本文選題：水下無線傳感器網(wǎng)絡(luò) + 錯誤信標(biāo)篩選　； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：水下無線傳感器網(wǎng)絡(luò)由部署在監(jiān)測水域的低成本、自組織的智能傳感器組成以去協(xié)同完成水域環(huán)境監(jiān)測的任務(wù),在環(huán)境監(jiān)測、無人監(jiān)控深海區(qū)域、自然災(zāi)難預(yù)防、軍事預(yù)防等領(lǐng)域有著廣泛的應(yīng)用。而在大部分實際應(yīng)用中,采集的數(shù)據(jù)必須標(biāo)記位置信息時才構(gòu)成具體物理意義。此外,準(zhǔn)確的節(jié)點位置信息有助于提高路由協(xié)議的效率,優(yōu)化網(wǎng)絡(luò)的拓?fù)湓O(shè)計及均衡網(wǎng)絡(luò)不同區(qū)域的能耗,因此對水下無線傳感器網(wǎng)絡(luò)中的定位算法進行研究是非常必要的。在復(fù)雜而惡劣的水下環(huán)境中,由于水流的變化、水下生物觸碰、強烈的電磁波干擾等原因,部分信標(biāo)節(jié)點很容易移動或損壞,導(dǎo)致這些信標(biāo)失效或其位置信息可能出錯,而在這些錯誤(或失效)信標(biāo)的輔助下,普通節(jié)點的定位誤差顯然會增大。針對錯誤(或失效)信標(biāo)問題,本文分別提出了兩種基于錯誤信標(biāo)篩選的定位算法。本文首先提出了一種基于k均值的錯誤信標(biāo)篩選算法,在周圍信標(biāo)節(jié)點的幫助下,每一個信標(biāo)節(jié)點使用改進的三邊測量法去定位,然后利用k均值聚類算法篩選出具有最大定位誤差的信標(biāo)節(jié)點,剩余的信標(biāo)節(jié)點繼續(xù)迭代以上過程,直到每個信標(biāo)節(jié)點的估計誤差不超過預(yù)設(shè)閥值。另外,針對k均值聚類算法的分類準(zhǔn)確性很大程度依賴初始中心值并且分類結(jié)果易陷入局部最優(yōu)的問題,本文將粒子群算法和k均值算法相結(jié)合,又提出了一種水下無線傳感器網(wǎng)絡(luò)中基于粒子群聚類的錯誤信標(biāo)篩選算法,可以更加準(zhǔn)確地篩選出錯誤信標(biāo)。最后本文對上述算法進行了復(fù)雜度分析和仿真測試。結(jié)果表明本文所提出的算法能夠有效地篩選出錯誤信標(biāo),從而為普通節(jié)點提供一個可靠的信標(biāo)集合,最終達到提高普通節(jié)點定位精度的目的。
[Abstract]:Underwater wireless sensor networks are made up of low-cost, self-organizing intelligent sensors deployed in monitoring waters to work together to perform environmental monitoring tasks in waters, in environmental monitoring, in unmonitored deep-sea areas, and in natural disaster prevention. Military prevention and other fields have a wide range of applications. In most practical applications, the collected data must mark the location information to form a specific physical meaning. In addition, accurate node location information is helpful to improve the efficiency of routing protocol, optimize the topology design of the network and equalize the energy consumption in different areas of the network. Therefore, it is very necessary to study the localization algorithm in underwater wireless sensor networks. In the complex and bad underwater environment, some beacon nodes are easily moved or damaged due to the change of water flow, underwater biological contact, strong electromagnetic interference and so on, resulting in the failure of these beacons or the possible errors in their location information. With the aid of these false (or invalid) beacons, the location errors of common nodes will obviously increase. Aiming at the problem of error (or failure) beacon, two localization algorithms based on error beacon filtering are proposed in this paper. In this paper, an error beacon selection algorithm based on k-means is proposed. With the help of the surrounding beacon nodes, each beacon node uses an improved trilateral measurement method to locate the beacon. Then the k-means clustering algorithm is used to select the beacon nodes with the maximum location error, and the remaining beacon nodes continue to iterate the above process until the estimation error of each beacon node does not exceed the preset threshold. In addition, aiming at the problem that the classification accuracy of k-means clustering algorithm depends on the initial center value to a great extent and the result of classification is prone to fall into local optimum, the particle swarm optimization algorithm and the k-means algorithm are combined in this paper. An error beacon selection algorithm based on particle clustering in underwater wireless sensor networks is proposed, which can screen error beacons more accurately. Finally, the complexity of the algorithm is analyzed and simulated. The results show that the algorithm proposed in this paper can effectively screen out error beacons, thus providing a reliable set of beacons for ordinary nodes, and finally achieving the purpose of improving the positioning accuracy of common nodes.
【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】：TP212.9;TN929.3

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本文編號：2080071