CN101415011A - 无线传感器网络中安全有效的数据聚合方法 - Google Patents
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Abstract
无线传感器网络中安全有效的数据聚合方法属于数据聚合领域。现有方法仅考虑能量有效性问题,并不能过滤掉异常的错误数据和恶意的攻击数据,无法保证有效性和安全性。本发明首先在智能分组阶段将整个传感器网络划分成若干个组,保证每个组内的传感器节点在物理空间上位置邻近而且传感器所采集到的数据值接近;其次,在数据传输及识别过滤阶段,组内的各个传感器节点将采集到的数据发送到本组的组长节点,组长节点通过执行异常数据识别算法过滤错误或者虚假的数据信息;最后,组长节点根据具体的传感器网络应用需求聚合过滤后的数据,并将其传输至网关节点。本方法保证有效和安全的识别并过滤错误虚假数据信息。
Description
技术领域
本发明涉及一种无线传感器网络的数据聚合方法,具体是一种基于分组结构的无线传感器网络数据聚合方法,可以用于无线传感器网络中安全有效的聚合各个传感器节点采集到的数据。
背景技术
无线传感器网络广泛应用于军事侦察、智能建筑、环境监控以及工业控制等各个领域中。首先,由于传感器节点本身使用无线链路进行数据传输,传输信号容易受到干扰而导致接收到错误数据,甚至丢失数据包。其次,传感器节点上的部分传感器件也会因为故障或损坏等原因而采集到错误的数据。最后,由于无线传感器网络部署区域的物理安全性很难保证,这使得传感器节点很容易被捕获,进而导致向无线传感器网络中注入恶意的虚假数据信息。这些异常的错误数据和恶意的攻击数据都会导致无线传感器网络中数据聚合时产生错误的聚合结果,从而对无线传感器网络的正常应用造成巨大的影响,甚至产生不可预测的严重后果。
目前现有的无线传感器网络数据聚合方法仅考虑能量有效性问题,并不能过滤掉异常的错误数据和恶意的攻击数据,进而无法保证无线传感器网络数据聚合时的有效性和安全性。
发明内容
本发明的目的在于提供一种无线传感器网络中安全有效的数据聚合方法。使用该方法即可以有效的识别和过滤由器件故障或传输干扰等原因而产生的错误数据,也可以安全的识别和过滤无线传感器网络所面临的各种恶意攻击方式而产生的虚假数据信息。而且,即使是在因网络干扰或故障等原因而导致部分采集数据缺失的情况下,本方法仍然能保证有效和安全的识别并过滤错误和虚假的数据信息。
为实现上述目的,本发明采取了以下技术方案。整个方案包括智能分组、数据传输及识别过滤、数据聚合三个阶段,如图1所示。首先,在智能分组阶段将整个传感器网络划分成若干个组,保证每个组内的传感器节点在物理空间上位置临近而且传感器所采集到的数据值接近(不超过预设的门限值)。其次,在数据传输及识别过滤阶段,组内的各个传感器节点将采集到的数据发送到本组的组长节点,组长节点通过执行异常数据识别算法过滤错误或者虚假的数据信息。最后,组长节点根据具体的传感器网络应用需求聚合过滤后的数据,并将其传输至网关节点。
智能分组阶段如图2所示,包括以下具体步骤:
1)每个传感器节点利用随机数生成函数生成随机延迟时间;
2)开始延迟;
3)如果上述传感器节点在延迟期间收到其他传感器节点发送来的加入组信息,则暂停延迟并保存剩余延迟时间,否则执行步骤7);
4)此传感器节点判断是否满足加入组的条件;即利用从加入组消息中获得的组内采集数据最大值、最小值、平均值以及此传感器节点采集到的数据值重新计算此节点加入组后采集数据的最大值、最小值、平均值;如果新计算的采集数据最大值、最小值与平均值之差的绝对值均小于预设门限值并且此传感器节点与组中组长节点的距离小于预设的最大跳数值,则此传感器节点加入本组,否则此传感器节点不加入本组;
5)如果步骤4)中加入组条件不满足,则此传感器节点恢复延迟时间,跳转至步骤2)继续延迟;否则,此传感器节点加入本组,修改其父节点为所接收到的加入组消息中的源节点;更新组内采集数据的最大值、最小值、平均值和节点个数;
6)此传感器节点利用更新后的采集数据最大值、最小值、平均值、节点个数填充广播数据包中的各个域,并广播更新后的加入组数据包;
7)此传感器节点选择自己为本组组长,用自己采集到的数据填充广播数据包中的采集数据最大值、最小值、平均值域,并广播此加入组数据包。
数据传输及识别过滤阶段如图3所示,具体包括以下步骤:
a)组内成员传感器节点将采集到的数据发送给组长传感器节点;
b)组长传感器节点接收组内各传感器节点发送来的采集数据;
