无线传感器网络中基于代理的D-S数据融合

J4 ›› 2014, Vol. 36 ›› Issue (10): 1919-1924.

无线传感器网络中基于代理的D-S数据融合

孙子文1，刘加杰2，纪志成1

(1.江南大学物联网工程学院，江苏无锡 214122;2.江苏商达水务有限公司，江苏无锡 214122)

收稿日期:2013-03-05 修回日期:2013-06-20 出版日期:2014-10-25 发布日期:2014-10-25
基金资助:
国家自然科学基金资助项目(61373126)；江苏省自然科学基金资助项目(BK20131107)

Agent based D-S data fusion in wireless sensor networks

SUN Ziwen1,LIU Jiajie2,JI Zhicheng1

(1.School of Internet of Things Engineering,Jiangnan University,Wuxi 214122;
2.Jiangsu Sunda Water Co.,Ltd,Wuxi 214122,China)

Received:2013-03-05 Revised:2013-06-20 Online:2014-10-25 Published:2014-10-25

摘要/Abstract

摘要：

提出了一种无线传感器网络中基于移动代理带证据权的DS融合算法。引入证据权对证据进行修正以降低冲突数据对融合结果的影响。采用三级DS组合规则进行融合决策：节点级融合计算单个节点时间域融合检测概率；簇内级融合计算簇内节点间空间域融合检测概率获取局部决策结果；簇间级融合计算簇间的融合检测概率获取最终的全局决策结果。仿真结果表明，本算法能以较小的能耗代价获取准确的融合结果并有效降低冲突数据对融合结果的影响。

关键词: 无线传感器网络, 移动代理, 数据融合, D-S证据理论, 证据权

Abstract:

An agent-based D-S data fusion algorithm with evidence weights in wireless sensor networks is proposed.Evidence weights are adopted to modify the evidences before fusion in order to reduce the impact of data conflicts on fusion result.Three-level D-S combination rules are used to make fusion decision, the fusion detection probabilities of single node in time domain are calculated in the node-level fusion,and the detection probabilities of intra-cluster nodes in space domain are calculated to obtain the local decision in the intra-cluster level fusion.The fusion detection probabilities among clusters are calculated to obtain the final global decision in the inter-cluster level fusion.The stimulation results show that the proposed algorithm can obtain an accurate fusion result with lower energy consumption.

Key words: wireless sensor network;mobile agent;data fusion;D-S evidence theory;evidence weight

孙子文1，刘加杰2，纪志成1. 无线传感器网络中基于代理的D-S数据融合[J]. J4, 2014, 36(10): 1919-1924.

SUN Ziwen1,LIU Jiajie2,JI Zhicheng1. Agent based D-S data fusion in wireless sensor networks [J]. J4, 2014, 36(10): 1919-1924.

参考文献［14］

［1］	Li Li,Li Weijia．The analysis of data fusion energy consumption in WSN［C］∥Proc of International Conference on System Science, Engineering Design and Manufacturing Informatization, 2011:310313．
［2］	Rogova G L, Bosse E．Information quality in information fusion［C］∥Proc of the 13th International Conference on Information Fusion, 2010:18.
［3］	Pohanka P, Hrabovsky J, Fiedler M．Sensor simulation environment for sensor data fusion ［C］∥Proc of the 14th International Conference on Information Fusion, 2011:18.
［4］	Nicholson D．Defence applications of agentbased information fusion ［J］．Computer Journal, 2011, 54(2):263273.
［5］	Rajagopalan R．A multiobjective optimization approach for data fusion in mobile agent based distributed sensor networks［C］∥Proc of 2010 IEEE International Conference on Instrumentation and Measurement Technology, 2010:208212.
［6］	Zhou Yan,Wu Xia．Quantum neural network algorithm based on multiagent in target fusion recognition system［C］∥Proc of 2010 International Conference on Computational and Information Sciences, 2010:537540．
［7］	Chen Xiaoping,Huang He．Immune feed forward neural network for fault detection ［J］．Tsinghua Science and Technology, 2011, 16(3):272277.
［8］	Meghdad K, Hojat A, Mahmoud O, et al．Vibration condition monitoring of planetary gears based on decision level data fusion using dempstershafer theory of evidence［J］．Journal of Vibroengineering, 2012, 14(2):838851.
［9］	Li Yibo, Wang Ning, Zhou Chang, et al. Based on DS evidence theory of information fusion improved method［C］∥Proc of 2010 International Conference on Computer Application and System Modeling, 2010:V1416V1419.
［10］	Peng Huiping, Cao Xiaojun．Research conflict problem of DS evidence and its application in multisensor information fusion technology［C］∥Proc of IEEE International Conference on Information Theory and Information Security, 2010:V4314V4318.
［11］	Chen Xuhui, Yang Yongxu, Hui Yang, et al. Conflict problem of DS evidence and its application in multisensor information fusion technology［C］∥Proc of International Conference on Computer Application and System Modeling, 2010:V4314V4318．
［12］	Feng Haishan, Xu Xiaobin, Wen Chenglin．A new fusion method of conflicting interval evidence based on the similarity measure of evidence［J］．Journal of Electronics & Information Technology, 2012, 34(4):851857.(in Chinese)
［13］	Sun Ziwen, Li Hui, Ji Zhicheng．Fusion image steganalysis based on DempsterShafer evidence theory ［J］ .Control and Decision, 2011, 26(8):11921196.(in Chinese)
［14］	He You, Wang Guohong, Peng Yingning,et al．Appllication and information of multisensor［M］．Beijing：Publishing House of Electronics Industry，2010．(in Chinese)
	附中文参考文献：
［12］	冯海山，徐晓滨，文成林．基于证据相似性度量的冲突性区间证据融合方法［J］．电子与信息学报，2012, 34(4)：851857．
［13］	孙子文，李慧，纪志成．基于DS证据理论的融合图像隐写分析［J］．控制与决策，2011, 26(8):11921196．
［14］	何友，王囯红，彭应宁,等．多传感器信息融合及应用［M］．北京：电子工业出版社，2010．

