CN112165694A - Method for establishing trust model of wireless sensor network - Google Patents

Method for establishing trust model of wireless sensor network Download PDF

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CN112165694A
CN112165694A CN202011208346.2A CN202011208346A CN112165694A CN 112165694 A CN112165694 A CN 112165694A CN 202011208346 A CN202011208346 A CN 202011208346A CN 112165694 A CN112165694 A CN 112165694A
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王娜
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/009—Security arrangements; Authentication; Protecting privacy or anonymity specially adapted for networks, e.g. wireless sensor networks, ad-hoc networks, RFID networks or cloud networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • 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
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H04W40/14—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality based on stability

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Abstract

The invention discloses a method for establishing a trust model of a wireless sensor network, which is characterized by comprising the following steps: (1) abstracting out interaction factors; (2) abstracting out private factors; (3) and constructing an interactive trust model based on the interactive factors and a private trust model based on the private factors. By applying the trust model, fault nodes, event nodes and normal nodes in the network can be distinguished. And the decision is further guided, the influence of the fault node on data acquisition is reduced, and trust route reference is provided for stable, effective and accurate data transmission.

Description

Method for establishing trust model of wireless sensor network
Technical Field
The invention relates to a method, in particular to a method for establishing a trust model of a wireless sensor network.
Background
Wireless Sensor Networks (WSNs) typically consist of thousands of miniature embedded computers equipped with specific types of sensors that can sense information from the surrounding environment. The collected information is relayed from one sensor to another using a secure multi-hop routing protocol until the data reaches the desired destination node. The wireless sensor network technology is widely applied to the fields of industry, environment, earthquake, building, traffic, military, traffic control, agriculture and the like. However, wireless sensor networks are vulnerable to various types of external and internal attacks, and cryptographic solutions can successfully defend against external attacks, but can fail under internal malicious attacks.
In recent years, wireless sensor network trust research based on different backgrounds is widely applied, and the models generally calculate a direct trust value on each neighbor node according to a trust factor defined by node behaviors so as to detect malicious attacks. Meanwhile, a recommended trust value from a public neighbor node is obtained through conditional transitivity, and the weight of each recommendation is obtained through a modified D-S evidence theory. The models consider the factors such as packet receiving rate, successful packet sending rate, packet forwarding rate, data consistency, security level and the like, but do not consider the relationship among the factors, so that the measurement of trust is not accurate enough, and the rate of detection of loss of trust is not high.
Therefore, a new method for establishing a trust model of a wireless sensor network is urgently needed.
Disclosure of Invention
The invention aims to solve the problems, and provides a method for establishing a trust model of a wireless sensor network, wherein the trust model measures the trust value of a node by quantifying the characteristics and the behavior of the wireless sensor network node, and judges a fault node, an event node and a normal node in the network according to the trust value so as to reduce the influence of the fault node on data acquisition and provide trust support for related applications (such as routing).
In order to achieve the purpose, the technical scheme of the invention is as follows:
the invention provides a method for establishing a trust model of a wireless sensor network, which is characterized by comprising the following steps:
(1) abstracting out interaction factors;
(2) abstracting out private factors;
(3) and constructing an interactive trust model based on the interactive factors and a private trust model based on the private factors.
In a preferred embodiment of the present invention, the interaction factors include interactive communication effectiveness, interactive data similarity, and interactive clock consistency. In a preferred embodiment of the present invention, the private factors include stability of the node sensing data and remaining energy of the node within a certain time Δ t.
The invention provides a method for establishing a trust model of a wireless sensor network, which comprises the following steps:
(1) constructing an interactive communication factor
The communication in the interaction factor is defined as: continuous effective communication rate in delta t time, i.e.
Figure BDA0002756705260000021
Wherein s isi，k(Δ t) is the number of successful communications between nodes i and j at Δ t, fi，j(Δ t) is the number of communication failures between nodes i and j at time Δ t, when the number of failures is greater than the number of successes, the two nodes i and j are considered to be untrusted;
(2) constructing interactive data factors
The data in the interaction factors are defined as: continuous data similarity rate over time Δ t, i.e.
