本文選題：無線傳感器網(wǎng)絡(luò) + 分簇路由��； 參考：《山東師范大學(xué)》2014年碩士論文

【摘要】：無線傳感器網(wǎng)絡(luò)是由大量具有特定功能的傳感器節(jié)點(diǎn)通過自組織的無線通信方式，相互傳遞信息，協(xié)同完成特定功能的智能專用網(wǎng)絡(luò)。由于其自身特點(diǎn)，無線傳感器網(wǎng)絡(luò)已在眾多的領(lǐng)域中得到應(yīng)用，具有巨大的應(yīng)用價(jià)值。而正是由于其廣闊的應(yīng)用前景和巨大的應(yīng)用價(jià)值，近年來在國際上引起了廣泛的關(guān)注，并且越來越多的人投身于無線傳感器網(wǎng)絡(luò)技術(shù)的研究。但是，由于無線傳感器網(wǎng)絡(luò)中節(jié)點(diǎn)間傳輸、處理數(shù)據(jù)等均需要依靠電池供電，而無線傳感器網(wǎng)絡(luò)特殊的工作環(huán)境又致使更換電池很困難甚至是不可能的。因此，設(shè)計(jì)高效能路由算法以盡可能降低平均能耗，延長網(wǎng)絡(luò)生命周期是無線傳感器網(wǎng)絡(luò)中的關(guān)鍵問題。 針對無線傳感器網(wǎng)絡(luò)能量有限且負(fù)載不均衡問題，為提高能量利用率進(jìn)而延長網(wǎng)絡(luò)生命周期，提出一種基于分簇的節(jié)能路由策略。首先，通過經(jīng)線和緯線對網(wǎng)絡(luò)進(jìn)行動(dòng)態(tài)分區(qū)。利用非均勻分簇的思想將網(wǎng)絡(luò)劃分為大小不等的區(qū)域，離匯聚節(jié)點(diǎn)較近的區(qū)域形成的簇規(guī)模較小，數(shù)量較多，這樣簇首消耗在簇內(nèi)通信和數(shù)據(jù)聚合上的能量就少，從而能夠補(bǔ)償由于轉(zhuǎn)發(fā)外層區(qū)域簇首數(shù)據(jù)包而所需的能量，進(jìn)而實(shí)現(xiàn)能耗均衡。分區(qū)完成后再根據(jù)節(jié)點(diǎn)的地理位置和剩余能量為每個(gè)分區(qū)選出最優(yōu)簇首。然后，，非簇首節(jié)點(diǎn)選擇與自身距離較小且剩余能量較大的簇首加入，從而構(gòu)建成簇。最后，完成數(shù)據(jù)的傳輸過程。由于在組織成簇和選擇中繼轉(zhuǎn)發(fā)節(jié)點(diǎn)時(shí)均考慮距離和剩余能量的因素，從而在一定程度上進(jìn)一步均衡了網(wǎng)絡(luò)中的能耗。另外，又對數(shù)據(jù)融合算法進(jìn)行了研究。在利用LEACH算法劃分網(wǎng)絡(luò)的基礎(chǔ)上，首先通過閾值的設(shè)置來減少非簇首節(jié)點(diǎn)的數(shù)據(jù)發(fā)送量，降低非簇首節(jié)點(diǎn)因發(fā)送相似數(shù)據(jù)而消耗的能量。然后通過延遲時(shí)間的設(shè)置讓簇首能夠在接收到足夠多的數(shù)據(jù)后再進(jìn)行數(shù)據(jù)融合，以此來保證數(shù)據(jù)的準(zhǔn)確性。最后對簇首收集的數(shù)據(jù)取平均值，大大減少了簇首與基站間的通信量，降低了簇首節(jié)點(diǎn)的能耗。仿真結(jié)果表明:兩種算法均能優(yōu)化網(wǎng)絡(luò)能耗，延長網(wǎng)絡(luò)生命周期。
[Abstract]:Wireless sensor network (WSN) is a special intelligent network which is composed of a large number of sensor nodes with specific functions. Because of its own characteristics, wireless sensor networks have been applied in many fields and have great application value. Because of its wide application prospect and great application value, it has attracted wide attention in the world in recent years, and more people devote themselves to the research of wireless sensor network technology. However, due to the transmission between nodes in wireless sensor networks, data processing needs to rely on battery power supply, and the special working environment of wireless sensor networks makes it difficult or even impossible to replace batteries. Therefore, designing a high performance routing algorithm to minimize the average energy consumption and prolong the network life cycle is a key problem in wireless sensor networks. Aiming at the problem of limited energy and unbalanced load in wireless sensor networks, an energy saving routing strategy based on clustering is proposed to improve energy utilization and prolong network life cycle. First, the network is dynamically partitioned by longitude and weft. Using the idea of non-uniform clustering, the network is divided into regions of different sizes. The cluster size is smaller and the number of clusters is larger in the region near the convergence node, so the cluster head consumes less energy in the communication and data aggregation within the cluster. It can compensate for the energy needed by forwarding the cluster head packets in the outer region, and then realize the energy balance. After the partition is completed, the optimal cluster head is selected for each partition according to the location of the node and the residual energy. Then, the non-cluster head nodes are selected to join the cluster heads with small distance and large residual energy, so as to form clusters. Finally, the data transmission process is completed. Because the factors of distance and residual energy are taken into account in organizing clusters and selecting relay forwarding nodes, the energy consumption in the network is further balanced to a certain extent. In addition, the data fusion algorithm is studied. On the basis of the Leach algorithm to divide the network, the threshold is set to reduce the amount of data transmission and the energy consumption of the non-cluster head node due to the transmission of similar data. Then the delay time is set so that the cluster head can fuse the data after receiving enough data to ensure the accuracy of the data. Finally, the data collected by cluster head are averaged, which greatly reduces the communication between cluster head and base station, and reduces the energy consumption of cluster head node. The simulation results show that both algorithms can optimize the network energy consumption and prolong the network life cycle.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212.9;TN929.5

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