搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于动态最小生成树路由协议的数据聚融算法

彭海霞 赵海 李大舟 林川

引用本文:
Citation:

基于动态最小生成树路由协议的数据聚融算法

彭海霞, 赵海, 李大舟, 林川

Data fusaggregation algorithm based on dynamic minimal spanning tree routing protocol

Peng Hai-Xia, Zhao Hai, Li Da-Zhou, Lin Chuan
PDF
导出引用
  • 大规模的地理环境监测,以及用来传输与处理数据的物理基础设施无法和监测区域的规模保持同样的增速,使得不可靠链路下的数据采集与处理呈现出一种饱和流状态,无线传感器网络的能力看似难以稳定. 另一方面,尽管理想网络模型的计算结果足够精确,然而,由于与实际应用偏差甚大,使得网络用户无法充分地分析和利用从工业现场所获得到的网络感知数据,并且没有针对网络规模和性能对数据聚融的影响进行分析. 为此,本文提出以”过渡区“作为工业现场仿真的假设条件,并在此基础上提出了一种面向实际应用的数据聚融算法,即基于可信度的数据聚融算法(R算法). 在具体设计R算法过程中,选用聚集和操作符SUM为例,通过对网络提供出的近似聚集和加以自动分析、综合,针对相对误差界限ε,计算出近似聚集和的可信度的下限η;并将近似聚集和、参数η 一同提供给用户,在为用户提供网络概要信息的同时,还提供了参数η作为对信息可信度的判断,以便指导用户对数据聚集结果进行深度处理和提高网络的感知性能. 仿真实验描述了过渡区内由于信噪比导致的链路不可靠所引起的η的变化规律;讨论了网络性能和规模对η的影响,随着网络运行周期的增加和网络规模的增大,η的值将逐渐靠近0;从而为WSNs从理论模型投入实际工业应用提供了理论依据和经验公式.
    Large-scale geographic environmental monitoring and physical infrastructure for transferring and processing data cannot maintain the same growth rate with the monitoring scale, thus make the data gathering and processing under unreliable links, showing a kind of saturation flow state; and the ability of wireless sensor network (WSN) seemingly cannot be stabilized. On the other hand, though the calculation results from ideal network model are accurate enough, they deviate from the practical application greatly, hence the network users cannot adequately analyze and utilize the sensed data from industrial field network, and also cannot analyze the influence of network size and performance on data fusaggregation. Because of these, we will present the‘transitional region phenomenon'to be one of assumptions for industrial field simulation, and propose a data fusaggregation algorithm for the practical application on this basis, i.e. data fusaggregation algorithm based on reliability (R algorithm). When designing the R algorithm, as an example of sum operator, the lower limit η of reliability of appreciate aggregation sum result will be calculated by analyzing and synthesizing the result automatically. Then the aggregation sum result and the value of η will be sent to users together. In addition to providing the summary information from the monitored area to users, R algorithm also provides the parameter η as the judgment of information reliability to facilitate users to do further handling of aggregation results and improve the WSN sensing performance. Simulation results describe the changing rule of reliability η caused by unreliable links from the signal-to-noise ratio in transitional region, and discuss the network influence of size and performance on reliability η, with the increase of network operation cycles and network scale, when the value of reliability η becomes gradually close to 0. And this provides theoretical foundations and empirical formulas for WSNs from theoretical model to practical industrial application.
    • 基金项目: 国家科技支撑计划(批准号:2012BAH82F04)、国家自然科学基金(批准号:61101121)和国家高技术研究发展计划(863计划)(批准号:2013AA102505)资助的课题.
    • Funds: Project supported by the funding from the National Science and Technology Support Program (Grant No. 2012BAH82F04), the National Natural Science Foundation of China (Grant No. 61101121), and the National High Technology Research and Development Program of China (Grant No. 2013AA102505).
    [1]

    Pei H, Li X, Mutka M W, Ning X 2013 IEEE Communications Surveys & Tutorials 15 101

    [2]

    Rozyyev A, Hasbullah H, Subhan F 2011 Research Journal of Information Technology 3 81

    [3]

    Szewczyk R, Osterweil E, Polastre J, Hamilton M, Mainwaring A, Estrin D 2004 Communications of the ACM 47 34

    [4]

    Chauhdary S H, Bashir A K, Shah S C, Park M S 2009 Journal of Applied Sciences 9 4247

    [5]

    Biswas P K, Phoha S 2006 IEEE Transactions on Computers 55 1033

    [6]

    Lee L T, Chen C W 2008 Information Technology Journal 7 737

    [7]

    Sabri N, Aljunid S A, Ahmad B, Yahya A, Kamaruddin R, Salim M S 2011 Journal of Applied Sciences 11 3104

    [8]

    Kumar D 2011 Research Journal of Environmental Sciences 5 105

    [9]