Figure BDA0002756705260000022
Wherein
Figure BDA0002756705260000023
Wherein, ci，j(Δ t) is the number of times node i and j are similar in data over the time Δ t, di，j(Δ t) is the number of times the data are dissimilar. x is the number ofiIs the sensing data of node i;
(3) constructing an interaction time factor
The time in the interaction factor is defined as: the continuous clock synchronization rate in Δ t time, namely:
Figure BDA0002756705260000024
wherein T isiIs the sensing time of node i;
(4) constructing an interactive trust model
When calculating the trust value of the interaction between the nodes, the method uses a weighted trust model to realize that:
Figure BDA0002756705260000025
wherein, TIRepresenting the trust of interaction between two nodes, yiIs the ith factor with a weight value of alphai；
(5) Constructing a private trust model
The private trust value is shown in the formula:
Figure BDA0002756705260000031
wherein, theta1+θ2+θ3+θ4F denotes the ratio of consecutive perceptually identical data with values varying between 0 and 1,
TP-1is the trust value in the previous round of the node,
d is the offset of the perceptual data with respect to the fused value,
r is the number of times of misreading,
e is used to measure the node residual energy, thereby judging that it can be found by its parent node,
θ2and theta3Respectively, are reward and punishment coefficients, the values of which vary between 0 and 1.
In a preferred embodiment of the present invention, in step (1), if f isi，j(Δ t) 0, setting DCTi，j(Δt)＝100。。
In a preferred embodiment of the present invention, in step (2), if d isi，j(Δ t) is 0, then DST is seti，j(Δt)＝100。
In a preferred embodiment of the present invention, in step (5), E is represented by
Figure BDA0002756705260000032
Derivation of, wherein ErIs the residual energy, EiIs at the beginningThe energy of the gas is converted into the energy,
when in use
Figure BDA0002756705260000033
When the value is lower than a threshold value capable of supporting normal transmission, E is-1, otherwise, E is 0;
when in use
Figure BDA0002756705260000034
When the node is equal to 0, the node can be judged to be exhausted and can not work normally, and the node is deleted from the network.
In a preferred embodiment of the present invention, in step (5), when T ispBelow the threshold, the node will be considered as a failed node and should be removed from the network.
The invention also provides a method for detecting the lost message node, which comprises the following steps:
if the private trust value of the node is higher than the threshold value, the node is regarded as a normal node, otherwise, the node is an isolated point or an abnormal node and can be identified by using the interactive trust value, if the interactive trust value is higher than the threshold value and the private trust value is low, the node is a node positioned at the edge of the event area, otherwise, the node is a fault node.
The invention establishes a trust model considering multiple factors such as data, communication, clock, energy and the like so as to support the applications such as data aggregation, fault detection, routing and the like. On the premise of considering data and energy requirements, a node trust model based on interaction factors and private factors is established, the trust model calculates trust values of nodes through two kinds of trust, namely private trust and interaction trust, wherein the interaction between the node concerned by the private trust and the neighbor is realized through the past records sensed by the node concerned by the private trust and the residual energy of the node concerned by the interaction trust. The trust model can identify fault nodes in the network, reduce the influence of the fault nodes on data acquisition and select trust routes for accurate data transmission.
(1) Abstracting out interactive factors
During the interaction process, interaction factors (including interactive communication effectiveness, interactive data similarity and interactive clock consistency) are fused in a cluster head based on the attributes of node interaction behaviors, and are called communication, data and clock.
(2) Abstracting out private factors
The private factors of a single node include the stability of the node perception data and the residual energy of the node within a certain period of time Δ t. The data factor is related to the correctness of the data, and the energy factor is related to the working capacity of the node. When the model is constructed, a reward coefficient of correction data and a penalty coefficient of error data are set, and data factors are quantized. Energy is quantified in terms of initial energy and residual energy, which determines whether a node is still operating.
(3) And constructing interactive trust based on the interactive factors and private trust based on the private factors.
(4) And detecting the lost message node.
