CN106162720B - A Node Stability Evaluation Method for Cognitive Wireless Self-Organizing Networks Based on Multi-attribute Decision Making - Google Patents

A Node Stability Evaluation Method for Cognitive Wireless Self-Organizing Networks Based on Multi-attribute Decision Making Download PDF

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CN106162720B
CN106162720B CN201610446646.1A CN201610446646A CN106162720B CN 106162720 B CN106162720 B CN 106162720B CN 201610446646 A CN201610446646 A CN 201610446646A CN 106162720 B CN106162720 B CN 106162720B
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白跃彬
王炜涛
冯鹏
程琨
顾育豪
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Beihang University
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Abstract

A cognitive wireless self-organizing network node stability assessment method based on multi-attribute decision-making. The method aims to improve the usability of the cognitive wireless self-organizing network node, and solves the problem of judging the stability of the cognitive wireless self-organizing network node on the basis of a node stability evaluation system. And according to the characteristic data collected by the nodes, counting and calculating the index attribute parameters related to the stability, the stability of the available frequency spectrum and the interference degree of the neighbor nodes. These parameters are converted into attribute vectors by a normalization method. And evaluating the influence degree of each index influencing the node stability, and calculating the weight of each index in the node stability evaluation system. The node stability evaluation problem is converted into a multi-attribute decision problem, and the current stability state of the node is judged by combining the prior knowledge based on sample sampling through a positive and negative ideal solution method. The invention introduces a node stability evaluation system model, a fuzzy analytic hierarchy process and a positive and negative ideal solution method, so that the calculation result is suitable for the fields of routing, clustering, distribution and the like.

Description

Cognitive wireless self-organizing network node stability evaluation method based on multi-attribute decision
Technical Field
The invention relates to the field of node state evaluation of cognitive wireless networks, in particular to a node state evaluation method based on multi-attribute decision.
Background
The cognitive radio self-organizing network is a self-organizing network based on a cognitive radio technology. Each Cognitive User (CU) finds and utilizes spectrum holes through Cognitive radio devices. After detecting a possible transmission opportunity (spectrum hole), the CU transmits data to a receiving side through Cognitive Radio (Cognitive Radio) by performing dynamic spectrum access. If a Primary User (PU) suddenly appears, the CU has to immediately backoff. Since the dynamic change of the available frequency spectrum of the cognitive wireless self-organizing network node and the dynamic characteristics of the topology have great influence on the network availability, the efficient and reliable node stability evaluation method is particularly important for the cognitive wireless self-organizing network with poor network reliability, unstable links and dynamic change of the available frequency spectrum.
In recent years, researchers have conducted research from a number of aspects to solve the problem of evaluating node stability in a cognitive radio ad hoc network environment, and the results obtained mainly include:
(1) method for predicting link holding time based on movement of node
When this method is used, the node needs to obtain the position, speed and moving direction of itself and the neighboring nodes. According to the position and the moving track of the node, the link available time of the two nodes is calculated by a geometric method in combination with the moving characteristics of the node, so that the stability degree of the node and the adjacent nodes can be calculated. The method is mainly used in the network form which can be positioned at any time and has definite moving direction.
(2) Node stability evaluation method based on node signal interference
When the method is adopted, the node records the intra-flow interference and inter-flow interference values of the links with the adjacent node, and carries out weighted accumulation on the interference values of each pair of links. And predicting the total interference value of the current node according to the interference values of each pair of adjacent links, so that the stability degree of the node can be calculated. The method is mainly used in the multi-hop and multi-stream cognitive wireless self-organizing network.
Depending on the required information, (1) can be generalized to a method requiring node location information, and (2) belongs to a method not requiring node location information. In the method (1), under the distributed environment of the cognitive wireless self-organizing network, the position and the speed of a target node are generally difficult to obtain, and the influence on the stability of the node is not only caused by topology change; the method (2) currently has relatively rough interference prediction in most scenes and has poor timeliness. The factors all restrict the accuracy of the node stability evaluation method in the cognitive wireless self-organizing network in practical application.
In various cognitive wireless self-organizing network environments, factor node factors influencing node stability are mined, and then all the factors are analyzed and classified, so that effectiveness and accuracy of node stability prediction are improved, and usability of a cognitive wireless self-organizing network is further improved. Therefore, the induction and the analysis of the influence degree on the stability of the influenced nodes have very important significance for improving the usability of the cognitive wireless self-organizing network.
