CN103812696B - A kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm - Google Patents
A kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm Download PDFInfo
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Abstract
A kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm, analyze the Local Features of Internet of Things interior joint, calculate the importance of the node in Internet of Things Autonomous Domain, the foundation that node importance after calculating is screened as node, node is clustered using shuffled frog leaping algorithm, the higher class node of node importance is chosen as the neighbor node of credit assessment, according to credit assessment algorithm, credit assessment is carried out to the node for needing to assess using neighbor node, more accurate node credit value is calculated according to the current prestige of node and history prestige, given threshold, node credit value and the threshold value are compared predicate node whether credible；When credit value is less than given threshold, then predicate node is insincere node, and no person is trusted node；The present invention can be prevented effectively from the problem of insincere node occurred in traditional credit standing evaluation system produces interference to assessment result.
Description
Technical field
The present invention relates to communication technical field, a kind of Internet of things node based on shuffled frog leaping algorithm is particularly related to
Credit assessment method.
Background technology
At present, the credible Journal of Sex Research for distributed system is concentrated mainly on credible management and the aspect of credible evaluation two, can
The essence of fuse tube reason is the access control model and method based on certification and mandate in one.Method for evaluating trust is then with inter-entity
Recommendation trust relation based on, evaluation is made to entity confidence level with reference to experience, then according to confidence level carry out decision-making.
Classical Prestige Management technology asks prestige average value scheme, Bayesian network and cluster filtering.Letter is set up between node, between domain
Appoint particularly significant, in general, level of trust can pass through the behavior of neighbor node and information that they generate to corresponding event
It is estimated.However, node is very easily under attack, and then provide insecure or malice feedback, the real section of influence
Point credit value.
The defect of existing method has：
1st, credit rating, which is assessed, lacks indirect prestige recommendation
Common credit rating, which is assessed, to be needed to consider subjective and objective both sides factor, and such as resource is commented user's credibility
Valency is not only determined by the direct credit worthiness locally obtained, be should also be considered and is calculated the indirect prestige recommendation that Agency obtains（Sound
Reputation）；And the evaluation of the node degree of reliability is not also single by determining using the user of the node, need also exist for considering other self-control domains
User recommends the prestige of the node.Consider during user, the prestige of node and credit assessment it is subjective it is objective because
Element.
2nd, interference of the unreliable node to credit assessment
Node in the high isomery having by Internet of Things itself and high hybrid characteristic, Autonomous Domain likely suffers from non-
The Network Intrusion of method, so as to do the degrees of comparison evaluation made mistake, is disturbed final assessment result, impact evaluation result
Accuracy.
The content of the invention
The present invention produces interference for the insincere node being likely to occur in traditional credit standing evaluation system to assessment result
Problem, proposes a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm.
The technical solution adopted in the present invention is：A kind of Internet of things node credit assessment side based on shuffled frog leaping algorithm
Method, described method comprises the following steps：
Step 1, the Local Features for analyzing Internet of Things interior joint, calculate the importance of the node in Internet of Things Autonomous Domain；
Step 2, the foundation for screening the node importance after calculating as node, are entered using shuffled frog leaping algorithm to node
Row cluster；
The higher class node of the node importance that is obtained in step 3, selecting step 2 is saved as the neighbours of credit assessment
Point；
Step 4, according to credit assessment algorithm, the neighbor node obtained using step 3 to need assess node believe
Reputation is assessed；
Step 5, more accurate node credit value calculated according to the current prestige of node and history prestige；
Whether step 6, given threshold, predicate node is compared by the node credit value obtained in step 5 and the threshold value
It is credible；When credit value is less than given threshold, then predicate node is insincere node, and no person is trusted node.
