to the next hop node. In this way, we can not only reduce data transmission in network, reduce the amount of data received by Sink nodes, but also effectively improve the accuracy of monitoring results, and better reduce the energy consumption of nodes. The basic idea of data aggregation algorithm is to give full play to the data processing ability of sensor nodes, and collect, collect and transmit a large amount of raw data collected by them. The use of efficient data aggregation algorithms to increase the lifetime of the network is one of the hotspots of current research. Data aggregation based on nonuniform clusters is an efficient and reliable algorithm for prolonging network life. In this paper, two kinds of data aggregation algorithms are proposed: (1) a new algorithm for data aggregation based on heterogeneous grid is proposed for isomorphic wireless sensor networks. According to the location of Sink nodes, the algorithm divides the network into grid of different sizes. In each grid, the nodes with the highest remaining energy are selected as cluster heads. The rest nodes join clusters selectively according to the proximity principle, and then form an uneven cluster based on Sink node location. All nodes in each cluster are preprocessed by grabbas, and the adaptive weighted data aggregation algorithm is applied to transmit effective data to cluster head nodes in a single hop form. Then, the cluster head node transmits data to other cluster head nodes or Sink nodes in a multi hop way. (2) for the non isomorphic wireless sensor networks, we propose a new double data aggregation algorithm based on data filtering. Non uniform grid clustering method and the algorithm on an algorithm using the same, member nodes within the cluster are filtered according to the statistical model in data transmission, delete the error data and redundant data, cluster nodes send the data to the cluster head nodes in single hop mode by sequentially weighted data aggregation algorithm, cluster head the nodes in a multi hop form to transfer data to the Sink node after polymerization to the next level of data aggregation. We will present the algorithm with other data aggregation algorithm by comparing the simulation results, the results show that the proposed data aggregation algorithm can significantly reduce the energy consumption of sensor nodes, realize the maximization of data aggregation, and the mechanism of selection of cluster head nodes more effectively balance the network energy consumption, reduce the number of dead nodes, and then prolong the lifetime of the sensor network, but also improve the robustness and accuracy of the data network.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP212.9;TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫彥清;彭艦;劉唐;陳曉海;;基于動(dòng)態(tài)分區(qū)的無(wú)線傳感器網(wǎng)絡(luò)非均勻成簇路由協(xié)議[J];通信學(xué)報(bào);2014年01期

2 何學(xué)文;鄭樂(lè)平;孫汗;;無(wú)線傳感器網(wǎng)絡(luò)簇內(nèi)多傳感器數(shù)據(jù)融合算法[J];傳感器與微系統(tǒng);2014年01期

3 陳零;王建新;張士庚;奎曉燕;;無(wú)線傳感器網(wǎng)絡(luò)中基于樹的能量高效分布式精確數(shù)據(jù)收集算法[J];電子學(xué)報(bào);2013年09期

4 ;An energy-driven unequal clustering protocol for heterogeneous wireless sensor networks[J];Journal of Control Theory and Applications;2011年01期

5 劉玉華;趙永鋒;許凱華;高景菊;;無(wú)線傳感器網(wǎng)絡(luò)LEACH協(xié)議的改進(jìn)[J];計(jì)算機(jī)工程與應(yīng)用;2010年17期

6 楊云;李斌;高峰;石婷婷;劉鳳玉;;無(wú)線傳感器網(wǎng)絡(luò)簇內(nèi)優(yōu)化的最小跳數(shù)路由算法[J];計(jì)算機(jī)應(yīng)用與軟件;2010年02期

7 李成法;陳貴海;葉懋;吳杰;;一種基于非均勻分簇的無(wú)線傳感器網(wǎng)絡(luò)路由協(xié)議[J];計(jì)算機(jī)學(xué)報(bào);2007年01期

8 趙澤;崔莉;;一種基于無(wú)線傳感器網(wǎng)絡(luò)的遠(yuǎn)程醫(yī)療監(jiān)護(hù)系統(tǒng)[J];信息與控制;2006年02期

9 于海斌,曾鵬,王忠鋒,梁英,尚志軍;分布式無(wú)線傳感器網(wǎng)絡(luò)通信協(xié)議研究[J];通信學(xué)報(bào);2004年10期

10 馬祖長(zhǎng),孫怡寧,梅濤;無(wú)線傳感器網(wǎng)絡(luò)綜述[J];通信學(xué)報(bào);2004年04期

，

本文編號(hào)：1338047