CN111654855A - Authority updating method in underwater wireless sensor network based on AHP - Google Patents
Authority updating method in underwater wireless sensor network based on AHP Download PDFInfo
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Abstract
The invention discloses an AHP-based trust updating method for an underwater wireless sensor network, which comprises the following four steps: 1. selecting a trust attribute, and initializing a node trust level; 2. selecting a trust evidence according to the trust attribute, and establishing a trust mechanism hierarchical structure model of the underwater wireless sensor network based on an AHP algorithm; 3. constructing a judgment matrix of the trust attribute and the trust evidence, carrying out consistency check, and if the judgment matrix passes the consistency check, constructing a trust degree vector and a multidimensional trust degree matrix; 4. and constructing a time weight matrix, obtaining a multi-dimensional weight matrix, calculating a comprehensive trust level by combining the multi-dimensional trust level matrix, and judging and updating the node trust level according to the comprehensive trust level. The method can effectively calculate and update the comprehensive trust level of the node, thereby ensuring the safety of the underwater wireless sensor network.
Description
Technical Field
The invention belongs to the field of Internet of things security, and particularly relates to a trust updating method in an underwater wireless sensor network based on AHP.
Background
With the rapid development and wide application of computer network technology and communication technology (broadband technology, wireless technology, etc.), the network security problem is increasingly prominent. In recent years, some scholars at home and abroad begin to introduce trust means aiming at the network security problem, obtain some preliminary results, and trust provides a new idea for solving the access control problem in the open dynamic network. The trust model plays an important role in resisting internal attack, identifying malicious nodes, selfish nodes, low-competitiveness nodes and the like, can be applied to research in aspects of safe routing, safe positioning, data fusion and the like, and is used for improving the safety, reliability and fairness of the system.
Different from trust calculation in a land wireless sensor network, the calculation of comprehensive trust of nodes in an underwater wireless sensor network is very complex, and trust evidences to be considered are not only the self-capability of the nodes but also the fluctuation, stability and the like of an underwater environment. There is a certain degree of difficulty in dealing with the complex and diverse trust evidences underwater.
Analytic hierarchy process, AHP for short, refers to a decision-making method that decomposes elements always related to decision-making into levels such as targets, criteria, schemes, etc., and performs qualitative and quantitative analysis on the basis. The method is a hierarchical weight decision analysis method which is provided by the American operational research staff Pittsburgh university professor Sudoku application network system theory and a multi-target comprehensive evaluation method. The trust mechanism of the underwater wireless sensor network is evaluated by using the idea of an analytic hierarchy process, and a more reliable scheme is provided for constructing a trust model of a trusted network.
At present, there are studies on trust calculation by scholars at home and abroad using AHP, and the relevant documents are as follows:
a User Behavior evaluation Method Based on an analytic hierarchy process is provided in an article 'Research on Trustedevaluation Method of User Behavior Based on AH P Algorithm' published in 2015 by Jie MA, Yongsheng ZHANG and the like.
An assessment method of Trust management model for Ad hoc networks was proposed in article "Creditityevaluation of Trust Models based on Fuzzy Quantization and AHP in Ad hoc Screen", published by Jingpei Wang, Jie Liu, Zi Xing et al in 2016. First, some attributes are extracted from the trust request of a specific scene in the ad hoc network, and some traditional ad hoc trust models are qualitatively analyzed. And then, quantitatively calculating the overall evaluation value of the hierarchical attributes of the candidate trust model by using a fuzzy theory and an analytic hierarchy process, and selecting an optimal scheme according to the sorted evaluation results. The method can effectively evaluate the trust model and select the optimal trust model for a decision maker in a specific scene.
In an article "Based on tree Structure and AHP Algorithm in Wireless Sensor Networks", published in 2017 by Eghbal Ghazizadeh, Brian Cusack et al, a theoretical solution is proposed for the trust gap between a cloud identity provider and a cloud identity customer, wherein a multi-standard decision (MCDM) is introduced to prioritize the attributes of a cloud identity trust framework, dividing the overall trust assessment into two parts: trust analysis and quantification of trust for federated identity management systems provide input to the Analytic Hierarchy Process (AHP) using the MCDM method to complete trust evaluation.
Disclosure of Invention
Aiming at the problems, the invention provides a trust updating method in an underwater wireless sensor network based on AHP, which overcomes the problem that the weighted value is difficult to accurately take value in the traditional method, effectively calculates and updates the node trust in real time, and comprises the following steps:
a trust updating method in an underwater wireless sensor network based on AHP comprises the following four main steps:
(1) initializing a node trust level and dividing node trust attributes;
(2) selecting a trust evidence according to the trust attribute, and constructing a trust level model of the underwater wireless sensor network based on AHP;
(3) constructing a judgment matrix of trust attributes and trust evidences, carrying out consistency check, and if the judgment matrix passes the consistency check, constructing a trust degree vector and a multidimensional trust matrix;
(4) and constructing a time weight matrix, obtaining a multi-dimensional weight matrix, calculating a comprehensive trust level by combining the multi-dimensional trust matrix, and judging and updating the node trust level according to the comprehensive trust level.