[1]	罗仕杭, 何庆. 多策略融合改进的均衡优化算法及其应用[J]. 计算机工程与科学, 2023, 45(08): 1508-1520.
[2]	赵玉华, 贾向东, 胡海霞, 敬乐天. 无人机辅助的无线传感网络AoI最小化方案研究[J]. 计算机工程与科学, 2022, 44(07): 1216-1222.
[3]	张孟健, 汪敏, 王霄, 覃涛, 杨靖, . 混合粒子群-蝴蝶算法的WSN节点部署研究[J]. 计算机工程与科学, 2022, 44(06): 1013-1022.
[4]	高航, 吴嘉鑫, 陈龙, 武继刚. QoS保障下的无人机传感网安全通信路径规划[J]. 计算机工程与科学, 2022, 44(06): 1037-1045.
[5]	苟平章, 原晨, 张芬. 基于软件定义的WSNs非均匀分簇QoS路由算法[J]. 计算机工程与科学, 2022, 44(02): 227-236.
[6]	张晶, 魏淼, . 基于Delaunay三角划分策略的WSN区域覆盖优化研究[J]. 计算机工程与科学, 2021, 43(11): 1944-1951.
[7]	魏连锁, 陈齐齐, 韩建, 苏扬. 一种基于区域分裂与合并的势博弈网络拓扑控制算法[J]. 计算机工程与科学, 2021, 43(11): 2063-2068.
[8]	苟平章, 刘学治, 孙梦源, 何博. 基于跳距修正与狮群优化的WSNs三维定位算法[J]. 计算机工程与科学, 2021, 43(08): 1405-1412.
[9]	周静, 关玉蓉. 基于SDN的DWSN技术分析及性能优化研究[J]. 计算机工程与科学, 2021, 43(08): 1413-1421.
[10]	宋玲, 黄达胜. 一种基于狼群优化的改进DV_Hop定位算法[J]. 计算机工程与科学, 2021, 43(07): 1210-1218.
[11]	王俊喜, 陈桂芬. 能耗均衡的多跳多路径认知分层路由算法[J]. 计算机工程与科学, 2021, 43(03): 442-448.
[12]	张华南, 金红, 王峰. 无线传感器网络节能探索与研究[J]. 计算机工程与科学, 2021, 43(02): 295-303.
[13]	谢苗苗, 李华龙. 基于改进D-S证据理论的室内环境控制决策系统[J]. 计算机工程与科学, 2020, 42(05): 938-943.
[14]	苟平章，张芬，毛刚，贾向东. 基于AGNES聚类的能耗均衡WSNs优化路由算法[J]. 计算机工程与科学, 2020, 42(04): 620-627.
[15]	胡震海，王立松. 抗窃听攻击的传感器网络空间范围聚集查询处理算法[J]. 计算机工程与科学, 2020, 42(01): 46-54.