CN103812696A - Shuffled frog leaping algorithm based internet of things node reputation evaluation method - Google Patents
Shuffled frog leaping algorithm based internet of things node reputation evaluation method Download PDFInfo
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Abstract
Disclosed is a shuffled frog leaping algorithm based internet of things node reputation evaluation method. The shuffled frog leaping algorithm based internet of things node reputation evaluation method includes analyzing local features of nodes in the internet of things, calculating importance of the nodes in internet of things autonomous domains, using the calculated node importance as a basis for node screen, using a shuffled frog leaping algorithm for clustering the nodes, selecting a kind of nodes with higher importance as neighbor nodes of reputation evaluation, using the neighbor nodes to perform reputation evaluation on nodes to be evaluated according to a reputation evaluation algorithm, calculating a more accurate node reputation value according to current reputation and history reputation of the nodes, setting a threshold value, comparing the node reputation value with the threshold value to judge whether the nodes are credible, judging the nodes are incredible nodes when the reputation value is lower than the set threshold hold, and otherwise, judging the nodes are credible nodes. According to the shuffled frog leaping algorithm based internet of things node reputation evaluation method, the problem that incredible nodes interfere evaluation results in traditional reputation evaluation systems can be effectively avoided.
Description
Technical field
The present invention relates to communication technical field, relate to specifically a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm.
Background technology
At present, mainly concentrate on credible management and credible evaluation two aspects for the credibility research of distributed system, the essence of credible management is the access control model based on authentication and authorization and method in.Method for evaluating trust is that to close with the recommendation trust of inter-entity be basis, in conjunction with self experience, entity confidence level is made to evaluation, then carries out decision-making according to confidence level.Classical Prestige Management technology asks prestige mean value scheme, Bayesian network and cluster to filter.Between node, between territory, break the wall of mistrust very importantly, in general, the information that level of trust can generate corresponding event by the behavior of neighbor node and they be assessed.But node is very easily under attack, and then provide feedback insecure or malice, affect real node credit value.
Existing methodical defect has:
1, credit rating assessment lacks indirect prestige recommendation
Common credit rating assessment need to be considered the factor of subjective and objective two aspects, and the direct credit worthiness of the evaluation of user's credibility not only being obtained by this locality as resource determines, also should consider to calculate the indirect prestige recommendation (reputation) that Agency obtains; Determined by the user who uses this node and the evaluation of the node degree of reliability is not single yet, need to consider that equally the user in other self-control territories recommends the prestige of this node.Consider the subjective objective factor in prestige and the credit assessment process of user, node.
2, the interference of unreliable node to credit assessment
The high isomery and the height that have due to Internet of Things self mix feature, and the node in Autonomous Domain is probably subjected to illegal invasion and attacks, thereby does the degrees of comparison evaluation making mistake, and final assessment result is disturbed to the accuracy of impact evaluation result.
Summary of the invention
The present invention is directed to the insincere node that may occur in traditional credit standing evaluation system and assessment result is produced to the problem of disturbing, propose 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 method based on shuffled frog leaping algorithm, and described method comprises the following steps:
The Local Features of node in step 1, analyte networking, the importance of the node in calculating Internet of Things Autonomous Domain;
The foundation of step 2, the screening using the node importance after calculating as node, utilizes shuffled frog leaping algorithm to carry out cluster to node;
The higher category node of the node importance that obtains in step 3, selecting step 2 is as the neighbor node of credit assessment;
Step 4, according to credit assessment algorithm, use the neighbor node that obtains of step 3 to carry out credit assessment to the node of needs assessment;
Step 5, calculate node credit value more accurately according to the current prestige of node and historical prestige;
Whether credible step 6, setting threshold, compare predicate node by the node credit value obtaining in step 5 and this threshold value; When credit value is during lower than setting threshold, predicate node is insincere node, and no person is trusted node.
