本文選題：無線傳感器網(wǎng)絡(luò) + 壓縮感知　； 參考：《湘潭大學(xué)》2017年碩士論文

【摘要】：隨著無線傳感器網(wǎng)絡(luò)(Wireless Sensor Network,WSN)在軍事防御、環(huán)境監(jiān)測、醫(yī)療診斷、智能交通等領(lǐng)域的越來越廣泛使用,利用WSN實(shí)現(xiàn)目標(biāo)對象實(shí)時(shí)監(jiān)測采集信號的研究也備受關(guān)注。WSN通常由大量體積小、成本低、不受環(huán)境限制的傳感器節(jié)點(diǎn)密集的布置在感知區(qū)域。但是由于單個(gè)節(jié)點(diǎn)的資源有限,而網(wǎng)絡(luò)中的通信量巨大,導(dǎo)致節(jié)點(diǎn)能源耗費(fèi)過快,網(wǎng)絡(luò)壽命不長。而節(jié)點(diǎn)間數(shù)據(jù)通信所消耗的能量占節(jié)點(diǎn)總能源耗費(fèi)的比重最大,因此減少網(wǎng)絡(luò)通信量可實(shí)現(xiàn)能量有效的數(shù)據(jù)收集。壓縮感知(Compressive Sensing,CS)技術(shù)利用相鄰節(jié)點(diǎn)在一段時(shí)間內(nèi)采集的數(shù)據(jù)存在時(shí)空相關(guān)性的特點(diǎn),減少網(wǎng)絡(luò)冗余數(shù)據(jù)的采集和收發(fā),不僅能夠減少網(wǎng)絡(luò)通信量還能平衡通信負(fù)載。但是,普通壓縮感知技術(shù)造成了節(jié)點(diǎn)早期的通信負(fù)載過高,據(jù)此,混合壓縮感知提出僅在通信量高于壓縮感知瓶頸的節(jié)點(diǎn)中使用CS技術(shù)處理數(shù)據(jù),否則傳輸原始數(shù)據(jù)。因此,針對如何減少WSN數(shù)據(jù)通信量、提升WSN壽命期限,本文提出一種結(jié)合混合CS技術(shù)的分簇式WSN數(shù)據(jù)收集方法。首先,按地理位置劃分感知區(qū)域成若干簇,并假設(shè)各簇區(qū)域中心存在一個(gè)虛擬簇頭節(jié)點(diǎn),且選取虛擬簇頭節(jié)點(diǎn)一跳通信范圍內(nèi)的節(jié)點(diǎn)為候選簇頭節(jié)點(diǎn);其次,使用Prim算法以sink為根節(jié)點(diǎn)連接各虛擬簇頭節(jié)點(diǎn)生成一棵最小生成樹;然后,從sink節(jié)點(diǎn)開始,為最小生成樹各分支中的簇從候選簇頭節(jié)點(diǎn)中動(dòng)態(tài)規(guī)劃選出簇頭節(jié)點(diǎn);最后,構(gòu)造以sink節(jié)點(diǎn)為根節(jié)點(diǎn)且按最小生成樹順序連接各簇頭節(jié)點(diǎn)的數(shù)據(jù)傳輸骨干樹。仿真結(jié)果證明,當(dāng)壓縮率為10時(shí),本文算法通信量比clustering without CS、SPT without CS、SPT with hybrid CS、以及clustering with hybrid CS,分別減少了65%、55%、40%和10%。
[Abstract]:With the increasing use of Wireless Sensor Network (WSN) in the fields of military defense, environmental monitoring, medical diagnosis, intelligent transportation, etc. The research of using WSN to realize real-time monitoring and collecting signal of target object is also concerned. WSN is usually arranged in the sensing area by a large number of sensor nodes which are small in volume, low in cost and not restricted by environment. However, due to the limited resources of a single node, and the huge amount of communication in the network, the node energy consumption is too fast, the network life is not long. The energy consumed by the data communication between nodes accounts for the largest proportion of the total energy consumption, so the energy efficient data collection can be realized by reducing the network traffic. Compressed-sensing (CS) technology can reduce the collection and transmission of redundant data in the network by utilizing the spatio-temporal correlation of the data collected by adjacent nodes over a period of time. It can not only reduce the network traffic but also balance the communication load. However, the common compression sensing technology causes the high communication load in the early stage of the nodes. Therefore, the hybrid compression sensing technology only uses CS technology to process the data in the nodes where the traffic is higher than the bottleneck of compression perception, otherwise, the original data is transmitted. Therefore, in order to reduce the data traffic and increase the lifetime of WSN, a clustering data collection method based on hybrid CS technology is proposed in this paper. Firstly, the perceptual region is divided into several clusters according to geographical location, and a virtual cluster head node is assumed to exist in the center of each cluster region, and the node in the one-hop communication range of the virtual cluster head node is selected as the candidate cluster head node. Using Prim algorithm to connect each virtual cluster head node with sink as the root node to generate a minimum spanning tree; then, starting from the sink node, the cluster heads in each branch of the minimum spanning tree are dynamically programmed from the candidate cluster header node. Finally, the cluster head node is selected from the candidate cluster head node. The data transmission backbone tree with sink node as the root node and connected each cluster head node in the order of minimum spanning tree is constructed. The simulation results show that when the compression ratio is 10:00, the traffic of our algorithm is 65% and 10% less than that of clustering without with hybrid CSand clustering with hybrid CSS, 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

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本文編號：2098251