c)组长传感器节点运行稳健的多维统计方法计算由组内各传感器节点所采集数据构成的多维矩阵的均值和协方差,如OGK(OrthogonalizedGnanadesikan-Kettenring)方法、MCD(Minimum Covariance Determinant)方法、FMCD(Fast Minimum Covariance Determinant)方法、MBA(MedianBall Algorithm)方法等;
d)组长传感器节点利用步骤c)中计算得到的均值和协方差计算组内各成员传感器节点所采集数据的马氏距离;
e)如果步骤d)中计算出的马氏距离大于自由度为组内节点个数的卡方分布值,则此成员传感器节点所传输的采集数据为异常数据,组长传感器节点将其过滤;否则,此成员传感器节点所传输的采集数据为正常数据,组长传感器节点接受此数据以用于数据聚合。
数据聚合阶段将根据具体的传感器网络应用需求聚合过滤后的采集数据,并将其传输至网关节点。这些数据中不再包含错误或虚假的数据信息,从而可以保证最终用户获得安全准确的数据信息。
附图说明
图1本发明整体方案
图2本发明智能分组阶段
图3本发明数据传输及识别过滤阶段
具体实施方式
本发明在具体实施时包括智能分组、数据传输及识别过滤、数据聚合三个阶段。本实施例中的部分数据来源于部署在英特尔伯克利实验室办公区的54个Mica2Dot传感器节点于2004年2月28日至4月5日所采集的数据,其中包括温度(单位:摄氏度)、相对湿度(单位:%)、光照(单位:每平方米流明)数据。
首先,执行智能分组,按照以下具体步骤实施:
1)每个传感器节点利用随机数生成函数RandomC.rand()生成一个随机数,乘以组内节点间平均消息传递时间得到此节点的随机延迟时间T。
2)传感器节点开始延迟。
3)如果上述传感器节点在延迟期间内接收到其他传感器节点发送来的加入组信息,则暂停延迟并保存剩余延迟时间T′,否则执行步骤(7)。
4)此传感器节点判断是否满足加入组的条件。利用从加入组消息中获得的组内采集数据最大值fmax、最小值fmin、平均值favg、节点个数n以及此传感器节点采集到的数据值f重新计算此节点加入组后采集数据的最大值最小值平均值即 如果新计算的采集数据最大值、最小值与平均值之差的绝对值均小于预设门限值δ,即 并且此传感器节点与组中组长节点的距离小于预设的最大跳数值h,则此传感器节点加入本组,否则此传感器节点不加入本组。实施例中δ=(2,5,600),分别对应于温度、湿度、光照的门限值,最大跳数值h取值为3。
5)如果步骤(4)中加入组条件不满足,则此传感器节点恢复延迟时间T′,跳转至步骤(2)继续延迟。否则,此传感器节点加入本组,修改其父节点为所接收到的加入组消息中的源节点。更新组内采集数据的最大值、最小值、平均值以及节点个数分别为和n+1。
7)此传感器节点选择自己为本组组长,用自己采集到的数据f填充广播数据包中的采集数据最大值、最小值、平均值域,即fmax=f,fmin=f,favg=f,节点个数n=1,并广播此加入组数据包。
智能分组阶段执行后,整个传感器网络被划分为8个组,分别为:
组序号 | 组长节点 | 组内成员节点 |
1 | 51 | 49 50 52 |
2 | 45 | 43 44 46 47 48 |
3 | 40 | 36 37 38 39 41 42 |
4 | 7 | 2 4 5 6 8 9 10 53 54 |
5 | 33 | 1 3 32 34 35 |
6 | 31 | 26 27 28 29 30 |
7 | 14 | 11 12 13 15 16 17 18 19 |
8 | 22 | 20 21 23 24 25 |
其次,执行数据传输及识别过滤,按照以下步骤实施。本实施例中以组4为例,组内各传感器节点所采集的数据分别如下表所示:
1)组内各成员传感器节点2,4,5,6,8,9,10,53,54将采集到的数据发送给组长传感器节点7。
2)组长传感器节点7接收组内各传感器节点发送来的采集数据。
3)组长传感器节点7运行稳健的多维统计方法计算由组内各传感器节点所采集数据构成的多维矩阵的均值μ和协方差∑,本实施例中选择OGK方法,得到均值μ和协方差∑分别为:
4)组长传感器节点利用步骤(3)中计算得到的均值和协方差计算组内各成员传感器节点所采集数据的马氏距离d=(f-μ)T∑(f-μ)。得到组中各传感器节点所采集数据的马氏距离分别如下表所示:
节点 | 2 | 4 | 5 | 6 | 7 |