The computational methods of the node importance in described step 1 comprise the following steps:
Step 201, net list is shown to two tuple YK, EY, node set is expressed as
, the set expression on the limit of connected node is
, wherein, n and m represent nodes and the limit number of network, the more important node in limit of connected node is just more important;
Step 202, utilize formula
calculate the weight on limit, wherein
represent the limit number being connected with node i;
Step 203, the weights on limit that node i connects are sued for peace
represent the weight of node i;
Step 204, utilize formula
the importance of computing node i
, wherein
.
The hybrid algorithm that leapfrogs in described step 2 comprises the following steps:
Step 301, take node importance as screening foundation, utilize shuffled frog leaping algorithm to screen neighbor node, each frog individuality
can represent the importance of a node
and utilize formula
calculate the fitness of frog;
The frog colony of step 302, a random initializtion P frog composition
, i=1,2 ... P;
Step 303, carry out descending according to the fitness of every the frog calculating, the frog individuality of functional value optimum is made as
;
Step 304, whole frog colony is divided into F group, each group comprises G frog, therefore
, first frog enters the 1st group, and second frog enters the 2nd group, and F frog enters F group, and F+1 frog enters again the 1st group afterwards, and F+2 frog enters the 2nd group, by that analogy, until all frog is divided complete;
Step 305, group carry out partial-depth search to each group after dividing, and has individuality optimum and the poorest fitness to be in each group
with
, iteration is for the poorest fitness each time
carry out, update strategy is: frog displacement
, upgrade the poorest frog position
(
) wherein,
be
between random number,
be the ultimate range that allows frog to move, by above formula, group's the poorest frog individuality of endoadaptation degree upgraded, each group carries out the Local Search number of times of setting;
Step 306, the group that will search for through partial-depth merge a new group of composition, and judge whether to meet the end condition of algorithm, complete the reliable neighbor node of screening.
Credit assessment in described step 4 specifically comprises the following steps:
According to the node obtaining after screening, using them as neighbor node, at T
pin cycle, have multiple neighbor nodes and observe evaluated node, each node has and safeguards the neighbor node list of self, in list, comprise the information such as No. ID, neighbor node and credit value, in the time that node i sends packet to node n, need intermediate node j to forward, node calculates node point reliability by existing monitoring condition
,
computing formula as follows:
, wherein
represent the quantity of node i requesting node j forwarding data bag;
represent that j is the quantity of i forwarding data bag, in the cycle
in, the more reliabilitys of packet that j forwards are higher.
Provide too high right to speak for fear of the high node of credit value, cause subjective deviation, introduce overall prestige R as parameter, be used for reducing risk, reduce the concrete formula of subjective deviation as follows:
Wherein, i is the neighbor node of N, S
nthe set of the neighbor node of N,
be to provide the credit value of monitoring node, T is reliability threshold value, can be considered to bad node, the parameter of introducing lower than the nodes ' behavior of threshold value
, wherein
for adjustment function,
, M
ikifor the total transaction amount of node i,
to expect with respect to the local prestige of its neighbor node according to node i, and
.
Beneficial effect of the present invention: the present invention filters the node in network before node is carried out to credit assessment, filtration show that node importance is higher as neighbours, avoid preferably the interference of unreliable neighbours to credit assessment result, improved the accuracy of credit assessment; The present invention utilizes shuffled frog leaping algorithm, local and the overall situation is searched for, and then node is screened, and has convergence and stronger robustness faster.
Accompanying drawing explanation
Fig. 1 is structured flowchart of the present invention.
Embodiment
As shown in the figure, a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm, described method comprises the following steps:
The Local Features of node in step 1, analyte networking, the importance of the node in calculating Internet of Things Autonomous Domain;
The foundation of step 2, the screening using the node importance after calculating as node, utilizes shuffled frog leaping algorithm to carry out cluster to node;
The higher category node of the node importance that obtains in step 3, selecting step 2 is as the neighbor node of credit assessment;
Step 4, according to credit assessment algorithm, use the neighbor node that obtains of step 3 to carry out credit assessment to the node of needs assessment;
Step 5, calculate node credit value more accurately according to the current prestige of node and historical prestige;
Whether credible step 6, setting threshold, compare predicate node by the node credit value obtaining in step 5 and this threshold value; When credit value is during lower than setting threshold, predicate node is insincere node, and no person is trusted node.