    Yick J, Mukherjee B, Ghosal D 2008 Computer Networks 52 2292

    [10]

    Arampatzis T, Lygeros J, Manesis S 2005 Proceedings of the 20th IEEE International Symposium on Intelligent Control (ISIC '05) 719

    [11]

    Tseng Y C, Pan M S, Tsai Y Y 2006 Computer 39 55

    [12]

    J Wu, S F Yuan, Zhao X, Yin Y, Ye W S 2007 Chin. Phys. B 16 1898

    [13]

    Hao Y, Foster R 2008 Chin. Phys. B 29 R27

    [14]

    Jang S D, Kang B W, Kim J 2013 Chin. Phys. B 22 025002

    [15]

    Wang C L, De D, Song W Z 2013 Knowledge-based Systems 37 346

    [16]

    Deligiannakis A, Kotidis Y, Rossopoulos N 2006 Information Systems 31 770

    [17]

    Guo J L 2010 Chin. Phys. B 19 120503

    [18]

    Tan R, Xing G L, Yuan Z H, Liu X, Yao J G 2013 ACM Transactions on Sensor Networks 9 28:1

    [19]

    Cong X, Liu S L, Ma R 2008 Acta Phys. Sin. 57 7487 (in Chinese)[丛蕊, 刘树林, 马锐 2008 物理学报 57 7487]

    [20]

    Wang P, He Y, L, Huang S 2013 Ad hoc Networks 11 1287

    [21]

    Lu Z Q, Tan S L, Biswas J 2013 Wireless Personal Communications 70 391

    [22]

    Wang Y Q, Yang X Y 2012 Acta Phys. Sin. 61 090202 (in Chinese) [王亚奇, 杨晓云 2012 物理学报 61 090202]

    [23]

    Liu Y, Zhang Q, Ni L M 2010 IEEE Transactions on Parallel and Distributed Systems 21 405

    [24]

    Wan P J, Huang S C, Wang L, Wan Z, Jia X 2009 The Tenth ACM International Symposium on Mobile Ad Hoc Networking and Computing 185

    [25]

    Akyildiz I F, Su W, Sankarasubramaniam Y, Cyirci E 2002 Computer Networks 38 393

    [26]

    Huang G Y, Li X W, He J 2006 IEEE Conference on Industrial Electronics and Applications, Singapore 1

    [27]

    Muruganathan S D, Ma C F, Bhasin R I, Fapojuwo A O 2005 IEEE Communications Magazine 43 8

    [28]

    Shen Z, Xie S Q, Pan C Y 2005 Probability Theory & Mathematical Statistics (Beijing: Higher Education Press) (in Chinese) [盛骤, 谢式千, 潘承毅 2005 概率论与数理统计(北京:高等教育出版社)]

    [29]

    Bernstein S, Bernstein R 2004 1st ed. Columbus: McGraw-Hill

    [30]

    Fazio P, De R F, Sottile C 2011 Performance Evaluation of Computer & Telecommunication Systems (SPECTS) 98

    [31]

    Zuniga M, Krishnamachari B 2004 IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks 517

    [32]

    Fischer H 2011 1st ed. New York Springer 194

    [33]

    He S M 2013 Ph. D. Dissertation (Changsha: Hunan University) [何施茗 2013 博士学位论文(长沙:湖南大学)]

    [34]

    Lindsey S, Raghavendra C, Sivalingam K M 2002 IEEE Transactions on Parallel and Distributed Systems 13 924

  • [1]

    Pei H, Li X, Mutka M W, Ning X 2013 IEEE Communications Surveys & Tutorials 15 101

    [2]

    Rozyyev A, Hasbullah H, Subhan F 2011 Research Journal of Information Technology 3 81

    [3]

    Szewczyk R, Osterweil E, Polastre J, Hamilton M, Mainwaring A, Estrin D 2004 Communications of the ACM 47 34

    [4]

    Chauhdary S H, Bashir A K, Shah S C, Park M S 2009 Journal of Applied Sciences 9 4247

    [5]

    Biswas P K, Phoha S 2006 IEEE Transactions on Computers 55 1033

    [6]

    Lee L T, Chen C W 2008 Information Technology Journal 7 737

    [7]

    Sabri N, Aljunid S A, Ahmad B, Yahya A, Kamaruddin R, Salim M S 2011 Journal of Applied Sciences 11 3104

    [8]

    Kumar D 2011 Research Journal of Environmental Sciences 5 105

    [9]

    Yick J, Mukherjee B, Ghosal D 2008 Computer Networks 52 2292

    [10]

    Arampatzis T, Lygeros J, Manesis S 2005 Proceedings of the 20th IEEE International Symposium on Intelligent Control (ISIC '05) 719

    [11]