The step (1) specifically comprises the following steps:
(1.1) node Trust level initialization
The node trust is initialized to five levels, namely complete trust [0.8,1], high trust [0.6,0.8), common trust [0.4,0.6), low trust [0.2,0.4) and distrust [0, 0.2);
(1.2) node Trust Attribute partitioning
The node trust attributes are divided into three categories: usability, reliability, security.
Selecting a trust evidence corresponding to the node trust attribute in the step (2): selecting the availability evidence as the available memory of the node and the node residual energy; selecting reliability evidence as the successful interaction times of the nodes and the transmission delay of the nodes; and selecting the safety evidence as the node recommendation capability and the consistency of the data packet.
The step (3) specifically includes:
(3.1) construction of judgment matrix of trust attribute
Firstly, calculating the importance degree of each trust attribute of the node j at the moment m according to the trust evidence of the node j at the moment m;
secondly, the importance degrees of the three trust attributes of the node j at the moment m are respectively compared to obtain a trust attribute judgment matrix of the node j at the moment m;
then calculating an attribute weight vector according to the attribute judgment matrix and carrying out consistency check on the attribute weight vector;
(3.2) construction of judgment matrix of trust evidence
After the judgment matrix of the trust attributes passes the verification, respectively comparing the importance degrees of trust evidences corresponding to the trust attributes of the node j at the moment m to obtain a trust evidence judgment matrix of the node j at the moment m;
finally, calculating a weight vector according to a judgment matrix of the trust evidence and carrying out consistency check;
(3.3) construction of confidence vector matrix
Firstly, combining trust evidence weight vectors passing through consistency check at the mth moment to form a trust evidence weight matrix;
secondly, calculating a trust degree vector of the node j at the m moment by using a trust evidence weight matrix of the node j at the m moment;
(3.4) construction of a multidimensional Trust matrix
And forming a multidimensional trust degree matrix of the node j according to the trust degree vector of the node j at the moment m and the trust degree vectors of the node j at N moments before the moment m.
The step (4) specifically includes:
(4.1) construction of time weight matrix
Setting the weights occupied by the trust evidences at the mth moment of the node j and N moments before the mth moment according to the principle that the trust weight at the latest moment is maximum, and constructing a time weight matrix;
multiplying the trust evidence weight vector by the time weight matrix to obtain a multidimensional weight matrix of the node j;
(4.2) calculation of comprehensive Trust
Calculating the comprehensive trust degree of the node j by using the multidimensional trust degree matrix and the multidimensional weight matrix of the node j;
(4.3) update of node Trust level
The trust factor is set as a support factor and two rejection factors; and when the trust level of the node is increased, calculating by adopting a support factor, and when the trust level is reduced, selecting a rejection factor according to the malicious behavior interaction proportion.
The invention has the beneficial effects that:
(1) according to the invention, the AHP idea is utilized to compare the trust attributes with the trust evidences, and the weight matrix is obtained by combining the importance of the trust evidences, so that the weights among the trust evidences can be conveniently measured, and the comprehensive trust degree is more accurate.
(2) The method for updating trust by using AHP in the invention combines time weight, which is convenient for processing dynamic update of trust.
(3) The division of the node trust attributes in the invention is different from the prior art, and the trust evidence is further divided by considering three aspects of availability, reliability and safety, so that the decision-making performance of the AHP method can be better realized, and the final trust decision-making result is facilitated.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a trust model hierarchy of an underwater wireless sensor network.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, a trust updating method in an AHP-based underwater wireless sensor network includes the following steps: (1) and initializing a node trust level and dividing node trust attributes.
(2) And selecting a trust evidence according to the trust attribute, and constructing an underwater wireless sensor network trust level model based on AHP.
(3) And constructing a judgment matrix of the trust attribute and the trust evidence, carrying out consistency check, and if the judgment matrix passes the consistency check, constructing a trust degree vector and a multidimensional trust degree matrix.
(4) And constructing a time weight matrix, obtaining a multi-dimensional weight matrix, calculating a comprehensive trust level by combining the multi-dimensional trust level matrix, and judging and updating the node trust level according to the comprehensive trust level.
The step (1) specifically comprises:
(1.1) initialization of node trust level: node trust is initialized to five levels, full trust [0.8,1], high trust [0.6,0.8), normal trust [0.4,0.6), low trust [0.2,0.4), no trust [0,0.2 ], respectively.
(1.2) node trust attribute division: the node trust attributes are divided into three categories: usability, reliability, security.