The computational methods of node importance in the step 1 comprise the following steps：
Step 201, by network representation it is two tuple YK, EY, node set is expressed as, connecting node
The set expression on side be, wherein, n and m represent the nodes and side number of network, and the side of connecting node is got over
Important then node is more important；
Step 202, utilize formulaTo calculate the weight on side, whereinRepresent the side number being connected with node i；
Step 203, the weights for connecting side to node i are summedRepresent the weight of node i；
Step 204, utilize formulaCalculate node i importance, wherein。
The hybrid algorithm that leapfrogs in the step 2 comprises the following steps：
Step 301, using node importance as screening foundation, neighbor node is screened using shuffled frog leaping algorithm, often
Individual frog individualThe importance of a node can be representedAnd utilize formulaCalculate frog
Fitness；
The frog colony of step 302, P frog composition of random initializtion, i=1,2 ... ... P；
Step 303, the fitness progress descending arrangement according to the every frog calculated, the optimal frog individual of functional value
It is set to；
Step 304, whole frog colony is divided into F group, each group includes G frog, therefore, the
One frog enters the 1st group, and second frog enters the 2nd group, and the F frog enters the F group, afterwards F+
1 frog enters the 1st group again, and the F+2 frog enters the 2nd group, by that analogy, until whole frogs have divided
Finish；
After step 305, group's division are finished, i.e., each group is carried out in local area deep-searching, each group with optimal
Individual with worst fitness isWith, iteration is for worst fitness each timeCarry out, more new strategy is：Frog
Displacement, update worst frog position（）Wherein,It isBetween random number,It is the ultimate range for allowing frog to move, by above formula to being fitted in group
The worst frog individual of response is updated, and each group performs the Local Search number of times of setting；
Step 306, one new group of merging composition of group that local area deep-searching will be passed through, and judge whether to meet calculation
The end condition of method, completes the reliable neighbor node of screening.
Credit assessment in the step 4 specifically includes following steps：
According to the node obtained after screening, using them as neighbor node, in TPIn cycle, have multiple neighbor nodes and see
Examine evaluated node, each node is owned by and safeguards the neighboring node list of itself, in list comprising neighbor node ID and
The information such as credit value, when node i sends packet to node n, it is necessary to which intermediate node j is forwarded, node passes through existing prison
Survey condition calculates node point reliability,Calculation formula it is as follows：, whereinRepresent section
Point i requesting nodes j forwards the quantity of packet；It is the quantity that i forwards packet to represent j, in the cycleIt is interior, j forwardings
The more reliabilitys of packet it is higher.
In order to avoid the high node of credit value provides too high right to speak, subjective bias is caused, global prestige R conducts are introduced
Parameter, for reducing risk, reduces the specific formula of subjective bias as follows：
Wherein, i is N neighbor node, SNIt is the set of N neighbor node,It is to provide the prestige for monitoring node
Value, T is reliability threshold value, and the nodes ' behavior less than threshold value can be considered as bad node, the parameter of introducing, whereinFor adjustment function,, MikiFor the total transaction amount of node i,It is root
Local prestige expectation according to node i relative to its neighbor node, and。
Beneficial effects of the present invention：The present invention is filtered before credit assessment is carried out to node to the node in network,
Filtering show that node importance is higher as neighbours, preferably avoids interference of the unreliable neighbours to credit assessment result, carries
The high accuracy of credit assessment；The present invention utilizes shuffled frog leaping algorithm, part and the overall situation is scanned for, and then node is entered
Row screening, with faster convergence and stronger robustness.
Brief description of the drawings
Fig. 1 is structured flowchart of the invention.
Embodiment
As illustrated, a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm, described method includes
Following steps：
Step 1, the Local Features for analyzing Internet of Things interior joint, calculate the importance of the node in Internet of Things Autonomous Domain；
Step 2, the foundation for screening the node importance after calculating as node, are entered using shuffled frog leaping algorithm to node
Row cluster；
The higher class node of the node importance that is obtained in step 3, selecting step 2 is saved as the neighbours of credit assessment
Point；
Step 4, according to credit assessment algorithm, the neighbor node obtained using step 3 to need assess node believe
Reputation is assessed；
Step 5, more accurate node credit value calculated according to the current prestige of node and history prestige；
Whether step 6, given threshold, predicate node is compared by the node credit value obtained in step 5 and the threshold value
It is credible；When credit value is less than given threshold, then predicate node is insincere node, and no person is trusted node.