Disclosure of Invention
The method aims to improve the node cognitive wireless self-organizing network availability, and solves the problem of node stability prediction of the cognitive wireless self-organizing network by combining a multi-attribute decision analysis method on the basis of factors influencing node stability. The method specifically comprises the following steps:
1. the node stability is influenced by a plurality of uncertain factors such as node movement, available frequency spectrum change, electromagnetic interference and the like, a node stability index evaluation system is established, and the influence degree of each factor on the node stability is analyzed.
2. Based on a node stability index evaluation system, a node stability evaluation problem is converted into a multi-attribute decision problem, so that the stability attribute of the cognitive wireless self-organizing network node is judged. In order to enable the calculation result to be fast and accurate and enable the result to be suitable for the fields of routing, clustering, distribution and the like, the invention introduces a cross-layer sharing mechanism and a positive and negative ideal solution method.
Compared with the prior art, the invention has the innovation points that: based on a node stability evaluation system, the method is suitable for different cognitive wireless self-organizing network forms and has self-adaptive capacity. The concrete expression is as follows:
1. based on the node stability evaluation system, in various environments of the cognitive wireless self-organizing network, each node can record attribute information of influencing factors in the evaluation system in a distributed manner, and the required information is easy to obtain compared with other methods.
2. In the using process of the attribute information of the influencing factors, relevant parameters of the network environment are adjusted in different network environments based on the prior knowledge of the nodes, so that the method has self-adaptive capacity.
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FIG. 1 shows a process of a node stability evaluation method
FIG. 2 is a schematic diagram of a node stability index evaluation system
FIG. 3 is a schematic diagram of a positive and negative ideal solution
Detailed Description
Referring to fig. 1, a node collects factor attribute information in a rule layer of a node stability index evaluation system (see fig. 2) through a cross-layer cooperative interaction mechanism, and generates a fuzzy evaluation decision matrix from the parameters; and converting the fuzzy evaluation decision matrix into a standard fuzzy decision matrix through the normalization of the attribute values of the decision indexes, and giving the standard value of each characteristic attribute of the current state of the node. And then, distributing the weight of each characteristic attribute, and determining the influence degree of each influence factor on the stable state of the node. And converting the node stability evaluation problem into a multi-attribute decision problem based on the sampling data as a standard matrix, and finally judging the stable state of the node by a positive and negative ideal solution method. The invention introduces a node stability evaluation system model, a positive and negative ideal solution method and a fuzzy analytic hierarchy process, so that the calculation result is suitable for the fields of routing, clustering, distribution and the like, and the actual implementation mainly comprises three stages.
Firstly, establishing an attribute vector based on a node stability evaluation system.
By analyzing the node stability factor of the cognitive wireless self-organizing network environment, a node stability evaluation system can be constructed, as shown in fig. 2. As can be seen from the figure, the factors influencing the node stability are summarized into 3 aspects, 6 indexes. The 3 aspects mean that: neighbor node stability, available spectrum stability, and interference level. The 6 indexes are: the number of one-hop neighbor nodes, the link stability with one-hop neighbor nodes, the number of available channels with one-hop neighbor nodes, the available channel variation activity statistics, the channel quality with one-hop neighbor nodes and the number of interfered channels. In order to construct and reflect the node stability state, index original data are required to be collected, wherein indexes reflecting available spectrum stability and interference degree are required to be obtained in a cross-layer cooperation mode, then calculation and normalization processing are carried out on the index data, and finally an attribute matrix is obtained.
The index data acquisition and calculation refers to acquiring the current 6 index data information of the nodes, wherein some indexes need to be obtained by directly acquiring original data, and some indexes need to further calculate the original data. Specifically, index 1 is directly obtained through a network layer topology management mechanism, index 3 is obtained through a MAC layer related protocol, and index 4 is a link interruption frequency, and can also be obtained through the MAC layer related protocol. Indexes 2 and 5 are calculated by using results of predecessors, and index 6 is the number of channels with the current available channel bit error rate of each neighbor node higher than a certain threshold value. In addition, after the indexes 2,3 and 5 calculate the index values of the neighbor nodes, the indexes are accumulated firstly and then averaged to finally obtain the final index values of the indexes 2,3 and 5.
