【摘要】：無線傳感器網(wǎng)絡(luò)(WSN)是目前在國內(nèi)外備受關(guān)注,涉及多學(xué)科且知識(shí)高度交叉、集成的前沿?zé)狳c(diǎn)研究領(lǐng)域。它實(shí)現(xiàn)了將傳輸網(wǎng)絡(luò)與客觀世界的信息有效地連接在一起,為下一代網(wǎng)絡(luò)提供最直接、最有效、最真實(shí)的信息。在WSN的研究中,覆蓋問題是大量傳感器節(jié)點(diǎn)部署在監(jiān)控區(qū)域內(nèi)首先遇到的問題,而如何選取高效的覆蓋算法及控制策略對(duì)WSN中節(jié)點(diǎn)進(jìn)行優(yōu)化部署,在很大程度上影響了節(jié)點(diǎn)能量的有效利用,網(wǎng)絡(luò)的感知能力、服務(wù)質(zhì)量以及被傳感器節(jié)點(diǎn)所覆蓋的區(qū)域范圍也可以得到大幅度地提升,從而延長了網(wǎng)絡(luò)的生存時(shí)間。本論文主要針對(duì)WSN部署中節(jié)點(diǎn)的覆蓋問題展開研究,以粒子群和細(xì)菌覓食算法作為理論研究基礎(chǔ),建立了相關(guān)的WSN覆蓋優(yōu)化模型,提出了改進(jìn)的細(xì)菌覓食算法與粒子群算法相結(jié)合的覆蓋優(yōu)化算法。論文主要內(nèi)容和研究成果如下:(1)首先對(duì)WSN覆蓋算法的收斂性能展開了研究。由于標(biāo)準(zhǔn)粒子群(PSO)算法在WSN覆蓋部署中存在粒子收斂速度慢、粒子易陷入局部最優(yōu)值而使算法出現(xiàn)“早熟”現(xiàn)象以及粒子種類單一等缺陷。針對(duì)上述問題,論文對(duì)標(biāo)準(zhǔn)的細(xì)菌覓食(BFO)算法進(jìn)行改進(jìn):在趨向操作步驟中,通過自適應(yīng)的改進(jìn)細(xì)菌的游動(dòng)步長,使細(xì)菌更加準(zhǔn)確快速的搜索到目標(biāo);在復(fù)制操作中,加入分布估計(jì)算法,以增加細(xì)菌種類的多樣性。進(jìn)一步地,在改進(jìn)的BFO算法基礎(chǔ)上,提出了粒子群-細(xì)菌覓食(PSO-BFO)優(yōu)化算法,即PSO算法完成對(duì)區(qū)域的全局搜索、記憶個(gè)體和群體的信息,而區(qū)域的局部搜索則由改進(jìn)的BFO算法的趨向和聚集操作完成。仿真結(jié)果表明,所提優(yōu)化算法在粒子的收斂速度以及尋優(yōu)效率方面,明顯優(yōu)于標(biāo)準(zhǔn)粒子群和細(xì)菌覓食算法,從而保證了算法在WSN部署中的有效性。(2)針對(duì)隨機(jī)高密度傳感器節(jié)點(diǎn)的網(wǎng)絡(luò)環(huán)境所導(dǎo)致的節(jié)點(diǎn)監(jiān)測(cè)區(qū)域易出現(xiàn)覆蓋重疊區(qū)及覆蓋盲區(qū)等問題,論文以最大化WSN網(wǎng)絡(luò)覆蓋率和最少的節(jié)點(diǎn)部署個(gè)數(shù)為覆蓋優(yōu)化目標(biāo),對(duì)提出的粒子群-細(xì)菌覓食(PSO-BFO)覆蓋優(yōu)化算法與單一的PSO和BFO算法進(jìn)行了覆蓋率性能的比較。仿真結(jié)果顯示,提出的覆蓋優(yōu)化算法能夠以較少的部署節(jié)點(diǎn)獲得較高的WSN網(wǎng)絡(luò)覆蓋率,較好地維持了網(wǎng)絡(luò)的穩(wěn)定性、有效延長了WSN網(wǎng)絡(luò)生存時(shí)間。最后,對(duì)論文所做工作進(jìn)行總結(jié),提出研究發(fā)展方向。
[Abstract]:Wireless sensor network (WSN) is a hot research field in the field of multi-disciplinary, highly cross-knowledge and integration, which has attracted much attention at home and abroad. It can effectively connect the transmission network with the information of the objective world, and provide the most direct, effective and truest information for the next generation network. In the research of WSN, the coverage problem is the first problem that a large number of sensor nodes are deployed in the monitoring area, and how to select an efficient coverage algorithm and control strategy to optimize the deployment of WSN nodes. To a large extent, the effective utilization of node energy is affected, and the perception ability, quality of service and the area covered by sensor nodes can be greatly improved, thus prolonging the lifetime of the network. In this paper, the coverage of nodes in WSN deployment is studied. Based on particle swarm optimization and bacterial foraging algorithm, the related WSN coverage optimization model is established. An improved coverage optimization algorithm combining bacterial foraging algorithm and particle swarm optimization algorithm is proposed. The main contents and research results are as follows: (1) the convergence performance of WSN coverage algorithm is studied firstly. Due to the slow convergence rate of standard particle swarm optimization (PSO) algorithm in WSN coverage deployment, the particle is prone to fall into local optimal value, which makes the algorithm appear premature phenomenon and single particle category. In order to solve the above problems, the paper improves the standard bacterial foraging (BFO) algorithm: in the trend operation step, the bacteria can find the target more accurately and quickly through the adaptive improvement of the bacterial walk size; in the reproduction operation, the bacteria can find the target more accurately and quickly. Distribution estimation algorithm is added to increase the diversity of bacteria species. Furthermore, based on the improved BFO algorithm, a particle swarm optimization algorithm (PSO-BFO) is proposed, that is, the PSO algorithm completes the global search of the region and memorizes the information of individuals and populations. The local search of the region is accomplished by the orientation and aggregation operation of the improved BFO algorithm. Simulation results show that the proposed optimization algorithm is superior to standard particle swarm optimization and bacterial foraging algorithm in particle convergence speed and optimization efficiency. This ensures the effectiveness of the algorithm in WSN deployment. (2) because of the network environment of random high-density sensor nodes, the node monitoring area is prone to cover overlapped areas and blind areas, etc. The coverage performance of the proposed PSO-BFO coverage optimization algorithm is compared with that of a single PSO and BFO algorithm with the goal of maximizing the WSN coverage and the minimum number of nodes deployed. Simulation results show that the proposed coverage optimization algorithm can obtain higher WSN coverage with fewer deployment nodes, maintain the stability of the network, and effectively prolong the lifetime of WSN networks. Finally, the paper summarizes the work done, and puts forward the direction of research and development.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN929.5;TP212.9

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本文編號(hào)：2130518