大规模无线传感器网络异构数据交换方法仿真
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摘要
为解决当前网络数据交换方法存在的安全性差和能耗高问题,提出基于OCHS的大规模无线传感器网络异构数据交换方法。根据传感器中存在的时间关联性产生候选异常数据点,依据空间关联性过滤候选异常数据点,获取局部异常数据点。将传感器中全部局部异常数据点集合融合至汇聚节点,得到全局异常数据点,引入卡尔曼滤波器过滤掉所有异常数据点,完成数据交换前预处理。利用OCHS算法选取数据交换簇首节点时,结合数据预处理结果,在考虑节点数据存在的周期性基础上,依据各异构节点的初始能量和传感器节点分布密度等因素,通过竞争的方式选择最优交换簇首集合,即最佳数据交换簇集合。传感器网络中各待交换异构数据根据勾股定理选取能耗最小的路径加入各数据簇,完成数据交换。实验结果表明,所提方法安全系数高、数据交换能耗低,整体性能优于当前相关方法。该方法具有较强地实用性和鲁棒性,可为数据处理提供一定理论依据。
At present, exchange method of network data has defects, such as poor security and higher energy consumption. In order to solve the defects, this research proposes a new exchange method of heterogeneous data of large-scale wireless sensor networks based on OCHS. Firstly, the research generated candidate abnormal data point according to time relevance existing in the sensor, then according to the relevance filtered the abnormal data points and acquired local abnormal data points. The research fused set of all local abnormal data points in the sensor into sink node and obtained global abnormal data points. The research introduced Kalman filter to filter all abnormal data points and completed pretreatment before data exchange. Integrated with results of the pretreatment, according to primary energy of each heterogeneous node and distribution density of sensor node, the research selected set of optimal exchange cluster head, namely set of optimal data exchange cluster, via competition manner based on periodicity of node data during selecting cluster head node of data exchange via OCHS algorithm. According to the Pythagorean Theorem, the research selected path with the smallest energy consumption in each heterogeneous data to exchange to add into each date cluster. Thus, we completed the data exchange. Simulation results show that the proposed method has high security coefficient and low energy consumption of data exchange. Its overall performance is superior to current methods. The proposed method has better practicability and robustness.
At present, exchange method of network data has defects, such as poor security and higher energy consumption. In order to solve the defects, this research proposes a new exchange method of heterogeneous data of large-scale wireless sensor networks based on OCHS. Firstly, the research generated candidate abnormal data point according to time relevance existing in the sensor, then according to the relevance filtered the abnormal data points and acquired local abnormal data points. The research fused set of all local abnormal data points in the sensor into sink node and obtained global abnormal data points. The research introduced Kalman filter to filter all abnormal data points and completed pretreatment before data exchange. Integrated with results of the pretreatment, according to primary energy of each heterogeneous node and distribution density of sensor node, the research selected set of optimal exchange cluster head, namely set of optimal data exchange cluster, via competition manner based on periodicity of node data during selecting cluster head node of data exchange via OCHS algorithm. According to the Pythagorean Theorem, the research selected path with the smallest energy consumption in each heterogeneous data to exchange to add into each date cluster. Thus, we completed the data exchange. Simulation results show that the proposed method has high security coefficient and low energy consumption of data exchange. Its overall performance is superior to current methods. The proposed method has better practicability and robustness.
引文
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[2] 谢婧,刘东,陆一鸣,等.基于异构模型映射的配电网信息交互方法[J].电网技术,2016,40(11):3559-3565.
[3] 徐斌,李琳,钟珞.基于中间件的政务信息管理与共享交换平台研究[J].武汉理工大学学报(信息与管理工程版),2017,39(3):334-337.
[4] 刘婧.基于元数据的多源异构海洋情报数据交互共享研究[J].情报杂志,2016,35(9):168-173.
[5] 田野,袁博,李廷力.物联网海量异构数据存储与共享策略研究[J].电子学报,2016,44(2):247-257.
[6] 于涤非,黄海宁,张春华,等.三维成像声纳数据采集系统设计[J].现代电子技术,2018,41(3):41-45.
[7] 文孟飞,刘伟荣,胡超.网络媒体大数据流异构多模态目标识别策略[J].计算机研究与发展,2017,54(1):71-79.
[8] 杨明霞,王万良,马晨明.面向节能和容错的异构WSNs数据收集算法[J].传感技术学报,2016,29(6):934-940.
[9] 李浩,赵国堂,范丁元,等.面向GIS应用的铁路工程三维信息模型数据交换方法[J].西南交通大学学报,2018,53(1):197-205.
[10] 王鸿亮,廉东本,徐久强.基于FMI的分布式联合仿真技术研究[J].计算机仿真,2017,34(4):256-261.