[1]刘芳[],马争先[]. 基于马尔可夫博弈的WSN功率控制研究[J].计算机技术与发展,2017,27(04):188-191.
 LIU Fang[],MA Zheng-xian[]. Investigation on WSN Power Control with Markov Game[J].,2017,27(04):188-191.
点击复制

 基于马尔可夫博弈的WSN功率控制研究()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年04期
页码:
188-191
栏目:
应用开发研究
出版日期:
2017-04-10

文章信息/Info

Title:
 Investigation on WSN Power Control with Markov Game
文章编号:
1673-629X(2017)04-0188-04
作者:
刘芳[1]马争先[2]
 1.广西财经学院 实验教学中心;2.格力电器股份有限公司
Author(s):
 LIU Fang[1]MA Zheng-xian[2]
关键词:
 马尔可夫博弈功率控制纳什均衡无线传感器网络
Keywords:
 Markov gamepower controlNash equilibriumwireless sensor network
分类号:
TP393
文献标志码:
A
摘要:
 无线传感器网络中的节点能量有限、工作环境复杂,易导致节点能量消耗不均,节点耗能不均将极大地缩短网络生命周期.针对无线传感器网络中节点能量有限和耗能不均问题,建立了一种基于马尔可夫博弈的功率控制模型.该模型引用分簇结构,确定研究对象为簇头节点;引入多信道技术,不同信道使用不同概率调节各自簇头节点的发射功率来降低节点之间的相互干扰,进行节点功率优化;通过迭代方式进行功率和概率补偿,求解功率控制模型中的纳什均衡,使节点发射功率达到最优,达到整个网络节点能量的均衡消耗,延长网络生命周期.仿真结果表明,该模型在网络节点能量消耗的均匀程度、加强节点之间的合作、减少节点间信道竞争和延长网络生命周期上都有显著效果.
Abstract:
 It can result in imbalance of energy consuming and shortening of network life that limited energy and complex work environment of network nodes in wireless sensor network.Aiming at limited energy and imbalance of energy consuming in wireless sensor networks,the power control model is constructed based on Markov game.Referencing cluster structure,research object are cluster head nodes.Then introducing multi-channel technology,the node power can be optimized through using different probability to adjust transmit power with different channel to reduce mutual interference between nodes.Method of adopting iteration computation is used to compensate transmitting power and probability to obtain the Nash equilibrium,to optimize node transmitting power,to balance network nodes energy consumption and prolong left-time of the network.The experimental results show that the model can effectively improve the uniformity of energy consumption of network nodes,strengthen cooperation between nodes,reduce node channel competition and prolong left-time of the network.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
 ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(04):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
 LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(04):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
 HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(04):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
 HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(04):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
 LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(04):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
 WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(04):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
 LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(04):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
 SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(04):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
 YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(04):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
 YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(04):47.

更新日期/Last Update: 2017-06-19