The calculation formula of the link stability of the index 2 and the one-hop neighbor node is as follows:
Figure GDA0003034066620000031
wherein T isijRepresents the time that node i and node j are expected to hold, and E (T) represents the expected value of the time that node i and node j hold.
The calculation formula of the index 5 and the channel quality of each neighbor node is as follows:
Figure GDA0003034066620000032
when p isloss，b＝0，Tf，b1 and BiWhen it is the applicable frequency band of node i, maxThr(i，j)，b＝Thr(i，j)，b. Weight value alphab(αb≦ 1) reflects different spectral characteristics (interference level of adjacent bands, channel error rate, path loss).
Since some index values are as large as possible (i.e., the larger the index value is, the more stable the node is), and some index values are as small as possible, it is necessary to normalize the index values. For larger and better indicators (indicators 1,2,3 and 5), the normalization formula is:
Figure GDA0003034066620000041
for indexes 4 and 6, the normalization formula is that the index value is larger and worse (i.e. the index value is larger and the node is more unstable), the normalization formula is:
Figure GDA0003034066620000042
after normalization, a 6-dimensional attribute vector is finally obtained
Figure GDA0003034066620000043
Second, a decision index attribute weight assignment is determined.
The decision index attribute weight distribution means that the weight of the index in a node stability evaluation system is determined according to the influence of the index attribute on the node stability. Specifically, the weights of the 6 indexes in the target layer to criterion layer model of fig. 2 are determined. The general method is to obtain the weight distribution of each layer of indexes first, and then obtain the weight of the lowest layer (i.e. 6 indexes) according to the weight of the upper layer.
The weight division of each layer of indexes refers to the weight distribution calculation of each layer of indexes of the criterion layer, and the weight distribution calculation comprises the following steps:
1) a blur matrix is calculated. When all indexes of the k layer of the criterion layer are compared pairwise, the indexes are converted by adopting a language conversion rule of triangular fuzzy numbers, and the triangular fuzzy numbers are mijIs the median enclosed area, mijIs a 1-9 scale value in an analytic hierarchy process, as shown in table 1 below. Obtaining fuzzy number a for comparing all indexes of k layers pairwiseij＝(lij，mij，hij) If the number of the judgment experts is t, the final triangular fuzzy number of the k-th layer index i to the index j is
Figure GDA0003034066620000044
Assuming that the k-th layer has h indexes, one h is obtainedk×hkIs fuzzy matrix of
Figure GDA0003034066620000045
It should be noted that the matrix has reciprocal properties, i.e. it is a matrix with a reciprocal property
Figure GDA0003034066620000046
TABLE 1 Scale comparison Table for attribute relative importance comparison 1 ~ 9
Figure GDA0003034066620000047
2) And calculating a fuzzy metric value of the index. According to the fuzzy measurement matrix obtained in the previous step, the ith index is integrated with the comprehensive fuzzy measurement value of all other factors of the same layer k
Figure GDA0003034066620000051
The calculation formula of (a) is as follows:
Figure GDA0003034066620000052
3) calculating the weight distribution of the ith index of the kth layer according to the following formula
Figure GDA0003034066620000053
And j is 1,2kTherein of
Figure GDA0003034066620000054
Is calculated in a manner that
Figure GDA0003034066620000055
Thus, the weight of the k-th layer index is assigned as:
Figure GDA0003034066620000056
4) and calculating the weight distribution of the lowest indexes of the criterion layer. Because the standard layer of the scheme is two layers, the first layer has three indexes, and the second layer has six indexes, according to the first 3 steps, the index weight vector can be respectively obtained
Figure GDA0003034066620000057
And
Figure GDA0003034066620000058
thus, the index attribute weight is assigned as:
Figure GDA0003034066620000059
third, node stability state decision computation.
After the weight distribution is carried out on the node stability evaluation system indexes according to the expert scores, the node stability state calculation is needed. The node stability state calculation is divided into 2 steps. Firstly, sampling is carried out according to the constructed attribute vector to obtain the attribute vector when the node is unstable, sub-stable and stable:
Figure GDA00030340666200000510
these sample state attribute vectors are then saved as knowledge in a sample repository. Secondly, according to the attribute vector currently constructed by the node
Figure GDA00030340666200000511
Computing
Figure GDA00030340666200000512
And finding out the sample state corresponding to the highest value of the fitting similarity as the current state of the node according to the fitting similarity of the attribute vectors of the sample states. The flow of the calculation method of the fitting similarity is as follows (refer to the description of the attached figure 3):
1) and constructing a decision space.