The computational methods of the node importance in described step 1 comprise the following steps:
Step 201, net list is shown to two tuple YK, EY, node set is expressed as
, the set expression on the limit of connected node is
, wherein, n and m represent nodes and the limit number of network, the more important node in limit of connected node is just more important;
Step 202, utilize formula
calculate the weight on limit, wherein
represent the limit number being connected with node i;
Step 203, the weights on limit that node i connects are sued for peace
represent the weight of node i;
Step 204, utilize formula
the importance of computing node i
, wherein
.
The hybrid algorithm that leapfrogs in described step 2 comprises the following steps:
Step 301, take node importance as screening foundation, utilize shuffled frog leaping algorithm to screen neighbor node, each frog individuality
can represent the importance of a node
and utilize formula
calculate the fitness of frog;
The frog colony of step 302, a random initializtion P frog composition
, i=1,2 ... P;
Step 303, carry out descending according to the fitness of every the frog calculating, the frog individuality of functional value optimum is made as
;
Step 304, whole frog colony is divided into F group, each group comprises G frog, therefore
, first frog enters the 1st group, and second frog enters the 2nd group, and F frog enters F group, and F+1 frog enters again the 1st group afterwards, and F+2 frog enters the 2nd group, by that analogy, until all frog is divided complete;
Step 305, group carry out partial-depth search to each group after dividing, and has individuality optimum and the poorest fitness to be in each group
with
, iteration is for the poorest fitness each time
carry out, update strategy is: frog displacement
, upgrade the poorest frog position
(
) wherein,
be
between random number,
be the ultimate range that allows frog to move, by above formula, group's the poorest frog individuality of endoadaptation degree upgraded, each group carries out the Local Search number of times of setting;
Step 306, the group that will search for through partial-depth merge a new group of composition, and judge whether to meet the end condition of algorithm, complete the reliable neighbor node of screening.
Credit assessment in described step 4 specifically comprises the following steps:
According to the node obtaining after screening, using them as neighbor node, at T
pin cycle, have multiple neighbor nodes and observe evaluated node, each node has and safeguards the neighbor node list of self, in list, comprise the information such as No. ID, neighbor node and credit value, in the time that node i sends packet to node n, need intermediate node j to forward, node calculates node point reliability by existing monitoring condition
,
computing formula as follows:
, wherein
represent the quantity of node i requesting node j forwarding data bag;
represent that j is the quantity of i forwarding data bag, in the cycle
in, the more reliabilitys of packet that j forwards are higher.
?provide too high right to speak for fear of the high node of credit value, cause subjective deviation, introduce overall prestige R as parameter, be used for reducing risk, reduce the concrete formula of subjective deviation as follows:
Wherein, i is the neighbor node of N, S
nthe set of the neighbor node of N,
be to provide the credit value of monitoring node, T is reliability threshold value, can be considered to bad node, the parameter of introducing lower than the nodes ' behavior of threshold value
, wherein
for adjustment function,
, M
ikifor the total transaction amount of node i,
to expect with respect to the local prestige of its neighbor node according to node i, and
.
Participate in that the neighbor node number of credit rating is more and volume of transmitted data is larger, the expectation of overall prestige is just more accurate.
Node in Internet of Things is because have the feature of mobility, and relatively inexpensive.If be identified as the unreliable node of passive forwarding data bag just seems and is unfair because of accidental cause.Therefore we also need historical credit value to take into account in the time calculating current credit value, form new credit value.Concrete formula is as follows:
The present invention filters the node in network before node is carried out to credit assessment, filters and show that node importance is higher as neighbours, has avoided preferably the interference of unreliable neighbours to credit assessment result, has improved the accuracy of credit assessment; The present invention utilizes shuffled frog leaping algorithm, local and the overall situation is searched for, and then node is screened, and has convergence and stronger robustness faster.
