發(fā)布時(shí)間：2018-06-18 18:01

  本文選題：無線傳感器網(wǎng)絡(luò) + K-means算法��； 參考：《信號處理》2017年08期

【摘要】：針對典型的LEACH分簇式路由協(xié)議分簇不均勻,簇頭節(jié)點(diǎn)分布隨機(jī)導(dǎo)致網(wǎng)絡(luò)能量消耗大的情況,本文提出一種基于死亡節(jié)點(diǎn)數(shù)目反饋的K-means分簇算法。首先通過K-means算法劃分簇的個(gè)數(shù),選擇簇的中心節(jié)點(diǎn)為該簇的簇頭,并通過位置集中性得到集中性較大的若干個(gè)節(jié)點(diǎn)為主簇頭群,其中最大的為主簇頭,自此完成初始化。此后用一個(gè)受死亡節(jié)點(diǎn)數(shù)調(diào)控的自適應(yīng)打分函數(shù)更新每一輪的簇頭和主簇頭。主簇頭只用于融合并傳輸數(shù)據(jù)并不負(fù)責(zé)感知環(huán)境信息。仿真實(shí)驗(yàn)結(jié)果表明:本算法相較LEACH以及傳統(tǒng)的基于K-means的分簇算法,在整個(gè)網(wǎng)絡(luò)的生存時(shí)間上分別提高了35%和25%。同時(shí)證明:反饋機(jī)制的加入和主簇頭的選取都有利于網(wǎng)絡(luò)壽命的提升。
[Abstract]:In this paper, a K-means clustering algorithm based on the number of dead nodes is proposed in this paper. Firstly, the number of clusters is divided by the K-means algorithm, and the center node of the cluster is selected as the cluster head of the cluster, and the location concentration is set through the location concentration. The largest cluster head is the main cluster head group, the largest cluster head is the main cluster head, and then the initialization is completed. After that, an adaptive scoring function controlled by the number of dead nodes is used to update the cluster head and the main cluster head of each round. The cluster head is used only for fusion and transmission of data and is not responsible for the perception of environmental information. Simulation experimental results table Ming: this algorithm is compared with LEACH and the traditional K-means based clustering algorithm. It has been improved by 35% and 25%. in the lifetime of the whole network, respectively. It is proved that the feedback mechanism and the selection of the main cluster head are all beneficial to the improvement of network life.
【作者單位】： 安徽大學(xué)電子信息工程學(xué)院計(jì)算智能與信號處理教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：安徽省科技攻關(guān)項(xiàng)目(1501b042205)
【分類號】：TN929.5;TP212.9

【相似文獻(xiàn)】

相關(guān)期刊論文 前3條

1 賈花萍;李堯龍;哈渭濤;史曉影;;K-means聚類神經(jīng)網(wǎng)絡(luò)分類器在睡眠腦電分期中的應(yīng)用研究[J];河南科學(xué);2012年06期

2 楊懷珍;李玲華;;基于K-means聚類與決策樹的有線電視交互服務(wù)訂制預(yù)測[J];微型機(jī)與應(yīng)用;2009年24期

3 關(guān)欣;孫祥威;曹昕瑩;;改進(jìn)的K-Means算法在特征關(guān)聯(lián)中的應(yīng)用[J];雷達(dá)科學(xué)與技術(shù);2014年01期

相關(guān)碩士學(xué)位論文 前1條

1 陳智;基于K-means聚類算法的機(jī)會網(wǎng)絡(luò)群組移動模型及其長相關(guān)性研究[D];湘潭大學(xué);2015年

，

本文編號：2036388