The step (2) specifically comprises:
(2.1) selecting trust evidences corresponding to the node trust attributes: the availability evidence is selected as available memory (represented by FM) of the node and residual energy (represented by RE) of the node; the reliability evidence is selected as the successful interaction times (represented by NS) of the nodes and the transmission delay (represented by TD) of the nodes; the security evidence is chosen as node recommendation capability (denoted by RA), and packet consistency (denoted by PC).
(2.2) constructing a trust hierarchy of the underwater wireless sensor network, dividing the trust hierarchy into three levels according to different trust behavior characteristics contained in trust evidence, and establishing a trust hierarchy model according to an AHP idea, wherein the trust hierarchy model corresponds to the trust evidence one by one, and the usability, the reliability and the safety are shown in figure 2.
The step (3) specifically comprises:
(3.1) construction of judgment matrix of trust attribute
Firstly, calculating the importance degree of each trust attribute of the node j at the moment m according to the trust evidence of the node j at the moment m;
secondly, the importance degrees of the three trust attributes of the node j at the moment m are respectively compared to obtain a trust attribute judgment matrix of the node j at the moment m;
then calculating an attribute weight vector according to the attribute judgment matrix and carrying out consistency check on the attribute weight vector;
(3.1.1) Trust Attribute C for levelsk(k 1, 2.. said., m), comparing the peer trust attribute pairs to form a judgment matrix C:
wherein, cijShowing the comparison result of the ith factor relative to the jth factor. In which nine-level classification is adopted for comparison of importance between attributes, and table 1 shows a general numerical value setting standard group.
TABLE 1 set of values for the standards group (3.1.2)
Wherein, consistency in consistency check refers to the logical consistency of judging logical thinking, and the maximum characteristic root of the matrix is:
wherein λ ismaxIs the largest feature root, w, of the matrix AiIs the element of each vector in A, corresponding to lambda in the judgment matrixmaxThe feature vector of (a) is normalized and then recorded as W (the sum of the elements in the vector is 1).
The consistency index is expressed as CI and is calculated as follows:
(3.2) construction of judgment matrix of trust evidence
Wherein n is the number of vectors in A.
After the judgment matrix of the trust attributes passes the verification, respectively comparing the importance degrees of trust evidences corresponding to the trust attributes of the node j at the moment m to obtain a trust evidence judgment matrix E of the node j at the moment m;
wherein e isijRepresenting all levels of trust evidences corresponding to the trust attributes at the moment m.
Finally, calculating a weight vector according to a judgment matrix of the trust evidence and carrying out consistency check; (3.2.1) carrying out geometric average method on the weight calculation of the trust evidence according to each row vector of the matrix of the trust evidence, and then normalizing to obtain a feature vector W and a weight matrix W of the trust evidence, wherein the feature vector W and the weight matrix W are expressed as follows:
(3.3) construction of confidence vector matrix
Firstly, combining trust evidence weight vectors passing through consistency check at the mth moment to form a trust evidence weight matrix;
secondly, calculating a trust degree vector of the node j at the moment m by using a trust evidence weight matrix of the j at the moment m;
(3.3.1) calculating a trust evidence matrix C and a weight matrix W to obtain a trust degree vector of the trust evidence of the node at the ith moment, and setting the trust degree vector as tiI.e. by
Finally, forming a multidimensional trust matrix of the node j according to the trust vector and the trust vectors of the node j at N moments before the mth moment;
(3.3.2) the trust degree vector of each trust evidence of the ith time node, the i-1 th time and the i-2 th time, and the trust degree of the i-3 th time form a multidimensional trust degree matrix tm.
Wherein t isFMiIs the trust level, t, of the available memory of the node at time iREiNode residual energy confidence level at time i, tNSiTrust level of successful interaction times of node at time i, tTDiFor node transmission delay trust level at time i, tRAiRecommending a capability trust level, t, for the node at time iPCiThe consistency trust level of the data packet of the node at the moment i; i-1 is the previous moment of i, and so on.
The step (4) specifically comprises:
(4.1) time-based matrix construction: setting weights of trust attributes of the ith moment and N moments before the ith moment of the node j according to the principle that the trust weight at the latest moment is maximum, constructing a time weight matrix, and multiplying a trust evidence weight vector w by the time weight matrix to obtain a multidimensional weight matrix of the node j;
(4.1.1) time weighting of
twm＝[twi-3,twi-2,twi-1,twi]T(8)
Multiplying the temporal weight by the trust evidence weight vector to arrive at an available time-dependent weight matrix twm。
(4.2) calculating the comprehensive trust degree: and calculating the comprehensive trust of the node j by using the multidimensional trust matrix and the multidimensional weight matrix of the node j.