The computational methods of node importance in the step 1 comprise the following steps：
Step 201, by network representation it is two tuple YK, EY, node set is expressed as, connection section
The set expression on side of point is, wherein, n and m represent the nodes and side number of network, the side of connecting node
More important then node is more important；
Step 202, utilize formulaTo calculate the weight on side, whereinRepresent the side number being connected with node i；
Step 203, the weights for connecting side to node i are summedRepresent the weight of node i；
Step 204, utilize formulaCalculate node i importance, wherein。
The hybrid algorithm that leapfrogs in the step 2 comprises the following steps：
Step 301, using node importance as screening foundation, neighbor node is screened using shuffled frog leaping algorithm, often
Individual frog individualThe importance of a node can be representedAnd utilize formulaCalculate frog
Fitness；
The frog colony of step 302, P frog composition of random initializtion, i=1,2 ... ... P；
Step 303, the fitness progress descending arrangement according to the every frog calculated, the optimal frog individual of functional value
It is set to；
Step 304, whole frog colony is divided into F group, each group includes G frog, therefore, the
One frog enters the 1st group, and second frog enters the 2nd group, and the F frog enters the F group, afterwards F+
1 frog enters the 1st group again, and the F+2 frog enters the 2nd group, by that analogy, until whole frogs have divided
Finish；
After step 305, group's division are finished, i.e., each group is carried out in local area deep-searching, each group with optimal
Individual with worst fitness isWith, iteration is for worst fitness each timeCarry out, more new strategy is：Frog
Displacement, update worst frog position（）Wherein,It isBetween random number,It is the ultimate range for allowing frog to move, by above formula to being fitted in group
The worst frog individual of response is updated, and each group performs the Local Search number of times of setting；
Step 306, one new group of merging composition of group that local area deep-searching will be passed through, and judge whether to meet calculation
The end condition of method, completes the reliable neighbor node of screening.
Credit assessment in the step 4 specifically includes following steps：
According to the node obtained after screening, using them as neighbor node, in TPIn cycle, have multiple neighbor nodes and see
Examine evaluated node, each node is owned by and safeguards the neighboring node list of itself, in list comprising neighbor node ID and
The information such as credit value, when node i sends packet to node n, it is necessary to which intermediate node j is forwarded, node passes through existing prison
Survey condition calculates node point reliability,Calculation formula it is as follows：, whereinRepresent section
Point i requesting nodes j forwards the quantity of packet；It is the quantity that i forwards packet to represent j, in the cycleIt is interior, j forwardings
The more reliabilitys of packet it is higher.
In order to avoid the high node of credit value provides too high right to speak, subjective bias is caused, global prestige R conducts are introduced
Parameter, for reducing risk, reduces the specific formula of subjective bias as follows：
Wherein, i is N neighbor node, SNIt is the set of N neighbor node,It is to provide the prestige for monitoring node
Value, T is reliability threshold value, and the nodes ' behavior less than threshold value can be considered as bad node, the parameter of introducing, whereinFor adjustment function,, MikiFor the total transaction amount of node i,It is root
Local prestige expectation according to node i relative to its neighbor node, and。
The neighbor node number of participation credit rating is more and volume of transmitted data is bigger, and the expectation of global prestige is more accurate
Really.
Node in Internet of Things is and relatively inexpensive because the feature with mobility.Recognized if as accidental cause
The unreliable node for being set to passive forwarding packet just seems and is unfair.Therefore we are also required to when calculating current credit value
History credit value is taken into account, new credit value is formed.Specific formula is as follows：
, therefore, according to different Autonomous Domain environment, we can lead to
The α factors are overregulated to weigh proportion of the history credit value to present node prestige.
The present invention is filtered before credit assessment is carried out to node to the node in network, and filtering draws node importance
It is higher as neighbours, preferably avoid interference of the unreliable neighbours to credit assessment result, improve the accurate of credit assessment
Property；The present invention utilizes shuffled frog leaping algorithm, and part and the overall situation are scanned for, and then node is screened, with faster
Convergence and stronger robustness.