The invention discloses a sensor node in a wireless sensor network, which is vulnerable to internal and external attacks, and a password solution can successfully resist the external attacks but can fail under the internal malicious attacks. Therefore, by means of evaluation on the trust degree of the sensor nodes, the distrusted nodes need to be separated to resist internal attack. In order to meet the requirements of effective data transmission and energy saving of wireless sensor nodes, the invention provides a trust model of a wireless sensor network, which calculates the comprehensive trust value of the nodes by measuring the private trust and the interactive trust of the nodes. History perception records of the private trust attention node and the nature (such as residual energy) of the private trust attention node, and interaction behaviors of the interaction trust attention node and neighbors of the interaction trust attention node. By applying the trust model, fault nodes, event nodes and normal nodes in the network can be distinguished. And the decision is further guided, the influence of the fault node on data acquisition is reduced, and trust route reference is provided for stable, effective and accurate data transmission.
Drawings
FIG. 1 depicts a positive and negative matrix of interaction factor weights;
fig. 2 depicts a flow of detecting a distrusted node.
Detailed Description
The method for establishing the trust model of the wireless sensor network comprises the following steps:
1. constructing an interactive communication factor
The communication in the interaction factor is defined as: the continuous effective communication rate in the time of delta t is as follows:
Figure BDA0002756705260000041
wherein s isi，k(Δ t) is the number of successful communications between nodes i and j at Δ t, fi，j(Δ t) is the number of communication failures between nodes i and j at time Δ t. When the number of failures is greater than the number of successes, the two nodes i and j are considered to be untrusted. By dividing by
Figure BDA0002756705260000051
Causing the communication factor to drop dramatically. In particular, if fi，j(Δ t) 0, setting DCTi，j(Δt)＝100。
2. Constructing interactive data factors
The data in the interaction factors are defined as: continuous data similarity rate over time Δ t, i.e.
Figure BDA0002756705260000052
Wherein
Figure BDA0002756705260000053
ci，j(Δ t) is the number of times node i and j are similar in data over the time Δ t, di，j(Δ t) is the number of times the data are dissimilar. x is the number ofiIs the perception data of node i, especially if di，j(Δ t) is 0, then DST is seti，j(Δt)＝100。
3. Constructing an interaction time factor
The time in the interaction factor is defined as: the continuous clock synchronization rate in Δ t time, namely:
Figure BDA0002756705260000054
wherein T isiIs the sensing time of node i.
4. Constructing an interactive trust model
In computing trust values for interactions between nodes, to describe the importance of relationships and factors in the model, a weighted trust model is used to implement:
Figure BDA0002756705260000055
wherein, TIRepresenting the trust of interaction between two nodes, yiIs the ith factor with a weight value of alphai. Here, yiRepresenting 3 interaction factors. Since α i is the weight of the factor, its value should show that the factor is at TIThe weight of each factor is determined by using a positive and inverse matrix, and as shown in fig. 1, the right feature root method calculates a weight vector as follows: (communication, data, time) — (0.5869, 0.3238, 0.0893).
5. Constructing a private trust model
In a WSN, the private trust of a node will depend on its historical operation. Private trust should be "punished" when the perception data deviates from the mean; when the perception data is continuously correct, "rewards" are given. The private trust value is shown in the formula:
Figure BDA0002756705260000056
wherein, theta1+θ2+θ3+θ 41. F represents the ratio of consecutive perceptually identical data with values varying between 0 and 1, TP-1The node is a trust value in the previous round of the node, D is the deviation of the sensing data relative to the fusion value, R is the number of misreadings, and E is used for measuring the residual energy of the node so as to judge that the node can be found by a parent node of the node. Theta2And theta3Respectively, are reward and punishment coefficients, the values of which vary between 0 and 1. E is composed of
Figure BDA0002756705260000061
Derivation, where Er is the residual energy and Ei is the initial energy. When in use
Figure BDA0002756705260000062
E is-1 below the threshold that can support it for normal transmissions, otherwise E is 0. When in use
Figure BDA0002756705260000063
When the node is equal to 0, the node can be judged to be exhausted and can not work normally, and the node is deleted from the network.
When T ispBelow the threshold, the node will be considered as a failed node and should be removed from the network. To be in contact with TIMaintaining a consistent magnitude, TpMultiplying by 100 yields an integer value.