马氏距离 | 2.422587 | 2.974098 | 3.281500 | 39730.351213 | 3.159323 |
节点 | 8 | 9 | 10 | 53 | 54 |
马氏距离 | 3.674534 | 3.261434 | 3.044650 | 1.713988 | 3.467885 |
5)如果步骤(4)中计算出的马氏距离大于自由度为组内节点个数的卡方分布值,即 则此成员传感器节点所传输的采集数据为异常数据,组长传感器节点将其过滤。否则,此成员传感器节点所传输的采集数据为正常数据,组长传感器节点接受此数据以用于数据聚合。实施例中卡方分布值 其中自由度n=10,精度α=0.95(α的取值范围一般为0.95~0.99)。组内成员传感器节点中节点6所采集数据计算出的马氏距离39730.351213>3.940299,故节点6所采集的数据被判定为异常数据,其值将被过滤。其余传感器节点所采集数据计算出的马氏距离均小于卡方分布值3.940299,故这些数据都属于正常数据,他们都将被传递到数据聚合阶段进行聚合。
最后,根据具体的传感器网络应用需求执行数据聚合,所聚合的数据范围为识别和过滤后的采集数据,并将其传输至网关节点。实施例中所采用的数据聚合方式为计算所采集数据的最大值、最小值、平均值。因此最终的聚合结果如下表所示:
温度 | 湿度 | 光照 | |
最大值 | 18.6756 | 38.6107 | 100.5200 |
最小值 | 18.0774 | 39.2107 | 103.1100 |
平均值 | 18.3645 | 38.9141 | 102.1256 |
数据聚合后,组长节点7将聚合结果传输至网关节点。因为这些识别和过滤后的采集数据中不再包含有错误或者虚假的数据信息,从而可以保证最终用户获得安全准确的数据信息。
Claims (1)
1.一种无线传感器网络中安全有效的数据聚合方法,其特征在于:包括智能分组、数据传输及识别过滤、数据聚合三个阶段;首先,在智能分组阶段将整个传感器网络划分成若干个组,保证每个组内的传感器节点在物理空间上位置临近而且传感器所采集到的数据值接近;其次,在数据传输及识别过滤阶段,组内的各个传感器节点将采集到的数据发送到本组的组长节点,组长节点通过执行异常数据识别算法过滤错误或者虚假的数据信息;最后,组长节点根据具体的传感器网络应用需求聚合过滤后的数据,并将其传输至网关节点;
智能分组阶段,包括以下具体步骤:
1)每个传感器节点利用随机数生成函数生成随机延迟时间;
2)开始延迟;
3)如果上述传感器节点在延迟期间收到其他传感器节点发送来的加入组信息,则暂停延迟并保存剩余延迟时间,否则执行步骤7);
4)此传感器节点判断是否满足加入组的条件;即利用从加入组消息中获得的组内采集数据最大值、最小值、平均值以及此传感器节点采集到的数据值重新计算此节点加入组后采集数据的最大值、最小值、平均值;如果新计算的采集数据最大值、最小值与平均值之差的绝对值均小于预设门限值并且此传感器节点与组中组长节点的距离小于预设的最大跳数值,则此传感器节点加入本组,否则此传感器节点不加入本组;
5)如果步骤4)中加入组条件不满足,则此传感器节点恢复延迟时间,跳转至步骤2)继续延迟;否则,此传感器节点加入本组,修改其父节点为所接收到的加入组消息中的源节点;更新组内采集数据的最大值、最小值、平均值和节点个数;
6)此传感器节点利用更新后的采集数据最大值、最小值、平均值、节点个数填充广播数据包中的各个域,并广播更新后的加入组数据包;
7)此传感器节点选择自己为本组组长,用自己采集到的数据填充广播数据包中的采集数据最大值、最小值、平均值域,并广播此加入组数据包;
数据传输及识别过滤阶段,具体包括以下步骤:
a)组内成员传感器节点将采集到的数据发送给组长传感器节点;
b)组长传感器节点接收组内各传感器节点发送来的采集数据;
c)组长传感器节点运行多维统计方法计算由组内各传感器节点所采集数据构成的多维矩阵的均值和协方差;
d)组长传感器节点利用步骤c)中计算得到的均值和协方差计算组内各成员传感器节点所采集数据的马氏距离;
e)如果步骤d)中计算出的马氏距离大于自由度为组内节点个数的卡方分布值,则此成员传感器节点所传输的采集数据为异常数据,组长传感器节点将其过滤;否则,此成员传感器节点所传输的采集数据为正常数据,组长传感器节点接受此数据以用于数据聚合。
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