本文選題：環(huán)境監(jiān)測 + 無線傳感器網(wǎng)絡(luò)�。� 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：隨著信息技術(shù)的發(fā)展和進(jìn)步,物聯(lián)網(wǎng)(Internet of Things,IoT)逐步融入社會(huì)生活的各個(gè)方面,在工業(yè)生產(chǎn)、環(huán)境監(jiān)測、醫(yī)療健康等眾多領(lǐng)域都得到了廣泛的應(yīng)用。無線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,WSNs)是物聯(lián)網(wǎng)的基礎(chǔ)設(shè)施與關(guān)鍵技術(shù)之一,具有自組織、規(guī)模龐大、分布密集等特征。受節(jié)點(diǎn)成本的限制,網(wǎng)絡(luò)的能量資源、計(jì)算資源、存儲(chǔ)資源等均很有限。在這種情況下,如何提高網(wǎng)絡(luò)性能是WSNs應(yīng)用研究的重點(diǎn)。本文主要對基于WSNs的環(huán)境監(jiān)測方法進(jìn)行了研究。節(jié)點(diǎn)調(diào)度算法是一種能有效延長WSNs生命周期的方法,但是節(jié)點(diǎn)的休眠會(huì)引起監(jiān)測數(shù)據(jù)的缺失,從而導(dǎo)致監(jiān)測精度的降低。因此本文利用克里金算法來估計(jì)缺失的數(shù)據(jù)。本文首先針對普通克里金算法變異函數(shù)模型擬合精度不高的問題,引入基于高斯變異的NM單純形法,提出了一種改進(jìn)克里金算法(Improved Kriging,IK)。仿真結(jié)果表明,改進(jìn)算法的估計(jì)精度要優(yōu)于普通克里金算法。然后,將改進(jìn)的克里金算法應(yīng)用到WSNs中,并將其與能量均衡休眠調(diào)度算法相結(jié)合,提出了一種基于改進(jìn)克里金的WSNs插值估計(jì)算法。仿真實(shí)驗(yàn)結(jié)果表明,本文提出的監(jiān)測方法在延長網(wǎng)絡(luò)生命周期的同時(shí),也能夠保證較好的監(jiān)測精度。最后,本文基于克里金插值估計(jì)算法,設(shè)計(jì)并實(shí)現(xiàn)了一個(gè)WSNs環(huán)境監(jiān)測仿真系統(tǒng),并進(jìn)行了演示。
[Abstract]:With the development and progress of information technology, Internet of things (Internet of things) has been gradually integrated into all aspects of social life. It has been widely used in many fields, such as industrial production, environmental monitoring, medical health and so on. Wireless Sensor Networks (WSNs) is one of the infrastructure and key technologies of the Internet of things. It has the characteristics of self-organization, large scale and dense distribution. Limited by node cost, network energy resources, computing resources, storage resources and so on are very limited. In this case, how to improve network performance is the focus of WSNs application research. In this paper, the method of environmental monitoring based on WSNs is studied. Node scheduling algorithm is an effective way to prolong the WSNs life cycle, but the node dormancy will lead to the lack of monitoring data, which leads to the decrease of monitoring accuracy. Therefore, the Kriging algorithm is used to estimate the missing data. Aiming at the problem that the fitting accuracy of the ordinary Kriging algorithm variogram model is not high, this paper introduces the NM simplex method based on Gao Si mutation, and proposes an improved Kriging algorithm to improve the Kriging algorithm. The simulation results show that the estimation accuracy of the improved algorithm is better than that of the ordinary Kriging algorithm. Then, the improved Kriging algorithm is applied to WSNs, and combined with the energy equilibrium sleep scheduling algorithm, a WSNs interpolation estimation algorithm based on improved Kriging is proposed. The simulation results show that the proposed method can not only prolong the network life cycle, but also ensure better monitoring accuracy. Finally, based on the Kriging interpolation estimation algorithm, a WSNs environment monitoring simulation system is designed and implemented.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;TN929.5

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