CN111065108B - Low-power-consumption self-adaptive clustering routing method based on energy and trust model - Google Patents

Low-power-consumption self-adaptive clustering routing method based on energy and trust model Download PDF

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CN111065108B
CN111065108B CN202010041182.2A CN202010041182A CN111065108B CN 111065108 B CN111065108 B CN 111065108B CN 202010041182 A CN202010041182 A CN 202010041182A CN 111065108 B CN111065108 B CN 111065108B
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CN111065108A (en
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王军
刘经涛
芦贺
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Shenyang University of Chemical Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H04W16/225—Traffic simulation tools or models for indoor or short range network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/246—Connectivity information discovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention discloses a Low-power consumption self-Adaptive Clustering routing method based on an Energy and Trust model, and relates to an Internet of things self-Adaptive Clustering routing method. And optimizing the routing protocol by comprehensively considering various factors such as node energy, node density, node trust value and the like. According to the ETM-LEACH algorithm, different weights are distributed to energy, density and trust values under different environments when a cluster head is elected, and finally, by updating a threshold value, elected cluster head nodes are high-efficiency and high-safety-factor nodes, so that a network can adaptively identify malicious nodes, network overhead is balanced, and the safety and reliability of the network are improved.

Description

Low-power-consumption self-adaptive clustering routing method based on energy and trust model
Technical Field
The invention relates to an adaptive clustering routing method for the Internet of things, in particular to a low-power-consumption adaptive clustering routing method based on an energy and trust model.
Background
With the rapid development of the industry of the industrial internet of things, the wireless sensor network is widely used as the core of the sensing layer, and has wide application prospect due to the low power consumption, low cost, independent sensing, data storage, processing and wireless communication capabilities in various fields such as military, industry, agriculture, medical treatment, home furnishing and the like. The wireless sensor network consists of a large number of sensor nodes with wireless communication functions, the nodes can sense various information of a monitored object in real time, and the acquired data are transmitted to a remote observer through the self-organizing network, so that the complex application problem can be solved. However, since the sensor nodes are deployed in an open environment and the energy of the nodes is limited and cannot be replenished for the second time, the storage, the communication and the computation are limited, the security cannot be guaranteed, and the network paralysis is easily caused by the attack of malicious nodes. And the cluster head nodes with more residual energy and high safety factor are elected, so that the method has important significance for guaranteeing the network safety and prolonging the network life cycle.
Disclosure of Invention
The invention aims to provide a low-power consumption self-adaptive clustering routing method based on an energy and trust model, which optimizes a routing protocol by comprehensively considering various factors such as node energy, node density, node trust value and the like. According to the ETM-LEACH algorithm, different weights are distributed to energy, density and trust values under different environments when a cluster head is elected, and finally, by updating a threshold value, elected cluster head nodes are nodes with high energy efficiency and high safety factors, so that a network can identify malicious nodes in a self-adaptive manner, network overhead is balanced, and the safety and reliability of the network are improved.
The purpose of the invention is realized by the following technical scheme:
a low-power consumption self-adaptive clustering routing method based on an energy and trust model comprises a low-power consumption self-adaptive clustering routing algorithm based on the energy and trust model, and a routing protocol is optimized by comprehensively considering factors in the aspects of node energy, node density and node trust value; according to the ETM-LEACH algorithm, different weights are distributed to energy, density and trust values under different environments when a cluster head is elected, and finally, by updating a threshold value, elected cluster head nodes are high-efficiency nodes with high safety coefficients, so that a network can adaptively identify malicious nodes, network overhead is balanced, and the safety and reliability of the network are improved;
the method comprises the following steps:
a. ETM-LEACH multi-factor model
The ETM-LEACH algorithm preferentially calculates the number of dynamic cluster heads, and then considers the network by comprehensively considering node energy, density factors and trust factors;
b. trust model
In the calculation of the node trust value, a method of combining the direct trust value and the indirect trust value of the node is adopted. The trust value evaluation of the detection subject i on the detection object j comprises a result DTN of direct detection of the node i and an indirect trust value ITN of N detection through common neighbor nodes N1, N2, 8230, 8230and the final trust value FTN is calculated according to weight after integration;
c. ETM-LEACH algorithm and flow
After the optimal cluster head is calculated according to a formula, a new threshold value is obtained by calculation after an energy factor, a density factor and a trust factor are introduced, and the threshold value and a random number set by the user are combined
Figure 100002_DEST_PATH_IMAGE002
(0<
Figure DEST_PATH_IMAGE002A
<1) By comparison, if
Figure DEST_PATH_IMAGE002AA
Less than a given threshold
Figure 100002_DEST_PATH_IMAGE004
If so, the node is selected as a cluster head in the current round; when the cluster head node is elected, the cluster head node can be elected at the nodes with more residual energy, high node density and high safety factor, so that the network is more balanced, the life cycle of the network is prolonged, malicious nodes can be identified in a self-adaptive manner, and the safety and reliability of the network are ensured.
