【摘要】：無線傳感器網(wǎng)絡(luò)(Wireless Sensor Network,WSN)由大量分布在特定區(qū)域,具有感知、存儲和通信能力的無線節(jié)點(diǎn)組成。WSN具有廣泛地應(yīng)用前景。譬如應(yīng)用于森林防火、智慧小區(qū)、智能穿戴、火車站安全監(jiān)測等。但是受WSN自身特點(diǎn)的限制,節(jié)點(diǎn)需要將收集到的數(shù)據(jù)進(jìn)行融合處理,降低能耗,提高數(shù)據(jù)準(zhǔn)確性。因此,無線傳感器網(wǎng)絡(luò)數(shù)據(jù)融合技術(shù)成為研究重點(diǎn)之一。本文將節(jié)省能量消耗作為基本要求,以提高數(shù)據(jù)融合準(zhǔn)確性和實(shí)時性為主要目標(biāo),在分簇技術(shù)和數(shù)據(jù)融合技術(shù)上做了創(chuàng)新性研究。本文具體內(nèi)容如下:首先,本文對無線傳感器網(wǎng)絡(luò)的背景現(xiàn)狀、體系結(jié)構(gòu)、網(wǎng)絡(luò)特點(diǎn),以及無線傳感器網(wǎng)絡(luò)的關(guān)鍵技術(shù)——數(shù)據(jù)融合技術(shù)進(jìn)行概述,并對數(shù)據(jù)融合算法的分類、性能和局限進(jìn)行了深入分析。其次,本文通過分析現(xiàn)有分簇技術(shù),提出了一種新的能量均衡的模糊非均勻分簇算法(Energy Enhanced Unequal Fuzzy Clustering Algorithm,EEUFC)。該算法通過計算節(jié)點(diǎn)相對密度和到基站的距離隨機(jī)選取臨時簇頭;然后引入模糊理論估計競爭半徑,并將相對密度與競爭半徑、剩余能量一起作為選舉最終簇頭的參考變量。成簇時,節(jié)點(diǎn)根據(jù)距離和代價選擇簇頭,更好地避免了“熱區(qū)”現(xiàn)象,均衡了能量消耗。再次,本文將模糊邏輯、矩陣加權(quán)等思想應(yīng)用于數(shù)據(jù)融合技術(shù),考慮了信息收集和傳輸中對數(shù)據(jù)準(zhǔn)確性和實(shí)時性的要求,提出了一種模糊加權(quán)的數(shù)據(jù)融合算法(Fuzzy Weighted Algorithm for Data Fusion,FWADF)。該算法基于分簇模型,考慮了外界因素的影響,在計算可信度的基礎(chǔ)上,對收到的數(shù)據(jù)分別在簇頭和基站中進(jìn)行融合處理,確保為用戶提供準(zhǔn)確、實(shí)時的數(shù)據(jù)信息。最后,通過使用仿真軟件NS-2(Network Simulator-Version 2,NS-2)對本文算法進(jìn)行了實(shí)驗(yàn)仿真。實(shí)驗(yàn)結(jié)果表明:本文提出的能量均衡的模糊非均勻分簇算法平衡了節(jié)點(diǎn)能量且避免了“熱區(qū)”;提出的模糊加權(quán)的數(shù)據(jù)融合算法提高了數(shù)據(jù)準(zhǔn)確性和實(shí)時性,二者共同延長了網(wǎng)絡(luò)的生命周期。
[Abstract]:Wireless sensor network (WSN) is composed of a large number of wireless nodes with sensing, storage and communication capabilities, which are distributed in a specific area. WSNs have a broad application prospect. For example, used in forest fire prevention, intelligent community, intelligent wear, railway station safety monitoring and so on. However, due to the limitation of WSN itself, nodes need to fuse the collected data to reduce energy consumption and improve the accuracy of data. Therefore, wireless sensor network data fusion technology has become one of the focus of research. In this paper, energy saving is taken as the basic requirement, and the main goal of this paper is to improve the accuracy and real-time of data fusion. The innovative research on clustering technology and data fusion technology has been done. The specific contents of this paper are as follows: firstly, this paper summarizes the background, architecture, network characteristics and the key technology of wireless sensor network data fusion, and classifies the data fusion algorithm. The performance and limitation are analyzed in depth. Secondly, by analyzing the existing clustering techniques, a new fuzzy nonuniform clustering algorithm, (Energy Enhanced Unequal Fuzzy Clustering algorithm, is proposed. By calculating the relative density of the node and the distance from the base station, the algorithm randomly selects the temporary cluster head, and then introduces the fuzzy theory to estimate the competition radius, and takes the relative density and the competition radius together as the reference variables for the election of the final cluster head. In clustering, the cluster heads are selected according to the distance and cost, thus avoiding the "hot zone" better and balancing the energy consumption. Thirdly, this paper applies fuzzy logic and matrix weighting to data fusion technology. Considering the requirement of data accuracy and real-time in information collection and transmission, a fuzzy weighted data fusion algorithm (Fuzzy Weighted Algorithm for Data fusion FWADF is proposed. Based on clustering model and considering the influence of external factors, the received data are fused in cluster head and base station on the basis of calculating credibility, so as to provide accurate and real-time data information for users. Finally, the simulation software NS-2 (Network Simulator-Version 2 / NS-2) is used to simulate the algorithm. The experimental results show that the proposed fuzzy non-uniform clustering algorithm balances node energy and avoids "hot zone", and the proposed fuzzy weighted data fusion algorithm improves the accuracy and real-time performance of the data. The two extend the life cycle of the network together.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9;TN929.5

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