本文關(guān)鍵詞：基于網(wǎng)絡(luò)編碼的傳感網(wǎng)數(shù)據(jù)流并行計(jì)算技術(shù)　出處：《南京理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 無線傳感網(wǎng) 網(wǎng)絡(luò)編碼 能量效率 Hadoop MapReduce框架 Single-Pass K-means

【摘要】：無線傳感網(wǎng)處于物聯(lián)網(wǎng)末梢,主要負(fù)責(zé)物聯(lián)網(wǎng)末端信息采集。隨著物聯(lián)網(wǎng)的發(fā)展,無線傳感網(wǎng)使用越來越廣泛,涉及領(lǐng)域也越來越多。云計(jì)算旨在對有意義的大規(guī)模數(shù)據(jù)集進(jìn)行有效的專業(yè)化處理。無線傳感網(wǎng)和云計(jì)算相結(jié)合已經(jīng)成為一個(gè)新的發(fā)展方向。本文設(shè)計(jì)的基于網(wǎng)絡(luò)編碼的傳感網(wǎng)數(shù)據(jù)流并行計(jì)算系統(tǒng),通過將無線傳感網(wǎng)和云計(jì)算技術(shù)相結(jié)合,以實(shí)現(xiàn)大規(guī)模傳感網(wǎng)數(shù)據(jù)集下的異常數(shù)據(jù)的快速聚類,為用戶決策提供有利輔助。本文的主要工作體現(xiàn)在以下方面:(1)針對無線傳感網(wǎng)中節(jié)點(diǎn)計(jì)算能力低,能量受限等不足,本文首先引入Reed-Solomon碼設(shè)計(jì)了一種構(gòu)造稀疏矩陣的方法來提高無線傳感網(wǎng)中節(jié)點(diǎn)的能量利用率。實(shí)驗(yàn)證明了本文設(shè)計(jì)的方法能夠提高無線傳感網(wǎng)中節(jié)點(diǎn)的能量利用率。(2)網(wǎng)絡(luò)編碼解碼工作會造成信宿時(shí)延,而且會增加節(jié)點(diǎn)的計(jì)算負(fù)擔(dān)。本文設(shè)計(jì)了適用于Hadoop集群的分布式解碼算法,將解碼工作放在集群中進(jìn)行,利用集群強(qiáng)大的計(jì)算能力來提高解碼效率,減輕了無線傳感網(wǎng)節(jié)點(diǎn)負(fù)擔(dān),并通過實(shí)驗(yàn)證明集群并行解碼的可行性,同時(shí)分析了集群中相關(guān)因素對解碼效率的影響。(3)針對傳統(tǒng)MapReduce框架下用于感知數(shù)據(jù)的異常檢測的k-means算法會造成極大的I/O消耗問題,提出了改進(jìn)的基于MapReduce的單遍k-means算法。文中,從理論上證明了本文提出的方法能夠降低程序執(zhí)行時(shí)的I/O消耗,并且實(shí)驗(yàn)結(jié)果顯示,本文設(shè)計(jì)的算法相對于傳統(tǒng)的基于MapReduce的k-means算法,在保證聚類效果的同時(shí),能夠降低執(zhí)行時(shí)間。
[Abstract]:Wireless sensor network is mainly responsible for Internet terminals, networking information collection terminal. With the development of IOT, wireless sensor network is more and more widely used, more and more involved in the field of cloud computing. Aimed at professional and effective for large-scale data meaningful set. Wireless sensor network and cloud computing has become a combination of a new direction of development. The design of the sensor network data encoding network flow based on parallel computing system, the wireless sensor network and cloud computing technology, to realize the fast clustering of abnormal data of large-scale sensor network data set under the favorable support for user decision. The main work of this paper is reflected in the following aspects: (1) for nodes in wireless sensor networks with low computing power, limited energy shortage, this paper introduces the design of Reed-Solomon codes is a method to improve the structure of sparse matrix Node line sensor network energy utilization. Experiment proves that this design method can improve the nodes in wireless sensor network energy utilization. (2) decoding network encoding will cause sink delay, but also increases the computational burden of nodes. This paper designs a distributed decoding algorithm for Hadoop cluster, the decoding in the cluster, to improve the decoding efficiency by using the powerful computing capability of cluster, reduce the burden of the node of wireless sensor network, and the experimental results proved that the feasibility of parallel cluster decoding, and analyses the related factors in the cluster on the decoding efficiency. (3) according to the traditional MapReduce framework k-means algorithm for anomaly detection sensing data the I/O will cause great consumption problems, proposed an improved MapReduce algorithm based on single pass k-means. In this paper, theoretically proved that this method can The I/O consumption of program execution is reduced, and the experimental results show that the algorithm designed in this paper can reduce the execution time while guaranteeing the clustering effect compared with the traditional MapReduce based k-means algorithm.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN929.5;TP212.9

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本文編號：1414140