本文選題：WSN + 機(jī)場(chǎng)噪聲監(jiān)測(cè)��； 參考：《南京航空航天大學(xué)》2014年碩士論文

【摘要】：民航業(yè)的不斷發(fā)展使得困擾民航已久的機(jī)場(chǎng)噪聲問(wèn)題愈發(fā)嚴(yán)重，有效地監(jiān)測(cè)機(jī)場(chǎng)噪聲是關(guān)系民航業(yè)持續(xù)健康發(fā)展的關(guān)鍵。傳感器網(wǎng)絡(luò)被公認(rèn)為是二十一世紀(jì)里最具影響力的改變世界十大技術(shù)之一，而無(wú)線傳感器網(wǎng)絡(luò)技術(shù)更是廣泛地應(yīng)用于各種環(huán)境監(jiān)測(cè)系統(tǒng)中�；赪SN的機(jī)場(chǎng)噪聲監(jiān)測(cè)系統(tǒng)為細(xì)粒度機(jī)場(chǎng)噪聲監(jiān)測(cè)與控制提供了可能。采用WSN技術(shù)，機(jī)場(chǎng)噪聲感知監(jiān)測(cè)點(diǎn)可以在廣大的區(qū)域中立體分布，全天時(shí)、全天候地進(jìn)行數(shù)據(jù)采集。 本文為監(jiān)測(cè)機(jī)場(chǎng)周圍噪聲數(shù)據(jù)狀況，根據(jù)機(jī)場(chǎng)內(nèi)部噪聲數(shù)據(jù)密集和機(jī)場(chǎng)周圍居民小區(qū)附近噪聲數(shù)據(jù)離散的特點(diǎn)，提出針對(duì)機(jī)場(chǎng)內(nèi)部二維環(huán)境的SANE-IGAOD監(jiān)測(cè)點(diǎn)優(yōu)化布局算法及針對(duì)機(jī)場(chǎng)周圍居民小區(qū)三維環(huán)境的MANE-IGHKCA監(jiān)測(cè)點(diǎn)優(yōu)化布局算法。 SANE-IGAOD模型先將目標(biāo)區(qū)域網(wǎng)格化，利用INM噪聲預(yù)測(cè)軟件來(lái)計(jì)算各個(gè)網(wǎng)格點(diǎn)在每個(gè)飛機(jī)噪聲事件發(fā)生時(shí)的預(yù)測(cè)到的噪聲值，根據(jù)SANE的限值來(lái)確定各個(gè)網(wǎng)格點(diǎn)監(jiān)測(cè)到的噪聲事件，再利用改進(jìn)的遺傳算法求得近似最優(yōu)解，使得解集的傳感器節(jié)點(diǎn)能覆蓋所有的有效噪聲事件并且節(jié)點(diǎn)數(shù)目盡可能少。 MANE-IGHKCA模型考慮機(jī)場(chǎng)附近環(huán)境復(fù)雜，，需要進(jìn)行三維全覆蓋，因此考慮三維WSN的布局；首先將三維目標(biāo)區(qū)域網(wǎng)格化，根據(jù)MANE的限值來(lái)確定各個(gè)網(wǎng)格點(diǎn)監(jiān)測(cè)到的噪聲事件，采用k重覆蓋的迭代貪婪啟發(fā)式算法得到監(jiān)測(cè)點(diǎn)的布局位置，使得部署的傳感器節(jié)點(diǎn)能覆蓋所有的有效噪聲事件且節(jié)點(diǎn)數(shù)目也盡可能少。 實(shí)驗(yàn)仿真結(jié)果表明兩種監(jiān)測(cè)點(diǎn)布局算法能獲得較高的無(wú)線傳感器網(wǎng)絡(luò)覆蓋質(zhì)量，分別能保證目標(biāo)區(qū)域內(nèi)用盡量少的監(jiān)測(cè)點(diǎn)覆蓋所有的飛機(jī)噪聲事件。
[Abstract]:With the continuous development of civil aviation industry, the airport noise problem which has been puzzling civil aviation for a long time has become more and more serious. The effective monitoring of airport noise is the key to the sustained and healthy development of civil aviation industry. Sensor network is recognized as one of the ten most influential technologies in the world in the 21 century, and wireless sensor network technology is widely used in various environmental monitoring systems. The airport noise monitoring system based on WSN provides the possibility for fine grained airport noise monitoring and control. Using WSN technology, airport noise sensing monitoring points can be distributed stereoscopically in a wide area, and data can be collected all day and all day. In order to monitor the noise data around the airport, according to the characteristics of the dense noise data inside the airport and the discrete noise data near the residential area around the airport, The optimal layout algorithm of SANE-IGAOD monitoring points for the two-dimensional environment of the airport and the optimal layout algorithm of the MANE-IGHKCA monitoring points for the three-dimensional environment of the residential area around the airport are proposed. The SANE-IGAOD model first grips the target area and calculates the predicted noise values of each grid point at the time of each aircraft noise event by using INM noise prediction software. According to the limit value of SANE, the noise events monitored by each grid point are determined. Then the improved genetic algorithm is used to obtain the approximate optimal solution so that the sensor nodes of the solution set can cover all the effective noise events and the number of nodes is as small as possible. Considering the complex environment near the airport, the MANE-IGHKCA model considers the layout of 3D WSN. Firstly, the noise events monitored by each grid point are determined according to the limited values of MANE. The k-overlay iterative greedy heuristic algorithm is used to obtain the location of the monitoring points, so that the deployed sensor nodes can cover all the effective noise events and the number of nodes is as small as possible. The simulation results show that the two algorithms can achieve high coverage quality of wireless sensor networks and can cover all aircraft noise events with as few monitoring points as possible in the target area.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：V351;TB53

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本文編號(hào)：1970555