本文選題：無(wú)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

【摘要】：傳感器網(wǎng)絡(luò)技術(shù)作為21世紀(jì)最重要的技術(shù)之一,很大程度上改變了我們的生活。通過(guò)傳感器獲取的傳感數(shù)據(jù),再通過(guò)物聯(lián)網(wǎng)的使用將會(huì)使數(shù)據(jù)變得更加有用,而物聯(lián)網(wǎng)的應(yīng)用則比傳感器網(wǎng)絡(luò)更加多樣化,因此物聯(lián)網(wǎng)可以看成是將傳感器網(wǎng)絡(luò)的功能實(shí)現(xiàn)了更大的延伸。在大型傳感器網(wǎng)絡(luò)監(jiān)測(cè)環(huán)境中會(huì)布署大量的傳感器節(jié)點(diǎn),在節(jié)點(diǎn)密集的區(qū)域,傳感器之間的感知數(shù)據(jù)存在多種差異性和時(shí)空相關(guān)性。這些情況可能會(huì)導(dǎo)致多傳感器采集的數(shù)據(jù)之間可以存在著某些對(duì)應(yīng)的關(guān)系或者相似的數(shù)據(jù)。為了有效地提取信息,通常會(huì)將數(shù)據(jù)融合技術(shù)加入在數(shù)據(jù)收集的過(guò)程中,該技術(shù)主要目的就是在滿足一定的應(yīng)用監(jiān)測(cè)需求下,將多份數(shù)據(jù)通過(guò)去冗余實(shí)現(xiàn)更有效的傳輸。在成本和體積限制的情況下,無(wú)線傳感器網(wǎng)絡(luò)中的節(jié)點(diǎn)一般都是能源有限的。而無(wú)線傳感器網(wǎng)絡(luò)面向應(yīng)用需求設(shè)計(jì)可以通過(guò)引入數(shù)據(jù)融合技術(shù)獲得更大效益。本文通過(guò)深入研究典型的時(shí)空相關(guān)性數(shù)據(jù)融合算法的特點(diǎn)、原理和相應(yīng)的性能指標(biāo),結(jié)合應(yīng)用需求與無(wú)線傳感器網(wǎng)絡(luò)的采集數(shù)據(jù)之間的時(shí)空相關(guān)性,提出了相應(yīng)的傳感器網(wǎng)絡(luò)數(shù)據(jù)收集分簇模型,針對(duì)收集的數(shù)據(jù)之間仍存在的冗余性,設(shè)計(jì)了相應(yīng)的提升小波數(shù)據(jù)壓縮去冗余算法。本文主要研究?jī)?nèi)容和貢獻(xiàn)主要有:(1)本文針對(duì)無(wú)線傳感器網(wǎng)絡(luò)監(jiān)測(cè)區(qū)域環(huán)境中臨近節(jié)點(diǎn)之間采集的感知數(shù)據(jù)的時(shí)間和空間相關(guān)性,提出了一種基于應(yīng)用需求的傳感器網(wǎng)絡(luò)數(shù)據(jù)收集分簇模型。通過(guò)臨近區(qū)域的數(shù)據(jù)相關(guān)性大小,先選取一定剩余能量高、數(shù)據(jù)代表性強(qiáng)的節(jié)點(diǎn)作為簇成員,參與到傳感器網(wǎng)絡(luò)數(shù)據(jù)收集中,其次,根據(jù)這部分成員節(jié)點(diǎn)與其余普通節(jié)點(diǎn)的數(shù)據(jù)相關(guān)度比較來(lái)選擇一部分不可以被替代的普通節(jié)點(diǎn),并將其加入簇內(nèi)參與數(shù)據(jù)傳輸。并且根據(jù)剩余能量和實(shí)時(shí)數(shù)據(jù)的變化,動(dòng)態(tài)地調(diào)整網(wǎng)絡(luò)分簇的大小和檢測(cè)異常節(jié)點(diǎn)的數(shù)據(jù)變化。通過(guò)模擬仿真實(shí)驗(yàn),結(jié)果表明該分簇模型能夠在滿足一定的數(shù)據(jù)變化傳輸要求下節(jié)省更多的能量。(2)本文針對(duì)區(qū)域單個(gè)簇頭節(jié)點(diǎn)所收集到的簇內(nèi)節(jié)點(diǎn)數(shù)據(jù)的空間相關(guān)性和不同時(shí)刻傳輸?shù)臄?shù)據(jù)之間的時(shí)間相關(guān)性,我們提出了一種有效的消隱式提升小波數(shù)據(jù)壓縮算法,該算法不僅能夠去除大量的數(shù)據(jù)冗余,而且計(jì)算速度快,占用內(nèi)存少,在匯聚節(jié)點(diǎn)的恢復(fù)數(shù)據(jù)精度也比較高,從而使無(wú)線傳感器網(wǎng)絡(luò)獲得了更大的效益。
[Abstract]:Sensor network technology, one of the most important technologies of the 21st century, has greatly changed our lives. Sensor data obtained through sensors, and then the use of the Internet of things, will make the data more useful. The application of the Internet of things is more diversified than that of the sensor network, so the Internet of things can be regarded as a larger extension of the function of the sensor network. A large number of sensor nodes will be deployed in the monitoring environment of a large sensor network. In areas where nodes are dense, There are many differences and spatiotemporal correlations between sensors. These situations may lead to some corresponding relationship or similar data between the data collected by multi-sensor. In order to extract information effectively, Data fusion technology is usually added to the process of data collection. The main purpose of this technology is to achieve more efficient transmission of multiple data through de-redundancy under certain application monitoring requirements. The nodes in wireless sensor networks are generally limited in energy, but the application-oriented design of wireless sensor networks can achieve greater benefits by introducing data fusion technology. In this paper, the typical space-time phase is studied in depth. The characteristics of correlation data fusion algorithm, The principle and the corresponding performance index, combined with the temporal and spatial correlation between the application requirements and the collected data of the wireless sensor network, the corresponding clustering model of the sensor network data collection is proposed, aiming at the redundancy between the collected data. A corresponding lifting wavelet data compression and de-redundancy algorithm is designed in this paper. The main research contents and contributions of this paper are as follows: (1) this paper focuses on the temporal and spatial correlation of perceptual data collected between adjacent nodes in the wireless sensor network monitoring region. A cluster model of sensor network data collection based on application requirement is proposed. According to the data correlation of adjacent region, the nodes with high residual energy and strong data representation are selected as cluster members. Participate in the sensor network data collection, secondly, according to this part of the member nodes and the other common nodes data correlation comparison to select a part of the ordinary nodes can not be replaced, It is added to the cluster to participate in data transmission. According to the change of residual energy and real time data, the size of network clustering and the data change of detecting abnormal nodes are dynamically adjusted. The results show that the clustering model can save more energy under certain data transmission requirements.) in this paper, the spatial correlation of data collected by a single cluster head node and its transmission at different times are discussed. The temporal correlation between the data, We propose an effective lifting wavelet data compression algorithm. This algorithm can not only remove a large amount of data redundancy, but also has the advantages of fast computing speed, less memory, and high accuracy of data recovery at convergent nodes. Thus, the wireless sensor network gets more benefit.
【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(lèi)號(hào)】：TN929.5;TP212.9

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