The low-power consumption self-adaptive clustering routing method based on the energy and trust model comprises the following steps:
(1) A dynamic cluster head;
(2) An energy factor;
(3) A density factor;
(4) A trust factor.
The low-power consumption adaptive clustering routing method based on the energy and trust model comprises the following steps:
1) The more the node residual energy is, the more data processing tasks are undertaken, and the higher the cluster head probability is;
2) The smaller the energy consumption change rate is, the smaller the node task amount in the period from the previous round to the current round is, the larger the task amount should be borne in the current round, and the probability of becoming a cluster head is increased.
According to the low-power-consumption self-adaptive clustering routing method based on the energy and trust model, the density factor is larger, the number of neighbor nodes is larger, the nodes in the area are dense, and the node has a better chance to select a cluster head node; the density factor is small, which means that the number of neighbor nodes is small, the periphery of the nodes is relatively sparse, and the probability of selecting the cluster head node is small.
The low-power consumption self-adaptive clustering routing method based on the energy and trust model comprises the following steps:
1) An entrance and exit degree factor;
2) A correlation factor;
3) A difference factor.
The low-power consumption self-adaptive clustering routing method based on the energy and trust model comprises the following steps:
(1) A direct trust value;
(2) An indirect trust value;
(3) A degree of dispersion;
(4) The node finally trusts the value.
The invention has the advantages and effects that:
from the viewpoint of balancing network Energy consumption and improving network security, the invention designs a new routing algorithm, namely a Low-power consumption self-Adaptive Clustering routing algorithm (Energy and Trust models based on Low Energy Adaptive Clustering Hierarchy, hereinafter referred to as ETM-LEACH) based on an Energy and Trust model. And optimizing the routing protocol by comprehensively considering various factors such as node energy, node density, node trust value and the like. According to the ETM-LEACH algorithm, different weights are distributed to energy, density and trust values under different environments when a cluster head is elected, and finally, by updating a threshold value, elected cluster head nodes are nodes with high energy efficiency and high safety factors, so that a network can identify malicious nodes in a self-adaptive manner, network overhead is balanced, and the safety and reliability of the network are improved.
Drawings
FIG. 1 is a flow chart of the ETM-LEACH.
Detailed Description
The present invention will be described in detail with reference to the embodiments shown in the drawings.
1. ETM-LEACH multi-factor model
Because random equal probability election of the LEACH algorithm is not beneficial to the overall balance of the network, the ETM-LEACH algorithm preferentially calculates the number of dynamic cluster heads, and then considers the network by comprehensively considering node energy, density factors and trust factors. And the safety and reliability of the network are enhanced.
(1) Dynamic cluster head number
First calculate node mortality
Figure 100002_DEST_PATH_IMAGE006
The formula is as follows:
Figure 100002_DEST_PATH_IMAGE008
（1）
Figure 100002_DEST_PATH_IMAGE010
and
Figure 100002_DEST_PATH_IMAGE012
representing the network's death cycle and the first node death cycle respectively,
Figure 100002_DEST_PATH_IMAGE014
the number of the representative nodes is shown in the public display (2) to obtain the number of the dynamic cluster heads:
Figure 100002_DEST_PATH_IMAGE016
（2）
Figure 100002_DEST_PATH_IMAGE018
which represents the period of the current node,
Figure 100002_DEST_PATH_IMAGE020
is the area of the region(s),
Figure 100002_DEST_PATH_IMAGE022
is a threshold value for the transmission distance and,
Figure 100002_DEST_PATH_IMAGE024
representing free space model coefficients and
Figure 100002_DEST_PATH_IMAGE026
representing the multipath fading spatial model coefficients.
