本文選題：無(wú)線傳感器網(wǎng)絡(luò) + 三維節(jié)點(diǎn)定位　； 參考：《廈門大學(xué)》2014年碩士論文

【摘要】：無(wú)線傳感器網(wǎng)絡(luò)是一個(gè)多學(xué)科交叉的、新興、前沿的熱點(diǎn)研究領(lǐng)域,它將會(huì)對(duì)人類的生產(chǎn)和生活產(chǎn)生深遠(yuǎn)的影響。與普通通信網(wǎng)絡(luò)相比,無(wú)線傳感器網(wǎng)絡(luò)在軍事、環(huán)境、醫(yī)療、家庭和工業(yè)等領(lǐng)域的應(yīng)用十分廣闊。而節(jié)點(diǎn)定位技術(shù)作為無(wú)線傳感器網(wǎng)絡(luò)的關(guān)鍵支撐技術(shù)之一,是必須要解決的難題。 本文主要探討了無(wú)線傳感器網(wǎng)絡(luò)基于測(cè)距技術(shù)的三維節(jié)點(diǎn)定位優(yōu)化方法。由于定位問(wèn)題在本質(zhì)上是一個(gè)優(yōu)化問(wèn)題,所以引入智能算法——模擬退火算法和遺傳算法進(jìn)行優(yōu)化,并通過(guò)仿真實(shí)驗(yàn)將它們與經(jīng)典的極大似然估計(jì)算法進(jìn)行比較和分析。 將定位精度和計(jì)算時(shí)間作為算法優(yōu)良的評(píng)價(jià)標(biāo)準(zhǔn),分別比較三種算法在不同測(cè)距誤差、不同信標(biāo)節(jié)點(diǎn)密度、及不同節(jié)點(diǎn)數(shù)目條件下對(duì)應(yīng)的平均定位誤差和計(jì)算時(shí)間。仿真實(shí)驗(yàn)表明,在不同測(cè)距誤差下,兩種智能算法的定位精度都比ML算法的高,且測(cè)距誤差越大智能算法的優(yōu)勢(shì)越明顯；在不同信標(biāo)節(jié)點(diǎn)密度下,兩種智能算法的定位精度也都比ML算法的高,而信標(biāo)節(jié)點(diǎn)密度對(duì)它們影響卻不大,因而采用這兩種算法只需在網(wǎng)絡(luò)中部署少量的信標(biāo)節(jié)點(diǎn)就可以獲得較高的定位精度,從而降低了成本；三種算法都表現(xiàn)出了較好的自適應(yīng)性；GA-L算法的定位精度最高,但計(jì)算時(shí)間最長(zhǎng),而SA-L算法既能獲得較好的定位精度,也不需花太多的計(jì)算時(shí)間。 總之,在無(wú)線傳感器網(wǎng)絡(luò)三維節(jié)點(diǎn)定位中,應(yīng)用智能算法可以有效避免過(guò)大的定位誤差,獲得更高的定位精度和更好定位性能,其中GA-L算法的定位精度最高,而在需要同時(shí)考慮算法定位精度和計(jì)算時(shí)間的情況下,SA-L算法更合適。
[Abstract]:Wireless sensor network (WSN) is a multi-disciplinary, emerging, cutting-edge research field, which will have a profound impact on human production and life. Compared with ordinary communication networks, wireless sensor networks are widely used in military, environmental, medical, home and industrial fields. As one of the key supporting technologies in wireless sensor networks, node location is a difficult problem to be solved. This paper mainly discusses the three-dimensional node location optimization method based on ranging technology in wireless sensor networks. Because the localization problem is essentially an optimization problem, an intelligent algorithm, simulated annealing algorithm and genetic algorithm, is introduced to optimize it, and the simulation experiments are carried out to compare them with the classical maximum likelihood estimation algorithm. The positioning accuracy and computing time are taken as the evaluation criteria of the algorithm, and the mean location error and calculation time of the three algorithms under different ranging errors, different beacon node densities, and different number of nodes are compared respectively. The simulation results show that the location accuracy of the two intelligent algorithms is higher than that of ML algorithm under different ranging errors, and the larger the ranging error is, the more obvious the advantages of the intelligent algorithm are, and under the different beacon node densities, the location accuracy of the two intelligent algorithms is higher than that of the ML algorithm. The localization accuracy of the two intelligent algorithms is also higher than that of ML algorithm, but the density of beacon nodes has little effect on them. Therefore, using these two algorithms, only a small number of beacon nodes can be deployed in the network to obtain higher localization accuracy. All of the three algorithms have the highest localization accuracy but the longest computation time, while the SA-L algorithm can not only obtain better positioning accuracy, but also need not spend too much time. In short, in the wireless sensor network 3D node location, the application of intelligent algorithm can effectively avoid excessive positioning errors, obtain higher positioning accuracy and better positioning performance, among which GA-L algorithm has the highest positioning accuracy. The SA-L algorithm is more suitable when the accuracy and computing time of the algorithm are taken into account at the same time.
【學(xué)位授予單位】：廈門大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN929.5;TP212.9

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