If the number of indexes of the node stability evaluation system is n and the number of sample state attribute vectors is m, an n-dimensional space S is constructednMapping each decision state i to SnA point in space characterizing a sample state i at SnAnd refer to these points as spatial points of the positive ideal solution
Figure GDA0003034066620000061
In order to avoid the situation that the similarity of the state fitting cannot be judged when the distances between the evaluation state and the positive ideal solutions of the states are equal, an n-dimensional space S is set for each positive ideal nodenNegative ideal solution of. The positive and negative ideal space points are virtual, and each index attribute of the negative ideal space point is the worst state of the corresponding positive ideal solution space point.
2) And calculating the fitting similarity.
And calculating the fitting similarity by respectively measuring the distances between the current attribute vector to be evaluated and each positive and negative ideal point in an n-dimensional space so as to measure the fitting similarity between the current attribute vector to be evaluated and each sample state.
The current attribute vector l to be evaluatedtPositive ideal solution space point with sample state i
Figure GDA0003034066620000062
The distance calculation formula of (c) is as follows:
Figure GDA0003034066620000063
wherein
Figure GDA0003034066620000064
Representing positive ideal solution space points
Figure GDA0003034066620000065
Is at SnIs measured.
Current attribute vector to be evaluated ItNegative ideal solution space point with sample state i
Figure GDA0003034066620000066
The distance calculation formula of (c) is as follows:
Figure GDA0003034066620000067
then the fitting similarity between the current attribute vector to be evaluated and the sample state i is:
Figure GDA0003034066620000068
3) and calculating the stability state of the current node.
Because the number of the node stability evaluation system indexes is 6 and the number of the sample state attribute vectors is 3, the current attribute vector I to be evaluated can be calculated according to the first step and the second steptSimilarity of fit C to each sample state ili(i ═ 1,2, 3), then the current node stability state is:
j＝{i|Cliis max} (14)。

Claims (1)

1.一种基于多属性决策的认知无线自组织网络节点稳定性评估方法，包含节点稳定性评价体系构建、基于模糊层次分析法的指标权重计算和基于正负理想解方法的节点稳定性判决计算，其特征在于：1. A multi-attribute decision-based node stability evaluation method for cognitive wireless ad hoc networks, including node stability evaluation system construction, index weight calculation based on fuzzy analytic hierarchy process, and node stability judgment based on positive and negative ideal solution methods calculation, which is characterized by: 1)各个节点周期性的收集当前局部认知无线自组织网络环境中与各个指标有关的参数，网络层通过跨层交互机制对这些参数获取和分析，构建节点稳定性指标评价体系，分析各个因素对节点稳定性的影响程度；节点稳定性指标评价体系分为目标层、准则层和决策方案层，其中目标层为节点稳定性评价；准则层包括2层，第一层包括邻居节点稳定性，可用频谱稳定性和干扰程度；第二层包括6个具体评价指标，分别为一跳邻居节点个数，与一跳邻居节点的链路稳定性，与一跳邻居节点的可用信道数，当前节点的可用信道变化活动统计，与一跳邻居节点的链路质量，当前节点受到干扰的信道统计；决策方案层包括三个决策，分别为稳定，次稳定和不稳定三类；1) Each node periodically collects the parameters related to each index in the current local cognitive wireless self-organizing network environment. The network layer obtains and analyzes these parameters through the cross-layer interaction mechanism, constructs a node stability index evaluation system, and analyzes each factor The degree of influence on the node stability; the node stability index evaluation system is divided into the target layer, the criterion layer and the decision plan layer, of which the target layer is the node stability evaluation; the criterion layer includes two layers, the first layer includes the neighbor node stability, Available spectrum stability and interference level; the second layer includes 6 specific evaluation indicators, namely the number of one-hop neighbor nodes, the link stability of one-hop neighbor nodes, the number of available channels and the number of one-hop neighbor nodes, and the current node. The available channel change activity statistics, the link quality with one-hop neighbor nodes, and the channel statistics of the current node being interfered with; the decision-making scheme layer includes three decisions, namely stable, sub-stable and unstable; 2)根据节点稳定性评价体系，采取如下步骤计算准则层各个指标的权重分配：2) According to the node stability evaluation system, the following steps are taken to calculate the weight distribution of each index of the criterion layer: a)采用三角模糊数，对准则层第k层所有指标进行两两比较,得到模糊矩阵A；其计算方式如下：三角模糊数以mij为中值的封闭区间，而mij是层次分析法中的1～9标度值；得到对k层所有指标两两比较的模糊数aij＝(lij，mij，hij)，假设评判专家的人数为t，则第k层指标i对指标j的最终的三角模糊数为