6. Detecting a lost message node
Through the trust model, a trust value is added to the node, and the detection of the fault node and the abnormal point in the data aggregation process can be deduced according to the trust value. If the private trust value of the node is above the threshold, then it is considered a normal node. Conversely, the node may be an isolated point or an abnormal node, and may be identified by the interactive trust value. If its interaction trust value is above the threshold and its private trust value is lower, then the node is a node at the edge of the event area (e.g., fire area), otherwise it is a failed node. The algorithm flow is shown in fig. 2.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1. A method of establishing a trust model for a wireless sensor network, the method comprising the steps of:
(1) abstracting out interaction factors;
(2) abstracting out private factors;
(3) and constructing an interactive trust model based on the interactive factors and a private trust model based on the private factors.
2. The method of claim 1, wherein the interaction factors comprise interaction communication effectiveness, interaction data similarity, and interaction clock consistency.
3. The method of claim 1, wherein the private factors include stability of node-aware data and remaining energy of the node for a certain period of time Δ t.
4. A method of establishing a trust model for a wireless sensor network, the method comprising the steps of:
(1) constructing an interactive communication factor
The communication in the interaction factor is defined as: continuous effective communication rate in delta t time, i.e.
Figure FDA0002756705250000011
Wherein s isi，k(Δ t) is the number of successful communications between nodes i and j at Δ t, fi，j(Δ t) is the number of communication failures between nodes i and j at time Δ t, when the number of failures is greater than the number of successes, the two nodes i and j are considered to be untrusted;
(2) constructing interactive data factors
The data in the interaction factors are defined as: continuous data similarity rate over time Δ t, i.e.
Figure FDA0002756705250000012
Wherein
Figure FDA0002756705250000013
Wherein, ci，j(Δ t) is the number of times node i and j are similar in data over the time Δ t, di，j(Δ t) is a data phaseAnd similar times. x is the number ofiIs the sensing data of node i;
(3) constructing an interaction time factor
The time in the interaction factor is defined as: the continuous clock synchronization rate in Δ t time, namely:
Figure FDA0002756705250000021
wherein T isiIs the sensing time of node i;
(4) constructing an interactive trust model
When calculating the trust value of the interaction between the nodes, the method uses a weighted trust model to realize that:
Figure FDA0002756705250000022
wherein, TIRepresenting the trust of interaction between two nodes, yiIs the ith factor with a weight value of alphai；
(5) Constructing a private trust model
The private trust value is shown in the formula:
Figure FDA0002756705250000023
wherein, theta1+θ2+θ3+θ4F denotes the ratio of consecutive perceptually identical data with values varying between 0 and 1,
TP-1is the trust value in the previous round of the node,
d is the offset of the perceptual data with respect to the fused value,
r is the number of times of misreading,
e is used to measure the node residual energy, thereby judging that it can be found by its parent node,
θ2and theta3Respectively, are reward and punishment coefficients, the values of which vary between 0 and 1.
5. The method of claim 4, wherein the step of removing the metal layer is performed in a batch processIn step (1), if fi，j(Δ t) 0, setting DCTi，j(Δt)＝100。。
6. The method of claim 4, wherein in step (2), if d is greater than di，j(Δ t) is 0, then DST is seti，j(Δt)＝100。
7. The method of claim 4, wherein in step (5), E is represented by
Figure FDA0002756705250000024
Derivation of, wherein ErIs the residual energy, EiIs the initial energy of the beam of light,
when in use
Figure FDA0002756705250000025
When the value is lower than a threshold value capable of supporting normal transmission, E is-1, otherwise, E is 0;
when in use
Figure FDA0002756705250000026
When the node is equal to 0, the node can be judged to be exhausted and can not work normally, and the node is deleted from the network.
8. The method of claim 4, wherein in step (5), when T ispBelow the threshold, the node will be considered as a failed node and should be removed from the network.
9. A method of detecting a distrusted node, the method comprising:
if the private trust value of the node is higher than the threshold value, the node is regarded as a normal node, otherwise, the node is an isolated point or an abnormal node and can be identified by using the interactive trust value, if the interactive trust value is higher than the threshold value and the private trust value is low, the node is a node positioned at the edge of the event area, otherwise, the node is a fault node.
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