發(fā)布時(shí)間：2018-06-29 19:33

  本文選題：無線傳感器網(wǎng)絡(luò) + 數(shù)據(jù)收集�。� 參考：《西北大學(xué)》2016年博士論文

【摘要】：數(shù)據(jù)收集是無線傳感器網(wǎng)絡(luò)技術(shù)中的重要內(nèi)容,而移動(dòng)式的數(shù)據(jù)收集有其獨(dú)特的優(yōu)勢,可以增大網(wǎng)絡(luò)生存時(shí)間和減少節(jié)點(diǎn)的能量消耗等。本文在以往研究的基礎(chǔ)上,通過分析相關(guān)問題以及結(jié)合新的技術(shù),研究了基于移動(dòng)Sink的無線傳感器網(wǎng)絡(luò)數(shù)據(jù)收集。針對移動(dòng)Sink無線傳感器網(wǎng)絡(luò)的數(shù)據(jù)收集中的數(shù)個(gè)重要問題,結(jié)合無線信號的傳輸、節(jié)點(diǎn)部署、隨機(jī)理論、排隊(duì)論和優(yōu)化理論等進(jìn)行了研究。本文的目標(biāo)是實(shí)現(xiàn)良好的移動(dòng)Sink無線傳感器網(wǎng)絡(luò)數(shù)據(jù)收集,達(dá)到增大網(wǎng)絡(luò)生存時(shí)間、減少網(wǎng)絡(luò)能耗以及縮短時(shí)延等目的。本文的主要工作和創(chuàng)新點(diǎn)如下：1.提出了基于數(shù)據(jù)包收包評價(jià)的最少節(jié)點(diǎn)隨機(jī)部署的方法。對一些精度要求不高的大規(guī)模無線傳感器網(wǎng)絡(luò)實(shí)際應(yīng)用,為降低部署成本,本文研究允許一定丟包率的節(jié)點(diǎn)隨機(jī)部署,旨在減少網(wǎng)絡(luò)的節(jié)點(diǎn)數(shù)量。通過測量不同環(huán)境、不同距離的數(shù)據(jù)包接收情況,依據(jù)無線信號接收情況和距離的概率關(guān)系,建立數(shù)據(jù)包收包評價(jià)模型,提出了基于數(shù)據(jù)包收包評價(jià)的最少節(jié)點(diǎn)隨機(jī)部署的方法,減少了隨機(jī)部署所需的節(jié)點(diǎn)。2.提出了基于排隊(duì)論和中心化方法的單天線移動(dòng)Sink的控制方法。移動(dòng)Sink可以實(shí)現(xiàn)部署的低成本和解決節(jié)點(diǎn)的負(fù)載均衡問題。本文基于排隊(duì)論對移動(dòng)Sink的數(shù)據(jù)收集過程進(jìn)行控制,根據(jù)數(shù)據(jù)的重要程度進(jìn)行排隊(duì)、允許過程中斷以及根據(jù)節(jié)點(diǎn)剩余能量建立損失制的排隊(duì)規(guī)則。這一排隊(duì)規(guī)則適合移動(dòng)Sink數(shù)據(jù)收集的特點(diǎn),增大了網(wǎng)絡(luò)的生存時(shí)間�；�于中心化方法對移動(dòng)Sink數(shù)據(jù)收集的軌跡進(jìn)行控制,加入若干虛擬點(diǎn)形成一個(gè)連通圖,根據(jù)連通圖的特點(diǎn),結(jié)合中心化方法對移動(dòng)Sink數(shù)據(jù)收集的軌跡進(jìn)行優(yōu)化,縮短了移動(dòng)Sink數(shù)據(jù)收集的軌跡,同時(shí)減少了網(wǎng)絡(luò)能耗。3.提出了基于速度控制的多天線移動(dòng)Sink數(shù)據(jù)收集方法�；�于速度控制的多天線移動(dòng)Sink數(shù)據(jù)收集方法將整個(gè)數(shù)據(jù)收集過程分為多個(gè)階段,通過對Sink移動(dòng)速度的調(diào)節(jié)和控制,使在各個(gè)階段多天線移動(dòng)Sink能夠完成數(shù)據(jù)收集,同時(shí)各階段之間的Sink移動(dòng)速度能夠銜接。將區(qū)域分為四類,優(yōu)先選擇匯聚區(qū)域以發(fā)揮多天線數(shù)據(jù)收集的優(yōu)勢,同時(shí)盡可能削減階段并最大化Sink移動(dòng)速度,達(dá)到了降低時(shí)延的效果。4.提出了基于馬爾科夫流量預(yù)測的節(jié)點(diǎn)能量和負(fù)載相適應(yīng)的方法。節(jié)點(diǎn)的能量和負(fù)載需要有一定的適應(yīng)機(jī)制。本文提出了基于馬爾科夫流量預(yù)測的能量和負(fù)載的適應(yīng)方法,運(yùn)用馬爾科夫流量預(yù)測解決簇頭更換這一隨機(jī)動(dòng)態(tài)決策問題,優(yōu)化簇頭的更換。分析了多跳轉(zhuǎn)發(fā)機(jī)制的多階馬爾科夫,選取三階馬爾科夫進(jìn)行流量預(yù)測�；�于馬爾科夫流量預(yù)測的能量和負(fù)載的適應(yīng)方法延長了網(wǎng)絡(luò)生存時(shí)間。5.提出了基于改進(jìn)的的模擬退火算法的節(jié)點(diǎn)可無線充電的數(shù)據(jù)收集方法。將數(shù)據(jù)收集裝置和無線充電裝置集于一體放置于可移動(dòng)裝置上,采用無線充電的方式對節(jié)點(diǎn)進(jìn)行能量補(bǔ)充,同時(shí)進(jìn)行數(shù)據(jù)收集。本文分析了數(shù)據(jù)收集和無線充電問題,將其歸結(jié)為組合優(yōu)化問題,提出了改進(jìn)的模擬退火算法,優(yōu)化移動(dòng)裝置的路徑以及速度,最大化無線充電和數(shù)據(jù)收集同時(shí)進(jìn)行的概率。該方法縮短了移動(dòng)裝置的移動(dòng)距離,同時(shí)減少了數(shù)據(jù)收集和無線充電所需時(shí)間。
[Abstract]:Data collection is an important part of wireless sensor network technology, and mobile data collection has its unique advantages, which can increase network lifetime and reduce the energy consumption of nodes. On the basis of previous research, the wireless sensor based on mobile Sink is studied by analyzing related problems and combining new technologies. Network data collection. Aiming at several important problems in the data collection of mobile Sink wireless sensor networks, this paper studies the transmission of wireless signals, node deployment, random theory, queuing theory and optimization theory. The aim of this paper is to achieve a good data collection of mobile Sink wireless sensor networks to increase network survival. Time, reducing network energy consumption and shortening delay. The main work and innovation of this paper are as follows: 1. a method of least node random deployment based on packet collection evaluation is proposed. The practical application of some large-scale wireless sensor networks with low precision is applied to reduce the cost of deployment, and the study allows a certain packet loss rate. A random