    Tseng Y C, Pan M S, Tsai Y Y 2006 Computer 39 55

    [12]

    J Wu, S F Yuan, Zhao X, Yin Y, Ye W S 2007 Chin. Phys. B 16 1898

    [13]

    Hao Y, Foster R 2008 Chin. Phys. B 29 R27

    [14]

    Jang S D, Kang B W, Kim J 2013 Chin. Phys. B 22 025002

    [15]

    Wang C L, De D, Song W Z 2013 Knowledge-based Systems 37 346

    [16]

    Deligiannakis A, Kotidis Y, Rossopoulos N 2006 Information Systems 31 770

    [17]

    Guo J L 2010 Chin. Phys. B 19 120503

    [18]

    Tan R, Xing G L, Yuan Z H, Liu X, Yao J G 2013 ACM Transactions on Sensor Networks 9 28:1

    [19]

    Cong X, Liu S L, Ma R 2008 Acta Phys. Sin. 57 7487 (in Chinese)[丛蕊, 刘树林, 马锐 2008 物理学报 57 7487]

    [20]

    Wang P, He Y, L, Huang S 2013 Ad hoc Networks 11 1287

    [21]

    Lu Z Q, Tan S L, Biswas J 2013 Wireless Personal Communications 70 391

    [22]

    Wang Y Q, Yang X Y 2012 Acta Phys. Sin. 61 090202 (in Chinese) [王亚奇, 杨晓云 2012 物理学报 61 090202]

    [23]

    Liu Y, Zhang Q, Ni L M 2010 IEEE Transactions on Parallel and Distributed Systems 21 405

    [24]

    Wan P J, Huang S C, Wang L, Wan Z, Jia X 2009 The Tenth ACM International Symposium on Mobile Ad Hoc Networking and Computing 185

    [25]

    Akyildiz I F, Su W, Sankarasubramaniam Y, Cyirci E 2002 Computer Networks 38 393

    [26]

    Huang G Y, Li X W, He J 2006 IEEE Conference on Industrial Electronics and Applications, Singapore 1

    [27]

    Muruganathan S D, Ma C F, Bhasin R I, Fapojuwo A O 2005 IEEE Communications Magazine 43 8

    [28]

    Shen Z, Xie S Q, Pan C Y 2005 Probability Theory & Mathematical Statistics (Beijing: Higher Education Press) (in Chinese) [盛骤, 谢式千, 潘承毅 2005 概率论与数理统计(北京:高等教育出版社)]

    [29]

    Bernstein S, Bernstein R 2004 1st ed. Columbus: McGraw-Hill

    [30]

    Fazio P, De R F, Sottile C 2011 Performance Evaluation of Computer & Telecommunication Systems (SPECTS) 98

    [31]

    Zuniga M, Krishnamachari B 2004 IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks 517

    [32]

    Fischer H 2011 1st ed. New York Springer 194

    [33]

    He S M 2013 Ph. D. Dissertation (Changsha: Hunan University) [何施茗 2013 博士学位论文(长沙:湖南大学)]

    [34]

    Lindsey S, Raghavendra C, Sivalingam K M 2002 IEEE Transactions on Parallel and Distributed Systems 13 924