Claims (4)
1. a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm, it is characterised in that：Described method includes
Following steps：
Step 1, the Local Features for analyzing Internet of Things interior joint, calculate the importance of the node in Internet of Things Autonomous Domain；
Step 2, the foundation for screening the node importance after calculating as node, are gathered using shuffled frog leaping algorithm to node
Class；
The higher class node of the node importance that is obtained in step 3, selecting step 2 as credit assessment neighbor node；
Step 4, according to credit assessment algorithm, the neighbor node obtained using step 3 to need assess node carry out prestige comment
Estimate；
Step 5, more accurate node credit value calculated according to the current prestige of node and history prestige；
Whether credible step 6, given threshold, be compared predicate node by the node credit value obtained in step 5 and the threshold value；
When credit value is less than given threshold, then predicate node is insincere node, and no person is trusted node；
Wherein, the hybrid algorithm that leapfrogs comprises the following steps：Step 301, using node importance as screening foundation, leapfroged using mixing
Algorithm is screened to neighbor node, each frog individualThe importance of a node can be representedAnd utilize formulaCalculate the fitness of frog；
The frog colony of step 302, P frog composition of random initializtion, i=1,2 ... ... P；
Step 303, the fitness progress descending arrangement according to the every frog calculated, the optimal frog individual of functional value are set to；
Step 304, whole frog colony is divided into F group, each group includes G frog, therefore, first
Frog enters the 1st group, and second frog enters the 2nd group, and the F frog enters the F group, F+1 afterwards
Frog enters the 1st group again, and the F+2 frog enters the 2nd group, by that analogy, is finished until whole frogs divide；
Step 305, group divide finish after, i.e., local area deep-searching is carried out to each group, with optimal and most in each group
The individual of poor fitness isWith, iteration is for worst fitness each timeCarry out, more new strategy is：Frog is moved
Distance, update worst frog position（）Wherein,It isBetween random number,It is the ultimate range for allowing frog to move, by above formula to being fitted in group
The worst frog individual of response is updated, and each group performs the Local Search number of times of setting；
Step 306, one new group of merging composition of group that local area deep-searching will be passed through, and judge whether to meet algorithm
End condition, completes the reliable neighbor node of screening.
2. a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm as claimed in claim 1, its feature exists
In：The computational methods of node importance in the step 1 comprise the following steps：
Step 201, by network representation it is two tuple YK, EY, node set is expressed as, connecting node
The set expression on side is, wherein, n and m represent the nodes and side number of network, and the side of connecting node is heavier
Will then node it is more important；
Step 202, utilize formulaTo calculate the weight on side, whereinRepresent the side number being connected with node i；
Step 203, the weights for connecting side to node i are summedRepresent the weight of node i；
Step 204, utilize formulaCalculate node i importance, wherein。
3. a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm as claimed in claim 1, its feature exists
In：Credit assessment in the step 4 specifically includes following steps：
According to the node obtained after screening, using them as neighbor node, in TPIn cycle, multiple neighbor node observation quilts are had
Node is assessed, each node is owned by and safeguards the neighboring node list of itself, neighbor node ID and prestige are included in list
The information such as value, when node i sends packet to node n, it is necessary to which intermediate node j is forwarded, node passes through existing monitoring bar
Part calculates node point reliability,Calculation formula it is as follows：, whereinRepresent that node i please
Node j is asked to forward the quantity of packet；It is the quantity that i forwards packet to represent j, in the cycleIt is interior, the number of j forwardings
It is higher according to many reliabilitys of Bao Yue.
4. a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm as claimed in claim 3, its feature exists
In：In order to avoid the high node of credit value provides too high right to speak, subjective bias is caused, overall situation prestige R is introduced as parameter,
For reducing risk, the specific formula of subjective bias is reduced as follows：
Wherein, i is N neighbor node, SNIt is the set of N neighbor node,It is to provide the credit value for monitoring node, T is
Reliability threshold value, the nodes ' behavior less than threshold value can be considered as bad node, the parameter of introducing, its
InFor adjustment function,, MikiFor the total transaction amount of node i,It is adjacent relative to it according to node i
The local prestige expectation of node is occupied, and。
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