Figure FDA0003034066610000011
假设第k层共有h个指标，则得到一个hk×hk的模糊矩阵
Figure FDA0003034066610000012
a) Using triangular fuzzy numbers, compare all the indicators of the k-th layer of the criterion layer pairwise, and obtain the fuzzy matrix A; the calculation method is as follows: the triangular fuzzy number takes m ij as the closed interval of the median value, and m ij is the analytic hierarchy process Scale values from 1 to 9 in ; obtain the fuzzy numbers a ij = (li ij , m ij , h ij ) for the pairwise comparison of all the indicators in the k layer. Assuming that the number of judging experts is t, then the index i of the kth layer is paired with The final triangular fuzzy number of index j is
Figure FDA0003034066610000011
Assuming that there are h indicators in the kth layer, a fuzzy matrix of h k ×h k is obtained
Figure FDA0003034066610000012
 b)根据模糊矩阵A，计算第i个指标对同层k的其它所有因素的综合模糊测度值
Figure FDA0003034066610000013
其计算方式如下：
Figure FDA0003034066610000014
b) According to the fuzzy matrix A, calculate the comprehensive fuzzy measure value of the i-th index for all other factors in the same layer k
Figure FDA0003034066610000013
It is calculated as follows:
Figure FDA0003034066610000014
 c)计算第k层第i个指标的权重分配，计算公式如下：c) Calculate the weight distribution of the i-th indicator of the k-th layer. The calculation formula is as follows:
Figure FDA0003034066610000015
其中
Figure FDA0003034066610000016
的计算方式为
Figure FDA0003034066610000015
in
Figure FDA0003034066610000016
is calculated as
Figure FDA0003034066610000017
Figure FDA0003034066610000017
 d)计算准则层最底层指标的权重分配；d) Calculate the weight distribution of the bottom index of the criterion layer; 根据上述计算步骤，得到准则层第二层6个指标的权重：According to the above calculation steps, the weights of the six indicators in the second layer of the criterion layer are obtained:
Figure FDA0003034066610000021
Figure FDA0003034066610000021
 其中
Figure FDA0003034066610000022
为准则层第一层的三个指标的权重向量；in
Figure FDA0003034066610000022
is the weight vector of the three indicators of the first layer of the criterion layer; 3)根据节点稳定性评价体系和准则层各个指标的权重分配向量，采用如下步骤计算节点当前的稳定性状态：3) According to the node stability evaluation system and the weight distribution vector of each index of the criterion layer, the following steps are used to calculate the current stability state of the node: a)构建决策空间，把每个决策状态i映射为Sn空间中的一点，表征样本状态i在Sn中的空间位置；a) Construct a decision space, map each decision state i to a point in Sn space, and represent the spatial position of sample state i in Sn; b)测量当前待评估属性向量与各个正负理想点在n维空间中的距离，计算拟合相似度，计算公式如下：b) Measure the distance between the current attribute vector to be evaluated and each positive and negative ideal point in the n-dimensional space, and calculate the fitting similarity. The calculation formula is as follows:
Figure FDA0003034066610000023
Figure FDA0003034066610000023
 其中
Figure FDA0003034066610000024
和
Figure FDA0003034066610000025
分别计算属性向量lt与样本状态i的正负理想解空间点的距离，其计算方式如下：in
Figure FDA0003034066610000024
and
Figure FDA0003034066610000025
Calculate the distance between the attribute vector l t and the positive and negative ideal solution space points of the sample state i respectively, and the calculation method is as follows:
Figure FDA0003034066610000026
Figure FDA0003034066610000026
 其中
Figure FDA0003034066610000027
表示正理想解空间点
Figure FDA0003034066610000028
的第j个属性在Sn中的分量值；in
Figure FDA0003034066610000027
Represents a positive ideal solution space point
Figure FDA0003034066610000028
The component value of the j- th attribute in Sn;
Figure FDA0003034066610000029
Figure FDA0003034066610000029
 c)根据拟合相似度，计算当前节点稳定性状态。c) Calculate the current node stability state according to the fitting similarity.
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