The comprehensive trust is obtained by multiplying a multidimensional trust matrix and a multidimensional weight matrix
ET＝rwm*tm (9)。
(4.3) update of trust level: the trust factor is set to one support factor and two rejection factors. Two changes usually exist in node trust, the level is higher or lower, when the trust level is higher, a support factor is adopted for calculation, and when the trust level is lower, a rejection factor is selected according to the malicious behavior interaction proportion. a is a support factor defined as:
wherein, lambda is a regulating factor, and lambda belongs to [0, 1 ].
Setting two rejection factors for the change of the untrusted evidence, wherein the updating of the trusted evidence is related to the interaction proportion of the malicious behaviors, when the malicious behavior proportion is smaller than r, the penalty factor is smaller, and when the malicious behavior proportion is larger than r, the penalty is doubled, and the trusted evidence is updated as follows:
where the slot is the kth, h is the next slot, Ti lOther trust levels within the time slot.
b is a rejection factor, defined as:
Claims (5)
1. a trust updating method in an underwater wireless sensor network based on AHP is characterized by comprising the following four main steps:
(1) initializing a node trust level and dividing node trust attributes;
(2) selecting a trust evidence according to the trust attribute, and constructing a trust level model of the underwater wireless sensor network based on AHP;
(3) constructing a judgment matrix of the trust attribute and the trust evidence, carrying out consistency check, and if the judgment matrix passes the consistency check, constructing a trust degree vector and a multidimensional trust degree matrix;
(4) and constructing a time weight matrix, obtaining a multi-dimensional weight matrix, calculating a comprehensive trust level by combining the multi-dimensional trust level matrix, and judging and updating the node trust level according to the comprehensive trust level.
2. The AHP-based underwater wireless sensor network trust updating method of claim 1, wherein: the step (1) specifically comprises the following steps:
(1.1) node Trust level initialization
The node trust is initialized to five levels, namely complete trust [0.8,1], high trust [0.6,0.8), common trust [0.4,0.6), low trust [0.2,0.4) and distrust [0, 0.2);
(1.2) node Trust Attribute partitioning
The node trust attributes are divided into three categories: usability, reliability, security.
3. The AHP-based underwater wireless sensor network trust updating method of claim 1,
the method is characterized in that the selection of the trust evidence corresponding to the node trust attribute in the step (2) is as follows: selecting the availability evidence as the available memory of the node and the node residual energy; selecting reliability evidence as the successful interaction times of the nodes and the transmission delay of the nodes; and selecting the safety evidence as the node recommendation capability and the consistency of the data packet.
4. The AHP-based underwater wireless sensor network trust updating method as recited in claim 1, wherein the step (3) specifically comprises:
(3.1) construction of judgment matrix of trust attribute
First according to the nodejIn the first placemTrust evidence of a moment, compute nodejIn thatmThe importance of each trust attribute at a time;
secondly is in alignment withmTime nodejThe importance degrees of the three trust attributes are respectively compared to obtain the nodejFirst, themA trust attribute judgment matrix of the moment;
then calculating each attribute weight vector according to the attribute judgment matrix and carrying out consistency check on the attribute weight vectors;
(3.2) construction of judgment matrix of trust evidence
After the judgment matrix of the trust attribute passes the verification, the nodes are respectively comparedjIn thatmThe node is obtained according to the importance degree of the trust evidence corresponding to each trust attributejIn thatmA moment trust evidence judgment matrix;
finally, calculating a weight vector according to a judgment matrix of the trust evidence and carrying out consistency check;
(3.3) construction of confidence vector matrix
First pass the consistency checkmCombining the trust evidence weight vectors at the moment to form a trust evidence weight matrix;
second utilizing nodejIn thatmA trust evidence weight matrix of the moment, a computing nodejIn thatmA confidence vector for the moment;
(3.4) construction of a multidimensional Trust matrix
According to the nodejIn thatmMoment confidence vector and nodejIn the first placemBefore the moment of timeNIndividual moment trustDegree vector forming nodejA multidimensional confidence matrix.
5. The method for updating trust in an AHP-based underwater wireless sensor network according to claim 1, wherein the step (4) specifically comprises:
(4.1) construction of time weight matrix
Setting nodes according to the principle that the trust weight at the latest moment is maximumjTo (1) amTime and firstmThe trust attributes of N moments before the moment account for the weight, and a time weight matrix is constructed;
multiplying the trust evidence weight vector by the time weight matrix to obtain the nodejA multidimensional weight matrix of (a);
(4.2) calculation of comprehensive Trust
Calculating the comprehensive trust degree of the node j by using the multidimensional trust degree matrix and the multidimensional weight matrix of the node j;
(4.3) update of node Trust level
The trust factor is set as a support factor and two rejection factors; and when the trust level of the node is increased, calculating by adopting a support factor, and when the trust level is reduced, selecting a rejection factor according to the malicious behavior interaction proportion.
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