The cluster head optimal percentage is Popt, and the formula is as follows:
Figure 100002_DEST_PATH_IMAGE028
（3）
according to the formula (2) and the formula (3), the optimal dynamic cluster head number and the optimal cluster head percentage can be obtained, so that energy waste of cluster head nodes, too small cluster head number and energy loss caused by too long transmission distance due to too many elected cluster heads can be avoided in the network.
(2) Energy factor
1) The more the node residual energy is, the more data processing tasks are undertaken, and the higher the cluster head probability is.
2) The smaller the energy consumption change rate is, the less the node task amount in the period from the previous round to the current round is, the larger the task amount should be borne in the current round, and the probability of becoming a cluster head is increased.
The probability of selecting a cluster head by filtering out nodes with low energy by using absolute deviation values is calculated according to the following formula:
Figure 100002_DEST_PATH_IMAGE030
（4）
Figure 100002_DEST_PATH_IMAGE032
is the residual energy of the node in the current round,
Figure 100002_DEST_PATH_IMAGE034
is the average energy of the surviving nodes and,
Figure 100002_DEST_PATH_IMAGE036
is the energy of the node before the previous round begins,
Figure DEST_PATH_IMAGE038
is the node initial energy.
From the above formula, when selecting a node, electing a node with the remaining energy of the node greater than the average energy to select a cluster head, reducing the energy of the node with lower energy to select the cluster head, and adding the energy difference of two rounds of nodes as a consideration factor, so that the node with lower energy consumption in the previous round is preferentially selected at the cluster head in the current round. When the energy-higher node selects the cluster head, the overall network communication becomes more efficient.
(3) Density factor
The larger the density factor is, the more the number of the neighbor nodes is, the more the nodes are dense in the area, and the more the node has the opportunity to select the cluster head node. The density factor is small, so that the number of neighbor nodes is small, the periphery of the nodes is relatively sparse, and the probability of selecting the cluster head node is small.
Within M × M coverage area, each cluster has ideal communication radius
Figure DEST_PATH_IMAGE040
Figure DEST_PATH_IMAGE042
（5）
The density factor represents the number of adjacency points and the actual node ratio within the ideal communication radius of each cluster, and is calculated as follows:
Figure DEST_PATH_IMAGE044
（6）
Figure DEST_PATH_IMAGE046
is shown in
Figure DEST_PATH_IMAGE048
The number of neighbor nodes in the network.
The conclusion is drawn from the above disclosure that, in the ideal communication radius range, the number of neighbor nodes is compared with the number of nodes in a cluster, and the density factor can effectively solve the node base station centralization and node marginalization problems, preferentially solve the hot zone problem, reduce the energy consumption loss, and prolong the network life cycle.
(4) Trust factor
1) Factor of degree of entry and exit
The network adopts single hop and multi-hop participation In data transmission, the ratio of the receiving data volume and the forwarding data volume of the observation node is used as a reference Factor, and the safety of the node is judged by the observation subject i at the time t according to the data packet access degree of the observation object j, and the formula is as follows:
Figure DEST_PATH_IMAGE050
（7）
SP is the number of forwarded packets and RP is the number of accepted packets.
2) Correlation factor
And a new correlation factor (pertinence factor) allows the received data and the transmitted data to have certain correlation, and the higher the correlation is, the higher the safety factor is. The detection subject i compares the data packet sent by itself with the data packet sent by the detection object j as follows:
Figure DEST_PATH_IMAGE052
（8）
PP represents the number of the data packets which are transmitted at the time i and the adjacent point j, and NPP represents the irrelevance of the data volume.
3) Differential factor
If the contents of the data packets sent repeatedly by a certain node at time t are the same, the node can be considered to be under replay attack, and a malicious node can repeatedly send the same data in different periods. And the detection is performed through the adjacent nodes, so that the effectiveness of data acquisition is improved. The node reduction trust value with high repeatability is disclosed as follows:
Figure DEST_PATH_IMAGE054
（9）
DP is the number of data packets with repeated content at time t, and NDP is the number of data packets with different data packets at the same time.