Claims (5)
1. the Internet of things node credit assessment method based on shuffled frog leaping algorithm, is characterized in that: described method comprises the following steps:
The Local Features of node in step 1, analyte networking, the importance of the node in calculating Internet of Things Autonomous Domain;
The foundation of step 2, the screening using the node importance after calculating as node, utilizes shuffled frog leaping algorithm to carry out cluster to node;
The higher category node of the node importance that obtains in step 3, selecting step 2 is as the neighbor node of credit assessment;
Step 4, according to credit assessment algorithm, use the neighbor node that obtains of step 3 to carry out credit assessment to the node of needs assessment;
Step 5, calculate node credit value more accurately according to the current prestige of node and historical prestige;
Whether credible step 6, setting threshold, compare predicate node by the node credit value obtaining in step 5 and this threshold value; When credit value is during lower than setting threshold, predicate node is insincere node, and no person is trusted node.
2. a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm as claimed in claim 1, is characterized in that: the computational methods of the node importance in described step 1 comprise the following steps:
Step 201, net list is shown to two tuple YK, EY, node set is expressed as
, the set expression on the limit of connected node is
, wherein, n and m represent nodes and the limit number of network, the more important node in limit of connected node is just more important;
Step 202, utilize formula
calculate the weight on limit, wherein
represent the limit number being connected with node i;
Step 203, the weights on limit that node i connects are sued for peace
represent the weight of node i;
Step 204, utilize formula
the importance of computing node i
, wherein
.
3. a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm as claimed in claim 1, is characterized in that: the hybrid algorithm that leapfrogs in described step 2 comprises the following steps:
Step 301, take node importance as screening foundation, utilize shuffled frog leaping algorithm to screen neighbor node, each frog individuality
can represent the importance of a node
and utilize formula
calculate the fitness of frog;
The frog colony of step 302, a random initializtion P frog composition
, i=1,2 ... P;
Step 303, carry out descending according to the fitness of every the frog calculating, the frog individuality of functional value optimum is made as
;
Step 304, whole frog colony is divided into F group, each group comprises G frog, therefore
, first frog enters the 1st group, and second frog enters the 2nd group, and F frog enters F group, and F+1 frog enters again the 1st group afterwards, and F+2 frog enters the 2nd group, by that analogy, until all frog is divided complete;
Step 305, group carry out partial-depth search to each group after dividing, and has individuality optimum and the poorest fitness to be in each group
with
, iteration is for the poorest fitness each time
carry out, update strategy is: frog displacement
, upgrade the poorest frog position
(
) wherein,
be
between random number,
be the ultimate range that allows frog to move, by above formula, group's the poorest frog individuality of endoadaptation degree upgraded, each group carries out the Local Search number of times of setting;
Step 306, the group that will search for through partial-depth merge a new group of composition, and judge whether to meet the end condition of algorithm, complete the reliable neighbor node of screening.
4. a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm as claimed in claim 1, is characterized in that: the credit assessment in described step 4 specifically comprises the following steps:
According to the node obtaining after screening, using them as neighbor node, at T
pin cycle, have multiple neighbor nodes and observe evaluated node, each node has and safeguards the neighbor node list of self, in list, comprise the information such as No. ID, neighbor node and credit value, in the time that node i sends packet to node n, need intermediate node j to forward, node calculates node point reliability by existing monitoring condition
,
computing formula as follows:
, wherein
represent the quantity of node i requesting node j forwarding data bag;
represent that j is the quantity of i forwarding data bag, in the cycle
in, the more reliabilitys of packet that j forwards are higher.
5. a kind of Internet of things node credit assessment method based on shuffled frog leaping algorithm as claimed in claim 4, it is characterized in that: provide too high right to speak for fear of the high node of credit value, cause subjective deviation, introduce overall prestige R as parameter, be used for reducing risk, reduce the concrete formula of subjective deviation as follows:
Wherein, i is the neighbor node of N, S
nthe set of the neighbor node of N,
be to provide the credit value of monitoring node, T is reliability threshold value, can be considered to bad node, the parameter of introducing lower than the nodes ' behavior of threshold value
, wherein
for adjustment function,
, M
ikifor the total transaction amount of node i,
to expect with respect to the local prestige of its neighbor node according to node i, and
.
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