本文選題：無線傳感器網(wǎng)絡(luò) + 數(shù)據(jù)聚合��； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：無線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,WSNs)利用各種各樣的傳感器節(jié)點(diǎn),實(shí)時(shí)采集和監(jiān)測網(wǎng)絡(luò)區(qū)域內(nèi)的各種信息,并將這些信息通過無線網(wǎng)絡(luò)發(fā)送到匯聚節(jié)點(diǎn)(sink)。因此,WSNs在環(huán)境監(jiān)測、移動(dòng)醫(yī)療、交通監(jiān)測等諸多領(lǐng)域都具有非常廣闊的應(yīng)用前景。通常,無線傳感器網(wǎng)絡(luò)能量有限,節(jié)點(diǎn)的數(shù)據(jù)傳輸將消耗大量的能量,因此如何減少網(wǎng)絡(luò)中的數(shù)據(jù)傳輸量,降低節(jié)點(diǎn)能量消耗,延長網(wǎng)絡(luò)的壽命成為無線傳感器網(wǎng)絡(luò)中研究的一個(gè)重點(diǎn)。數(shù)據(jù)聚合是無線傳感器網(wǎng)絡(luò)數(shù)據(jù)處理的重要技術(shù),通過對(duì)采集或者接收到的數(shù)據(jù)進(jìn)行聚合處理,可以有效地去除冗余數(shù)據(jù)。本文重點(diǎn)研究基于時(shí)空相關(guān)性的WSNs數(shù)據(jù)聚合算法,出發(fā)點(diǎn)是為了減少網(wǎng)內(nèi)數(shù)據(jù)量,節(jié)約節(jié)點(diǎn)的能量消耗,最后達(dá)到延長網(wǎng)絡(luò)壽命的目的。首先針對(duì)節(jié)點(diǎn)數(shù)據(jù)的空間相關(guān)性,提出了基于空間自相關(guān)模型的數(shù)據(jù)聚合算法SMDA(Spatial auto-regression Model based Data Aggregation)。在SMDA中,簇頭節(jié)點(diǎn)收集簇內(nèi)未休眠節(jié)點(diǎn)的信息,接著采用節(jié)點(diǎn)調(diào)度算法調(diào)度節(jié)點(diǎn)休眠,并利用Delaunay三角剖分算法和空間自相關(guān)模型預(yù)測休眠節(jié)點(diǎn)的缺失數(shù)據(jù),最后對(duì)所有數(shù)據(jù)進(jìn)行聚合操作并發(fā)送給匯聚節(jié)點(diǎn)。在此基礎(chǔ)之上,基于節(jié)點(diǎn)數(shù)據(jù)之間的時(shí)間相關(guān)性和空間相關(guān)性,提出了一種基于空間自相關(guān)模型和灰色模型的數(shù)據(jù)聚合算法SGDA(Spatial auto-regression model and Grey model based Data Aggregation)。SGDA考慮節(jié)點(diǎn)數(shù)據(jù)的時(shí)間和空間相關(guān)性,以誤差絕對(duì)值之和最小為最優(yōu)準(zhǔn)則,建立組合預(yù)測模型,進(jìn)一步減小了預(yù)測的誤差。仿真實(shí)驗(yàn)表明,本文提出的算法能夠很好的減少網(wǎng)內(nèi)冗余數(shù)據(jù)、均衡節(jié)點(diǎn)能耗、延長網(wǎng)絡(luò)壽命,并保證較高的數(shù)據(jù)精度。
[Abstract]:Wireless Sensor Networks (WSNs) uses a variety of sensor nodes to collect and monitor all kinds of information in the network area in real time, and send the information to the convergent node via wireless network. Therefore, WSNs have a broad application prospect in many fields, such as environmental monitoring, mobile medicine, traffic monitoring and so on. In general, wireless sensor networks have limited energy, and the data transmission of nodes will consume a lot of energy, so how to reduce the amount of data transmission in the network and reduce the energy consumption of nodes, Prolonging the lifetime of wireless sensor networks (WSN) has become an important issue in wireless sensor networks (WSN). Data aggregation is an important technology of data processing in wireless sensor networks. The redundant data can be removed effectively by aggregating the collected or received data. This paper focuses on the WSNs data aggregation algorithm based on spatio-temporal correlation, which aims at reducing the amount of data in the network, saving the energy consumption of nodes, and finally prolonging the network life. Firstly, aiming at the spatial correlation of node data, a spatial autocorrelation model based data aggregation algorithm SMDA(Spatial auto-regression Model based Data aggregation is proposed. In SMDA, the cluster head node collects the information of the non-dormant node in the cluster, then uses the node scheduling algorithm to schedule the node sleep, and uses the Delaunay triangulation algorithm and the spatial autocorrelation model to predict the missing data of the dormant node. Finally, all the data are aggregated and sent to the aggregation node. On this basis, based on the temporal and spatial correlation between node data, In this paper, a data aggregation algorithm based on spatial autocorrelation model and grey model is proposed. Considering the temporal and spatial correlation of node data, a combined prediction model is established using the minimum of absolute error as the optimal criterion. The error of prediction is further reduced. Simulation results show that the proposed algorithm can reduce redundant data, balance node energy consumption, prolong network life and ensure high data accuracy.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;TN929.5

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