2. Trust model
In the calculation of the node trust value, a method of combining the direct trust value and the indirect trust value of the node is adopted. The trust value evaluation of the detection subject i on the detection object j comprises a result DTN of direct detection of the node i and an indirect trust value ITN of N detection through common neighbor nodes N1, N2, 8230, 8230and N of the i and the j, and a final trust value FTN is calculated according to weights after integration.
(1) Direct trust value
Calculating a direct trust value of the evaluation object j according to the direct communication between the detection subject i and the detection object j, and calculating a direct trust value DTN formula at the t moment according to the access factor, the correlation factor and the difference factor and the distribution weight difference as follows:
Figure DEST_PATH_IMAGE056
（10）
Figure DEST_PATH_IMAGE058
，
Figure DEST_PATH_IMAGE060
，
Figure DEST_PATH_IMAGE062
are assigned weights, which add to 1.
(2) Indirect trust value
Firstly, calculating a direct trust value of a detection subject i to an adjacent point N1N2 \8230 \ 8230; N, and calculating a direct trust value of the adjacent point N1N2 \8230;, N to a detection object j, and further calculating an indirect trust value of i to j through the adjacent point N1:
Figure DEST_PATH_IMAGE064
（11）
(3) Degree of dispersion
As shown in the above diagram, the detecting object j may have a plurality of indirect trust values, which increases the complexity of the calculation and is not beneficial to the network. Calculating a dispersion degree formula by using mean square error:
Figure DEST_PATH_IMAGE066
（12）
s represents the deviation degree, data are filtered through mean square deviation, when the calculated indirect trust value is too much in deviation, the calculated indirect trust value is large in dispersion degree compared with the average indirect trust value, the recommendation that malicious nodes exist in adjacent nodes can occur, the height of the indirect trust value is forcibly improved, the nodes which are too much in deviation are deleted from the safety perspective, and the nodes with the dispersion degree within the threshold range are reserved.
(4) Node final trust value
After deleting the nodes with the high discrete degree, assuming that the number of the residual indirect trust values is K, the detection subject i evaluates and detects the comprehensive trust value FTN of the j, namely the final trust factor:
Figure DEST_PATH_IMAGE068
（13）
3. ETM-LEACH algorithm and flow
After the optimal cluster head is calculated according to the public (2), a new threshold value is obtained by calculation after an energy factor, a density factor and a trust factor are introduced, and the new threshold value is calculated as follows:
Figure DEST_PATH_IMAGE070
（14）
after the new threshold value is successfully calculated, the random number is set by the user
Figure DEST_PATH_IMAGE072
By comparison, if
Figure DEST_PATH_IMAGE074
Less than a given threshold
Figure DEST_PATH_IMAGE076
Then the node is selected as a cluster head in the current round.
Aiming at the problem that the prior protocol in the wireless sensor network has equal probability randomness when a cluster head node is elected, the ETM-LEACH algorithm is designed, and after energy, density and a trust model are comprehensively considered, the cluster head node can be elected at the nodes with more residual energy, high node density and high safety factor when the cluster head node is elected, so that the network is more balanced, the life cycle of the network is prolonged, malicious nodes can be identified in a self-adaptive mode, and the safety and the reliability of the network are ensured.
The whole process of ETM-LEACH is shown in FIG. 1.

