【摘要】：現(xiàn)如今,無線感知技術(shù)已經(jīng)融入到我們生活,學(xué)習(xí),工作和娛樂當(dāng)中。而在無線感知數(shù)據(jù)收集網(wǎng)絡(luò)中低耗損,低延時(shí)數(shù)據(jù)傳輸一向是研究熱門方向。節(jié)點(diǎn)的數(shù)據(jù)采集與壓縮,高效的路由策略,數(shù)據(jù)處理,都包含對網(wǎng)絡(luò)低功耗的分析。本文分析了傳統(tǒng)分簇式感知網(wǎng)絡(luò)數(shù)據(jù)收集方法與基于混合壓縮感知的分簇式感知網(wǎng)絡(luò)數(shù)據(jù)收集方法。通過分析現(xiàn)有分簇式感知網(wǎng)絡(luò)數(shù)據(jù)收集算法,注意到網(wǎng)絡(luò)中數(shù)據(jù)傳輸延遲與吞吐量是衡量算法好壞的關(guān)鍵指標(biāo)。因此,本文提出了基于馬爾科夫模型選擇移動(dòng)sink路徑的數(shù)據(jù)收集方法。首先對監(jiān)控的無線傳感器網(wǎng)絡(luò)進(jìn)行區(qū)域劃分,然后應(yīng)用馬爾科夫模型預(yù)測節(jié)點(diǎn)位置,其次根據(jù)預(yù)測得到的節(jié)點(diǎn)數(shù)目期望值對網(wǎng)絡(luò)區(qū)域賦予優(yōu)先級,再對各區(qū)域進(jìn)行數(shù)據(jù)融合,最后得到最優(yōu)的移動(dòng)sink節(jié)點(diǎn)運(yùn)動(dòng)軌跡。本方法能適用于不同規(guī)模的網(wǎng)絡(luò),具有節(jié)省網(wǎng)絡(luò)能量、延長網(wǎng)絡(luò)生存周期、降低網(wǎng)絡(luò)整體延時(shí)、并防止數(shù)據(jù)溢出等優(yōu)點(diǎn)。其次,提出一種類BP神經(jīng)網(wǎng)絡(luò)的分簇傳感網(wǎng)絡(luò)數(shù)據(jù)收集方法。首先對監(jiān)控的無線傳感器網(wǎng)絡(luò)進(jìn)行初始化處理,依節(jié)點(diǎn)的GPS信息尋找網(wǎng)絡(luò)的地理中心位置,其次根據(jù)節(jié)點(diǎn)的位置信息選舉網(wǎng)絡(luò)簇頭節(jié)點(diǎn)并構(gòu)建簇,再由已經(jīng)完成的分簇網(wǎng)絡(luò)建立BP神經(jīng)網(wǎng)絡(luò)模型,最后根據(jù)網(wǎng)絡(luò)的總傳輸跳數(shù)動(dòng)態(tài)調(diào)整網(wǎng)絡(luò)的分簇?cái)?shù)量,至網(wǎng)絡(luò)達(dá)到最優(yōu)簇頭數(shù)目的穩(wěn)態(tài)。本方法能適用于不同規(guī)模大小的網(wǎng)絡(luò),以最優(yōu)分簇?cái)?shù)量收集網(wǎng)絡(luò)數(shù)據(jù),具有減少網(wǎng)絡(luò)能耗、延長網(wǎng)絡(luò)生命周期、降低網(wǎng)絡(luò)延時(shí)等優(yōu)點(diǎn)。為了減少分簇式傳感器網(wǎng)絡(luò)中的數(shù)據(jù)傳輸量并均衡網(wǎng)絡(luò)負(fù)載,提出了一種采用混合壓縮感知進(jìn)行數(shù)據(jù)收集的方法。首先選定各臨時(shí)簇中離簇質(zhì)心最近的一些節(jié)點(diǎn)為候選簇頭節(jié)點(diǎn),然后基于已確定的簇頭節(jié)點(diǎn)到未確定的候選簇頭節(jié)點(diǎn)的距離依次確定簇頭,其次各普通節(jié)點(diǎn)選擇加入距離自己最近的簇,最后貪婪構(gòu)建一棵以sink節(jié)點(diǎn)為根節(jié)點(diǎn)并連接所有簇頭節(jié)點(diǎn)的數(shù)據(jù)傳輸樹,對數(shù)據(jù)傳輸量高于門限值的節(jié)點(diǎn)使用CS壓縮數(shù)據(jù)傳輸。仿真結(jié)果表明當(dāng)壓縮比率為10時(shí),數(shù)據(jù)傳輸量比Clustering without CS和SPT without CS分別減少了75%和65%,比SPT with Hybrid CS和Clustering with Hybrid CS分別減少了35%和20%;節(jié)點(diǎn)數(shù)據(jù)傳輸量標(biāo)準(zhǔn)差比Clustering without CS和SPT without CS分別減少了62%和81%,比SPT with Hybrid CS和Clustering with Hybrid CS分別減少了41%和19%。
[Abstract]:Nowadays, wireless sensing technology has been integrated into our life, study, work and entertainment. In wireless perceptual data collection networks, low-latency data transmission has always been a hot research direction. Node data acquisition and compression, efficient routing strategy, data processing, including the analysis of the low power consumption of the network. In this paper, we analyze the traditional clustering perceptual network data collection method and the clustering perceptual network data collection method based on hybrid compression perception. By analyzing the existing clustering perceptual network data collection algorithms, it is noted that the data transmission delay and throughput in the network are the key indicators to evaluate the algorithm. Therefore, this paper proposes a method of data collection based on Markov model to select mobile sink paths. Firstly, the monitored wireless sensor network