deployment of nodes is designed to reduce the number of nodes in the network. By measuring the receiving situation of different environments and different distances, according to the probability relationship between the reception and distance of the wireless signal, a packet collection evaluation model is set up, and a method of least node random deployment based on the packet collection evaluation is proposed, which reduces the random part. The required node.2. proposed a single antenna mobile Sink control method based on queuing theory and centralization. Mobile Sink can achieve low cost of deployment and solve node load balancing problem. This paper is based on queuing theory to control the data collection process of mobile Sink, queuing according to the importance of data and allowing process. This queuing rule is suitable for the characteristics of the mobile Sink data collection and increases the survival time of the network. Based on the centralization method, the trajectory of the mobile Sink data collection is controlled, and a number of virtual points are added to form a connected graph, according to the characteristics of the connected graph, the combination of the network is combined with the characteristics of the connected graph. The localization method optimizes the trajectories of the mobile Sink data collection, shortens the trajectory of the mobile Sink data collection, and reduces the network energy consumption.3., and proposes a multi antenna mobile Sink data collection method based on speed control. The multi antenna mobile Sink data collection method based on speed control divides the whole data collection process into multiple orders. Segment, by adjusting and controlling the moving speed of Sink, the multi antenna mobile Sink can complete data collection at all stages, and the Sink movement speed between each stage can be connected. The region is divided into four categories, and the preferential selection of the converging area is given to play the advantage of the multi antenna data collection, while the phase and the maximum Sink movement are reduced as much as possible. Speed, the effect of reducing time delay is achieved..4. puts forward the method of adapting the energy and load of nodes based on Markoff flow prediction. The energy and load of nodes need a certain adaptation mechanism. This paper proposes the adaptive formula of energy and load based on Markoff traffic prediction, and uses Markoff traffic prediction to solve cluster heads. Replacing this stochastic dynamic decision problem and optimizing the replacement of cluster heads, the multihop Markoff of multi hop forwarding mechanism is analyzed, and three order Markoff is selected for traffic prediction. Based on the adaptive method of energy and load of Markoff traffic prediction, the network survival time.5. is extended to the node of improved simulated annealing algorithm. A data collection method for wireless charging. A data collection device and a wireless charging device are placed on a mobile device. A wireless charging method is used to supplement the energy and collect data. This paper analyzes data collection and wireless charging problems, and puts it down as a combinatorial optimization problem, and proposes an improvement. The simulated annealing algorithm optimizes the path and speed of the mobile device to maximize the simultaneous probability of wireless charging and data collection. This method shortens the mobile distance of the mobile device and reduces the time required for data collection and wireless charging.
【學(xué)位授予單位】：西北大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP212.9;TN929.5

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