基于最优簇数和改进引力搜索的WSN路由算法

发布时间：2018-02-14 04:51

本文关键词： 无线传感器网络非均匀分簇引力搜索网络能耗生命周期　出处：《计算机应用》2017年12期 　论文类型：期刊论文

【摘要】：为了提高无线传感器网络(WSN)的能量利用效率,提出一种基于最优簇数和改进引力搜索的WSN路由算法(ONCIGS)。首先,根据非均匀分簇的思想计算最优簇数,并采用改进的凝聚嵌套(AGNES)算法实现网络的合理分簇;其次,将反向学习机制和精英策略思想引入到引力搜索算法中,并基于种群密度对作用力进行自适应调整,以提高搜索精度,加快收敛;然后,将簇头剩余能量的标准差作为目标函数,搜索能量均衡的簇间数据转发路径。实验结果表明,相比低功耗自适应集簇分层型(LEACH)路由算法和分布式能量均衡非均匀成簇(DEBUC)路由算法,ONCIGS在100 m×100 m网络规模下将网络生命周期分别延长41.94%和5.77%,在200 m×200 m网络规模下分别延长76.60%和7.82%。ONCIGS能够有效地延长网络寿命,提高能量效率。
[Abstract]:In order to improve the energy utilization efficiency of wireless sensor networks (WSNs), an WSN routing algorithm based on optimal cluster number and improved gravitational search is proposed. Firstly, the optimal cluster number is calculated according to the idea of non-uniform clustering. The improved agglomerate nesting algorithm is used to realize the reasonable clustering of the network. Secondly, the reverse learning mechanism and elite strategy are introduced into the gravitational search algorithm, and the force is adjusted adaptively based on the population density. In order to improve the searching accuracy and accelerate convergence, the standard deviation of residual energy of cluster head is taken as the objective function to search for the energy balanced data forwarding path between clusters. The experimental results show that, Compared with the low power adaptive cluster-hierarchical routing algorithm and the distributed energy equalization non-uniform clustering routing algorithm, ONCIGS extends the network life cycle by 41.94% and 5.77 respectively at 100m 脳 100m network scale, and divides the network at 200m 脳 200m network scale. Do not extend 76.60% and 7.82. ONCIGS can effectively extend the network life, Improve energy efficiency.
【作者单位】：辽宁工程技术大学电子与信息工程学院;辽宁工程技术大学研究生院;
【分类号】：TN929.5;TP212.9

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