Claims (1)

1. A low-power consumption self-adaptive clustering routing method based on an energy and trust model is characterized by comprising a low-power consumption self-adaptive clustering routing algorithm based on the energy and trust model, and optimizing a routing protocol by comprehensively considering factors in the aspects of node energy, node density and node trust value; the method comprises the steps that a low-power-consumption self-adaptive clustering routing algorithm ETM-LEACH based on an energy and trust model is adopted, different weights are distributed to energy, density and trust values under different environments when a cluster head is elected, and finally, by updating a threshold value, elected cluster head nodes are high-efficiency nodes with high safety factors, a network can self-adaptively identify malicious nodes, network overhead is balanced, and the safety and reliability of the network are improved;
the method comprises the following steps:
ETM-LEACH multifactor model:
the ETM-LEACH algorithm preferentially calculates the number of dynamic cluster heads, and then considers the network by comprehensively considering node energy, density factors and trust factors;
the trust model is as follows:
in the calculation of the node trust value, a method of combining a node direct trust value and an indirect trust value is adopted; the trust value evaluation of the detection subject i on the detection object j comprises a result DTN of direct detection of the node i and an indirect trust value ITN of N detection through neighbor nodes N1, N2 \8230, \8230, and the final trust value FTN is calculated according to the weight after the integration;
ETM-LEACH algorithm and process:
after the optimal cluster head is calculated according to the formula (2), a new threshold value is obtained by calculation after an energy factor, a density factor and a trust factor are introduced, and the new threshold value is calculated as follows:
Figure DEST_PATH_IMAGE001
（14）
after the new threshold value is successfully calculated, the random number is compared with the random number set by the user
Figure DEST_PATH_IMAGE002
By comparison, if
Figure 750633DEST_PATH_IMAGE003
Less than a given threshold
Figure DEST_PATH_IMAGE004
If so, the node is selected as a cluster head in the current round; when the cluster head node is elected, the cluster head node can be elected from the nodes with more residual energy, high node density and high safety coefficient, so that the network is more balanced, the life cycle of the network is prolonged, malicious nodes can be identified in a self-adaptive manner, and the safety and reliability of the network are ensured;
the ETM-LEACH multi-factor model comprises:
(1) A dynamic cluster head;
(2) An energy factor;
(3) A density factor;
(4) A trust factor;
the dynamic cluster head is as follows:
node mortality is first calculated
Figure 592687DEST_PATH_IMAGE005
The formula is as follows:
Figure DEST_PATH_IMAGE006
（1）
Figure 166058DEST_PATH_IMAGE007
and
Figure DEST_PATH_IMAGE008
representing the network's death cycle and the first node death cycle respectively,
Figure 105064DEST_PATH_IMAGE009
the number of nodes is represented, so the number of dynamic cluster heads obtained by formula (2) is as shown in the disclosure:
Figure DEST_PATH_IMAGE010
（2）
Figure 989843DEST_PATH_IMAGE011
which indicates the period of the current node,
Figure DEST_PATH_IMAGE012
is the area of the region(s),
Figure 381510DEST_PATH_IMAGE013
is a threshold value for the transmission distance and,
Figure DEST_PATH_IMAGE014
representing free space model coefficients and
Figure 707974DEST_PATH_IMAGE015
representing the multipath fading spatial model coefficients.
Obtained according to the optimal cluster head number, the optimal percentage of the cluster heads is
Figure DEST_PATH_IMAGE016
The formula is as follows:
Figure 439170DEST_PATH_IMAGE017
（3）
obtaining the optimal dynamic cluster head number and the optimal cluster head percentage according to the formula (2) and the formula (3), so that energy waste of cluster head nodes and energy loss caused by too few cluster heads and too long transmission distance due to too many elected cluster heads can be avoided in the network;
the energy factor is as follows:
1) The more the node residual energy is, the more the task of processing data is undertaken, and the higher the probability of becoming a cluster head is;
2) The smaller the energy consumption change rate is, the smaller the node task amount in the period from the previous round to the current round is, the larger the task amount is borne by the current round, and the probability of becoming a cluster head is increased;
the probability of selecting the cluster head by filtering out the nodes with low energy by using the absolute deviation value is calculated according to the following formula:
Figure DEST_PATH_IMAGE018
（4）
Figure 557167DEST_PATH_IMAGE019
is the residual energy of the node in the current round,
Figure DEST_PATH_IMAGE020
is the average energy of the surviving nodes and,
Figure 373813DEST_PATH_IMAGE021
is the energy of the node before the previous round begins,
Figure DEST_PATH_IMAGE022
is the node initial energy.
