CN106162720B - Cognitive wireless self-organizing network node stability evaluation method based on multi-attribute decision - Google Patents
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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
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.
Drawings
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:
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:
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:
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:
after normalization, a 6-dimensional attribute vector is finally obtained
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
Assuming that the k-th layer has h indexes, one h is obtainedk×hkIs fuzzy matrix of
It should be noted that the matrix has reciprocal properties, i.e. it is a matrix with a reciprocal property
TABLE 1 Scale comparison Table for attribute relative importance comparison 1 ~ 9
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
The calculation formula of (a) is as follows:
3) calculating the weight distribution of the ith index of the kth layer according to the following formula
Thus, the weight of the k-th layer index is assigned as:
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
And
thus, the index attribute weight is assigned as:
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:
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
Computing
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
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
The distance calculation formula of (c) is as follows:
wherein
Representing positive ideal solution space points
Is at SnIs measured.
Current attribute vector to be evaluated ItNegative ideal solution space point with sample state i
The distance calculation formula of (c) is as follows:
then the fitting similarity between the current attribute vector to be evaluated and the sample state i is:
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. a cognitive wireless self-organizing network node stability evaluation method based on multi-attribute decision-making comprises node stability evaluation system construction, index weight calculation based on a fuzzy analytic hierarchy process and node stability judgment calculation based on a positive and negative ideal solution method, and is characterized in that:
1) each node periodically collects parameters related to each index in the current local cognitive radio self-organizing network environment, a network layer acquires and analyzes the parameters through a cross-layer interaction mechanism, a node stability index evaluation system is constructed, and the influence degree of each factor on the node stability is analyzed; the node stability index evaluation system is divided into a target layer, a criterion layer and a decision scheme layer, wherein the target layer is used for node stability evaluation; the criterion layer comprises 2 layers, wherein the first layer comprises neighbor node stability, available frequency spectrum stability and interference degree; the second layer comprises 6 specific evaluation indexes, namely the number of one-hop neighbor nodes, the link stability of the one-hop neighbor nodes, the number of available channels of the one-hop neighbor nodes, the available channel change activity statistics of the current node, the link quality of the one-hop neighbor nodes and the channel statistics of the current node subjected to interference; the decision scheme layer comprises three decisions, namely stable, sub-stable and unstable;
2) according to the node stability evaluation system, the following steps are adopted to calculate the weight distribution of each index of the criterion layer:
a) comparing all indexes of the kth layer of the criterion layer pairwise by adopting a triangular fuzzy number to obtain a fuzzy matrix A; the calculation method is as follows: number of triangular blur in mijIs the median enclosed area, mijIs a scale value of 1-9 in an analytic hierarchy process; 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
Assuming that the k-th layer has h indexes, one h is obtainedk×hkIs fuzzy matrix of
b) According to the fuzzy matrix A, calculating the comprehensive fuzzy measurement value of the ith index to all other factors of the same layer k
The calculation method is as follows:
c) and calculating the weight distribution of the ith index of the kth layer according to the following calculation formula:
d) Calculating the weight distribution of the lowest indexes of the criterion layer;
according to the calculation steps, the weights of 6 indexes of the second layer of the criterion layer are obtained:
wherein
Weight vectors that are three indices of the first layer of the criterion layer;
3) according to the weight distribution vector of each index of the node stability evaluation system and the criterion layer, the current stability state of the node is calculated by adopting the following steps:
a) constructing a decision space, mapping each decision state i to SnA point in space characterizing a sample state i at Sn(iii) a spatial position of;
b) measuring the distance between the current attribute vector to be evaluated and each positive and negative ideal point in an n-dimensional space, and calculating the fitting similarity, wherein the calculation formula is as follows:
wherein
And
separately compute an attribute vector ltThe distance to the positive and negative ideal solution space points of the sample state i is calculated as follows:
wherein
Representing positive ideal solution space points
Is at SnThe component value of (1);
c) and calculating the stability state of the current node according to the fitting similarity.
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