發(fā)布時間：2018-03-13 00:01

  本文選題：無線傳感器網(wǎng)絡(luò)　切入點：時間相關(guān)性　出處：《集美大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在智能交通系統(tǒng)(Intelligent Transportation System,ITS)的交通設(shè)施中增加一種無線傳感器網(wǎng)絡(luò)技術(shù),能夠從根本上緩解困擾現(xiàn)代交通的安全、通暢、節(jié)能和環(huán)保等問題,同時還可以提高交通工作效率。無線傳感器網(wǎng)絡(luò)(Wireless Sensor Network,WSN)通過無線傳感器節(jié)點獲取環(huán)境信息,自組織地進行無線通信和組網(wǎng),在無線傳感器網(wǎng)絡(luò)中,單個傳感器節(jié)點周期性采集到的數(shù)據(jù)在時間上可能是相關(guān)的,地理位置相鄰的傳感器節(jié)點收集到的數(shù)據(jù)在空間上往往也是相關(guān)的,于是,可以使用某種變換來去除其中的冗余信息,達到數(shù)據(jù)壓縮、節(jié)省傳輸能耗的目的。本文對基于時空相關(guān)性的WSN節(jié)能策略進行研究。首先,在對周期性采集到的數(shù)據(jù)進行相關(guān)性分析,研究數(shù)據(jù)采集頻率和數(shù)據(jù)失真度之間的關(guān)系,對各種有損壓縮及無損壓縮的性能進行比較的基礎(chǔ)上,提出一種改進的LTC(Lightweight Temporal Compression)算法,該算法基于判斷門限,將重構(gòu)精度與采集頻率進行折衷。MATALB仿真結(jié)果表明,算法在保證數(shù)據(jù)失真度的同時,可有效地抑制數(shù)據(jù)發(fā)送頻率,減少傳輸冗余,從而達到節(jié)約能耗的目的。其次,討論了節(jié)點間距離、信道衰耗等空間相關(guān)因素對失真度的影響,證明了在滿足空間相關(guān)性的條件下,可以通過選用代表節(jié)點的方法,減少全網(wǎng)數(shù)據(jù)傳輸。同時引入相關(guān)半徑概念,利用相關(guān)半徑構(gòu)建成相關(guān)簇,根據(jù)相關(guān)性系數(shù)和節(jié)點位置信息確定失真函數(shù)。合理選擇簇頭節(jié)點和發(fā)送數(shù)據(jù)方式,既有效地利用數(shù)據(jù)之間的空間相關(guān)性,保證數(shù)據(jù)失真在一定范圍內(nèi),又避免了因數(shù)據(jù)傳輸量過大而能量消耗過大。并深入分析了GCC(Greedy Corrected Clustering,GCC)和K-Means兩種與空間相關(guān)性結(jié)合的節(jié)點分簇算法,經(jīng)MATLAB仿真比較得知,在對WSN中節(jié)點進行相關(guān)分簇時,這兩種算法都有效的抑制了數(shù)據(jù)傳輸量,降低了數(shù)據(jù)冗余,從而網(wǎng)絡(luò)能耗得到優(yōu)化。另外,經(jīng)數(shù)據(jù)對比可知,K-Means算法比GCC算法可以得到更加均勻的分簇,且在相同簇數(shù)量的情況下獲得較小的平均失真。最后,利用相關(guān)性分析結(jié)果,在典型的LEACH(Low Energy Adaptive Clustering Hierarchy)算法中,將相關(guān)性分簇的K-Means和GCC算法應(yīng)用于LEACH,仿真實驗表明,在不同的場景中,不同的算法融合適用于不同的需求,算法融合可進一步實現(xiàn)網(wǎng)絡(luò)能耗的節(jié)約,提高數(shù)據(jù)精度,減小失真,延長網(wǎng)絡(luò)壽命。
[Abstract]:Adding a kind of wireless sensor network technology to the transportation facilities of Intelligent Transportation system (ITS) can fundamentally alleviate the problems of safety, smooth, energy saving and environmental protection that beset modern traffic. At the same time, it can also improve traffic efficiency. Wireless Sensor Network (WSNs) can obtain environmental information through wireless sensor nodes, self-organize wireless communication and network, in wireless sensor networks, The data collected periodically by a single sensor node may be related in time, and the data collected by a sensor node adjacent to a geographical location are often also spatially relevant. Some transformation can be used to remove the redundant information and achieve the purpose of data compression and energy saving. In this paper, the WSN energy-saving strategy based on temporal and spatial correlation is studied. Based on the correlation analysis of periodically collected data, the relationship between data acquisition frequency and data distortion, and the comparison of various lossy compression and lossless compression performance, an improved LTC(Lightweight Temporal compression algorithm is proposed. Based on the threshold, the reconstruction accuracy and acquisition frequency are compromised. The simulation results show that the algorithm can effectively suppress the data transmission frequency and reduce the transmission redundancy while ensuring the data distortion. In order to save energy consumption, the influence of spatial correlation factors, such as distance between nodes, channel decay and so on, on the distortion degree is discussed. It is proved that the method of representing nodes can be used to satisfy the condition of spatial correlation. At the same time, the concept of correlation radius is introduced, the correlation radius is used to construct the correlation cluster, and the distortion function is determined according to the correlation coefficient and node position information. It not only makes use of the spatial correlation between the data effectively, but also ensures the distortion of the data within a certain range. It also avoids the excessive energy consumption due to too much data transmission. Two node clustering algorithms, GCC(Greedy Corrected clustering (GCC(Greedy) and K-Means (K-Means), which are combined with spatial correlation, are analyzed in detail. The results of MATLAB simulation show that, when the nodes are clustered in WSN, These two algorithms can effectively suppress the amount of data transmission, reduce the data redundancy, and optimize the network energy consumption. In addition, the K-Means algorithm can get more uniform clustering than the GCC algorithm. In the case of the same number of clusters, the average distortion is small. Finally, in the typical LEACH(Low Energy Adaptive Clustering hierarchy algorithm, the K-Means and GCC algorithms of correlation clustering are applied to read. In different scenarios, different algorithms can be used to meet different requirements. Algorithm fusion can further achieve energy saving, improve data accuracy, reduce distortion and prolong network life.
【學(xué)位授予單位】：集美大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495

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