is divided into regions, then the node position is predicted by Markov model, and then the network area is given priority according to the expected value of the number of nodes predicted, and then the data fusion of each region is carried out. Finally, the optimal trajectory of moving sink nodes is obtained. The method can be applied to networks of different scales, and has the advantages of saving network energy, prolonging network lifetime, reducing overall network delay and preventing data overflow. Secondly, a clustering sensor network data collection method based on BP neural network is proposed. Firstly, the monitored wireless sensor network is initialized to find the geographical center of the network according to the GPS information of the node, and then the cluster head node is selected according to the location information of the node and the cluster is constructed. Then the BP neural network model is established from the completed clustering network. Finally, according to the total transmission hops of the network, the clustering number of the network is dynamically adjusted to the steady state of the optimal cluster head number of the network. The method can be applied to networks of different size and size, collect network data with the optimal number of clusters, and has the advantages of reducing network energy consumption, prolonging network life cycle and reducing network delay. In order to reduce the amount of data transmission in cluster sensor networks and to balance the network load, a method of data collection using hybrid compression sensing is proposed. First, some nodes in each temporary cluster are selected as candidate cluster heads, and then the cluster heads are determined based on the distance from the determined cluster heads to the undetermined candidate cluster heads. Secondly, each common node chooses to join the nearest cluster. Finally, a data transmission tree with sink node as the root node and connecting all cluster head nodes is constructed, and the data transmission is compressed by CS for the node whose data transmission amount is higher than the threshold. The simulation results show that when the compression ratio is 10:00, The volume of data transmission is 75% and 65% less than Clustering without CS and SPT without CS, 35% and 20% less than SPT with Hybrid CS and Clustering with Hybrid CS, respectively. The standard deviation of node data transmission is 62% and 81% less than Clustering without CS and SPT without CS, respectively, compared with SPT with Hybrid CS. And Clustering with Hybrid CS decreased by 41% and 19%, respectively.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9;TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 劉亞;劉功亮;康文靜;;壓縮感知和LEACH結(jié)合的水下傳感器網(wǎng)絡(luò)信息采集方案[J];傳感技術(shù)學(xué)報(bào);2013年03期

2 焦李成;楊淑媛;劉芳;侯彪;;壓縮感知回顧與展望[J];電子學(xué)報(bào);2011年07期

3 戴瓊海;付長軍;季向陽;;壓縮感知研究[J];計(jì)算機(jī)學(xué)報(bào);2011年03期

4 夏娜;徐順安;蔣建國;;WSNs中節(jié)點(diǎn)能耗分析與測試[J];計(jì)算機(jī)研究與發(fā)展;2010年S1期

，

本文編號：2222987