本文選題：數(shù)據(jù)融合　切入點(diǎn)：無(wú)線傳感器網(wǎng)絡(luò)　出處：《計(jì)算機(jī)測(cè)量與控制》2014年04期

【摘要】：為提高無(wú)線傳感器網(wǎng)絡(luò)(WSN)數(shù)據(jù)融合效率,減少網(wǎng)絡(luò)的通信量以及降低傳感網(wǎng)的能量消耗,提出一種基于粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的無(wú)線傳感器網(wǎng)絡(luò)數(shù)據(jù)融合算法;該算法將粒子群算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的權(quán)值和閾值后,與傳感器網(wǎng)絡(luò)分簇路由協(xié)議有機(jī)結(jié)合,將無(wú)線傳感器網(wǎng)絡(luò)中簇頭和節(jié)點(diǎn)等同于BP神經(jīng)網(wǎng)絡(luò)里的神經(jīng)元,利用優(yōu)化后的BP神經(jīng)網(wǎng)絡(luò)有效地提取WSN數(shù)據(jù)融合原始數(shù)據(jù)之中的少量特征數(shù)據(jù),之后把提取的特征數(shù)據(jù)發(fā)送到匯聚節(jié)點(diǎn),進(jìn)而提升數(shù)據(jù)融合效率,延長(zhǎng)網(wǎng)絡(luò)生存周期;仿真實(shí)驗(yàn)證明,與LEACH算法、BP神經(jīng)網(wǎng)絡(luò)和GABP算法相比,該算法可有效減少網(wǎng)絡(luò)通信量,降低節(jié)點(diǎn)總能耗的15%,延長(zhǎng)網(wǎng)絡(luò)生存時(shí)間。
[Abstract]:In order to improve the data fusion efficiency of wireless sensor networks (WSNs), reduce the network traffic and reduce the energy consumption of wireless sensor networks, a data fusion algorithm based on particle swarm optimization BP neural network (PSO) for wireless sensor networks (WSN) is proposed. After optimizing the weight and threshold of BP neural network by particle swarm optimization, the algorithm combines with the clustering routing protocol of sensor network, and equates the cluster head and node in wireless sensor network with the neuron in BP neural network. The optimized BP neural network is used to effectively extract a small amount of feature data from the original data of WSN data fusion, and then send the extracted feature data to the convergence node to improve the efficiency of data fusion and prolong the lifetime of the network. The simulation results show that compared with LEACH algorithm and GABP algorithm, the proposed algorithm can effectively reduce the network traffic, reduce the total energy consumption of nodes and prolong the network lifetime.
【作者單位】： 河南城建學(xué)院計(jì)算機(jī)科學(xué)與工程學(xué)院;
【基金】：河南省科技廳基金項(xiàng)目(102102210020)
【分類(lèi)號(hào)】：TP212.9;TN929.5

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