From the above formula, when selecting a node, electing a node with the residual energy greater than the average energy to select a cluster head, reducing the energy of the node with lower energy to select the cluster head, and adding the energy difference value of two rounds of nodes as a consideration factor, so that the node with lower energy consumption in the previous round is preferentially selected at the cluster head in the current round; when the energy-higher node selects the cluster head, the overall network communication becomes more efficient;
the density factor is as follows:
the larger the density factor is, the more the number of the neighbor nodes is, the dense nodes in the area are shown, and the node has more opportunity to select a cluster head node; the density factor is small, so that the number of neighbor nodes is small, the periphery of the nodes is relatively sparse, and the probability of selecting a cluster head node is small;
within M × M coverage area, each cluster has ideal communication radius
Figure 363023DEST_PATH_IMAGE023
：
Figure DEST_PATH_IMAGE024
（5）
The density factor represents the number of adjacent points and the actual node ratio within the ideal communication radius of each cluster, and is calculated as follows:
Figure 73358DEST_PATH_IMAGE025
（6）
Figure DEST_PATH_IMAGE026
is shown in
Figure 565520DEST_PATH_IMAGE027
The number of the neighbor nodes in the network;
the conclusion is drawn from the above disclosure, in the ideal communication radius range of each cluster, the number of neighbor nodes is compared with the number of nodes in the cluster, and the density factor can effectively solve the node base station centralization and node marginalization problems, preferentially solve the hot zone problem, reduce the energy consumption loss and prolong the network life cycle;
the trust factors include:
1) An entrance and exit degree factor;
2) A correlation factor;
3) A difference factor;
the entrance and exit degree factor is as follows:
the IOF is an access factor, a network adopts single hop and multi-hop to participate in data transmission, the ratio of the receiving data volume and the forwarding data volume of an observation node is used as a reference factor, and the security of the node is judged by the observation subject i at the time t on the access of a data packet of an observation object j, and the formula is as follows:
Figure DEST_PATH_IMAGE028
（7）
SP is the number of forwarding data packets, and RP is the number of receiving data packets;
the correlation factor is as follows:
a new correlation factor PF, which enables the received data and the transmitted data to have certain correlation, wherein the higher the correlation is, the higher the safety factor is; the detection subject i compares the data packet sent by itself with the data packet sent by the detection object j as follows:
Figure 869462DEST_PATH_IMAGE029
（8）
PP represents t moment i and the data packet of adjacent point j to send the same data packet quantity, NPP represents the data quantity is not relevant;
the difference factor is as follows:
if the contents of data packets sent repeatedly by a certain node at the time t are the same, the node can be considered to carry out replay attack, and a malicious node can repeatedly send the same data in different periods; the detection is carried out through the adjacent nodes, and the effectiveness of data acquisition is improved; the node reduction trust value with high repeatability is disclosed as follows:
Figure DEST_PATH_IMAGE030
（9）
DP is the data quantity of the repeated data packet content at the time t, and NDP is the data packet quantity of the data packet difference at the same time;
the trust model includes:
(1) A direct trust value;
(2) An indirect trust value;
(3) A degree of dispersion;
(4) A final trust value of the node;
the direct trust value is:
calculating a direct trust value of the evaluation object j according to the direct communication between the detection subject i and the detection object j, and calculating a direct trust value DTN formula at the moment t according to the entrance and exit degree factors, the correlation factors and the difference factors and different distribution weights as follows:
Figure 269219DEST_PATH_IMAGE031
（10）
Figure DEST_PATH_IMAGE032
，
Figure 40254DEST_PATH_IMAGE033
，
Figure DEST_PATH_IMAGE034
is the assigned weight, the weights add to 1;
the indirect trust value is as follows:
firstly, the direct trust value of detection main body i to adjacent point N1N2 \8230, N is calculated, then adjacent point N1N2 \8230, N pair detection is calculatedAnd (3) calculating the direct trust value of the object j, and then calculating the indirect trust value of j through the adjacent point N1:
Figure 437737DEST_PATH_IMAGE035
（11）
the dispersion degree is as follows:
the detection object j may have a plurality of indirect trust values, so that the complexity is increased due to the increase of calculation, and the network is not facilitated; calculating a dispersion degree formula by using mean square error:
Figure DEST_PATH_IMAGE036
（12）
s represents the deviation degree, data are filtered through mean square error, when the calculated indirect trust value is too much in deviation, the calculated indirect trust value is large in dispersion degree compared with the average indirect trust value, the adjacent nodes are recommended to have malicious nodes, the height of the indirect trust value is forcibly improved, the nodes which are too high in deviation are deleted from the safety perspective, and the nodes with the dispersion degree within the threshold value range are reserved;
the final trust value of the node is as follows:
after deleting the nodes with the high discrete degree, assuming that the number of the residual indirect trust values is K, the detection subject i evaluates and detects the comprehensive trust value FTN of the j, namely the final trust factor:
Figure 353609DEST_PATH_IMAGE037
（13）。
CN202010041182.2A 2020-01-15 2020-01-15 Low-power-consumption self-adaptive clustering routing method based on energy and trust model Active CN111065108B (en)

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