  • [1] 罗小元, 李昊, 马巨海. 基于最小刚性图代数特性的无线网络拓扑优化算法. 物理学报, 2016, 65(24): 240201. doi: 10.7498/aps.65.240201
    [2] 李小龙, 冯东磊, 彭鹏程. 一种基于势博弈的无线传感器网络拓扑控制算法. 物理学报, 2016, 65(2): 028401. doi: 10.7498/aps.65.028401
    [3] 蒋锐, 杨震. 基于质心迭代估计的无线传感器网络节点定位算法. 物理学报, 2016, 65(3): 030101. doi: 10.7498/aps.65.030101
    [4] 郝晓辰, 刘伟静, 辛敏洁, 姚宁, 汝小月. 一种无线传感器网络健壮性可调的能量均衡拓扑控制算法. 物理学报, 2015, 64(8): 080101. doi: 10.7498/aps.64.080101
    [5] 郝晓辰, 姚宁, 汝小月, 刘伟静, 辛敏洁. 基于生命期模型的无线传感器网络信道分配博弈算法. 物理学报, 2015, 64(14): 140101. doi: 10.7498/aps.64.140101
    [6] 刘浩然, 尹文晓, 董明如, 刘彬. 一种强容侵能力的无线传感器网络无标度拓扑模型研究. 物理学报, 2014, 63(9): 090503. doi: 10.7498/aps.63.090503
    [7] 方伟, 宋鑫宏. 基于Voronoi图盲区的无线传感器网络覆盖控制部署策略. 物理学报, 2014, 63(22): 220701. doi: 10.7498/aps.63.220701
    [8] 刘洲洲, 王福豹. 一种能耗均衡的无线传感器网络加权无标度拓扑研究. 物理学报, 2014, 63(19): 190504. doi: 10.7498/aps.63.190504
    [9] 刘彬, 董明如, 刘浩然, 尹荣荣, 韩丽. 基于综合故障的无线传感器网络无标度容错拓扑模型研究. 物理学报, 2014, 63(17): 170506. doi: 10.7498/aps.63.170506
    [10] 韩丽, 刘彬, 李雅倩, 赵磊静. 能量异构的无线传感器网络加权无标度拓扑研究. 物理学报, 2014, 63(15): 150504. doi: 10.7498/aps.63.150504
    [11] 尹荣荣, 刘彬, 刘浩然, 李雅倩. 无线传感器网络中无标度拓扑的动态容错性分析. 物理学报, 2014, 63(11): 110205. doi: 10.7498/aps.63.110205
    [12] 宋佳, 罗清华, 彭喜元. 基于节点健康度的无线传感器网络冗余通路控制方法. 物理学报, 2014, 63(12): 128401. doi: 10.7498/aps.63.128401
    [13] 黄锦旺, 冯久超, 吕善翔. 混沌信号在无线传感器网络中的盲分离. 物理学报, 2014, 63(5): 050502. doi: 10.7498/aps.63.050502
    [14] 刘浩然, 尹文晓, 韩涛, 董明如. 一种优化无线传感器网络生命周期的容错拓扑研究. 物理学报, 2014, 63(4): 040509. doi: 10.7498/aps.63.040509
    [15] 刘向丽, 李赞, 胡易俗. 无线传感网中基于质心的高效坐标压缩算法. 物理学报, 2013, 62(7): 070201. doi: 10.7498/aps.62.070201
    [16] 祁浩, 王福豹, 邓宏. 基于无线传感器网络的地震信号特征提取方法研究. 物理学报, 2013, 62(10): 104301. doi: 10.7498/aps.62.104301
    [17] 王翥, 王祁, 魏德宝, 王玲. 无线传感器网络中继节点布居算法的研究. 物理学报, 2012, 61(12): 120505. doi: 10.7498/aps.61.120505
    [18] 王亚奇, 杨晓元. 一种无线传感器网络簇间拓扑演化模型及其免疫研究. 物理学报, 2012, 61(9): 090202. doi: 10.7498/aps.61.090202
    [19] 佟晓筠, 左科, 王翥. 基于无线传感器网络的混合混沌新分组加密算法. 物理学报, 2012, 61(3): 030502. doi: 10.7498/aps.61.030502
    [20] 周杰, 刘元安, 吴帆, 张洪光, 俎云霄. 基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配. 物理学报, 2011, 60(9): 090504. doi: 10.7498/aps.60.090504
计量
  • 文章访问数:  3345
  • PDF下载量:  439
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-12-13
  • 修回日期:  2014-01-20
  • 刊出日期:  2014-05-05

基于动态最小生成树路由协议的数据聚融算法

  • 1. 东北大学信息科学与工程学院, 沈阳 110819
    基金项目: 国家科技支撑计划(批准号:2012BAH82F04)、国家自然科学基金(批准号:61101121)和国家高技术研究发展计划(863计划)(批准号:2013AA102505)资助的课题.

摘要: 大规模的地理环境监测,以及用来传输与处理数据的物理基础设施无法和监测区域的规模保持同样的增速,使得不可靠链路下的数据采集与处理呈现出一种饱和流状态,无线传感器网络的能力看似难以稳定. 另一方面,尽管理想网络模型的计算结果足够精确,然而,由于与实际应用偏差甚大,使得网络用户无法充分地分析和利用从工业现场所获得到的网络感知数据,并且没有针对网络规模和性能对数据聚融的影响进行分析. 为此,本文提出以”过渡区“作为工业现场仿真的假设条件,并在此基础上提出了一种面向实际应用的数据聚融算法,即基于可信度的数据聚融算法(R算法). 在具体设计R算法过程中,选用聚集和操作符SUM为例,通过对网络提供出的近似聚集和加以自动分析、综合,针对相对误差界限ε,计算出近似聚集和的可信度的下限η;并将近似聚集和、参数η 一同提供给用户,在为用户提供网络概要信息的同时,还提供了参数η作为对信息可信度的判断,以便指导用户对数据聚集结果进行深度处理和提高网络的感知性能. 仿真实验描述了过渡区内由于信噪比导致的链路不可靠所引起的η的变化规律;讨论了网络性能和规模对η的影响,随着网络运行周期的增加和网络规模的增大,η的值将逐渐靠近0;从而为WSNs从理论模型投入实际工业应用提供了理论依据和经验公式.

English Abstract

参考文献 (34)

目录

    /

    返回文章
    返回