CN110839244A - Credible data collection method based on node trust value virtual force - Google Patents
Credible data collection method based on node trust value virtual force Download PDFInfo
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- H04W12/12—Detection or prevention of fraud
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- H04W28/00—Network traffic management; Network resource management
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- H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
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- 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
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Abstract
The invention relates to a credible data collection method based on node trust value virtual force, in the application of data collection of the Internet of things, firstly, a node trust evaluation algorithm (including direct trust and indirect trust) is designed according to the relationship between nodes, and the quantitative trust value of the nodes is calculated; then mapping the trust value of the node into physical force borne by the node, giving attraction force to the trusted node and giving repulsion force to the untrusted node; simulating the moving initial path into a magnetic soft rope, wherein the initial path moves under the action of the joint force to finally generate a credible data collection path with higher credibility; the credible cluster head nodes are sequentially accessed within a limited moving distance through the moving edge nodes, credible sensing data are collected and sent to surrounding base stations or directly applied to upper-layer users, and the purposes of credible system application and decision making are achieved.
Description
Technical Field
The invention belongs to the field of information security of large-scale wireless sensor networks, and particularly relates to a credible data collection method based on node trust value virtual power.
Background
The rapid development of the internet of things (IoT) and mobile applications puts more stringent requirements on cloud infrastructure and underlying wireless sensor networks, such as system security, ultra-low latency, network energy consumption and reliability, etc. These stringent requirements drive the demand for highly localized services near the network edge of the user. Therefore, Mobile Edge Computing (MEC) has come to be a distributed open platform that merges network, computing, storage, and application core capabilities at the network edge near the source of the object or data, and can provide edge intelligent services nearby.
The data collected by the underlying sensor network is the foundation of the internet of things system and the foundation of all applications, but the data collected by the sensors in the network is not credible in the real situation. The sensor nodes are randomly deployed in unattended areas and severe environments to perform various complex tasks and play an important role in various fields. Such as battlefield surveillance, smart cities, intelligent medical surveillance, intrusion detection and emergency response. Due to the complex environment of the underlying sensor network, the network is more vulnerable, resulting in invalid and even misleading data being collected by the sensors, while less than 49% of the data is valid and trustworthy. This will make upper layer data protection and application silent if the data collected by the sensors in the network is itself problematic and untrusted. Network security is intended to protect network systems or network resources from various types of attacks. In the underlying WSN, attacks can be divided into two categories: internal attacks and external attacks. Although the security mechanisms of the encryption authentication and routing protocols can effectively resist external attacks, the security mechanisms have little effect on internal attacks, and the existing documents and researches show that the internal attacks of the underlying network of the internet of things are far more harmful than the external attacks. Trust evaluation is an effective and lightweight method for dealing with attacks on malicious nodes inside the nodes, and is an important component of computer security. Meanwhile, in the data acquisition research of the existing internet of things system, the used high-efficiency mobile data acquisition devices are all common nodes at the bottom layer, and the computing capacity, the storage capacity, the communication capacity and the energy are very limited. In addition, the traditional mobile data collector can visit most of the bottom nodes in the data collection process, so that the delay is high, the energy consumption of the nodes is high, and the collected data is mostly invalid data from malicious nodes.
Under the background, by introducing the idea of an edge computing mode, a mobile node with strong functions and fixed nodes such as traditional sink nodes and base stations form an edge node layer, and a credibility virtual power-based credibility data collection method is provided. According to the method, factors of credibility of the sensor nodes are comprehensively considered on the basis of a traditional mobile data collection method, and credible data collection is performed on an Internet of things system by fully utilizing local computing and storing capacity of the edge nodes based on credible evaluation of the nodes.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a credible data collection method based on the node trust value virtual force.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a credible data collection method based on node trust value virtual force comprises the following steps:
s10, obtaining the trust value of the node;
s20, determining a credible cluster head node based on the obtained trust value;
and S30, mapping the trust value of the node to the virtual acting force applied to the node so as to push the initial path to move, and generating a data collection path with higher reliability.
Preferably, the S10 specifically includes:
s101, judging whether the distance from the current node to the cluster head node is within a preset communication range of the node or not, if so, calculating a direct trust value of the current node, and taking the direct trust value obtained by calculation as a trust value of the node; if not, go to S102;
s102, judging whether the sum of the distance from the current node to any other node in the cluster and the distance from the other node to the cluster head node is within a preset communication range of the node, if so, marking the other node as a neighbor node of the current node, calculating an indirect trust value of the current node, and taking the calculated indirect trust value as the trust value of the node.
Preferably, the direct trust value is expressed as follows:
wherein the content of the first and second substances,
expressed as the total direct trust value of node i and node j; omegacom、ωl、ωeAnd ωPRespectively representing the weight, omega, given by communication trust, distance trust, energy trust and packet loss rate trustcom+ωl+ωe+ωP＝1；TcomRepresenting a node communication trust value; t islRepresenting a node location trust value; t iseRepresenting a node energy trust value; t ispRepresenting a node packet loss rate trust value;
node communication trust value TcomThe calculation method of (2) is as follows:
Tcom＝ωoldd×Toldd+ωnewd×Tnewd，
wherein, Tnewd＝S/C；ωoldd+ωnewd＝1；ωolddAnd omeganewdRespectively representing the old and new trust value weights of the node, TolddIndicating old trust value, initialization phase setting Toldd＝0，TnewdRepresenting a new trust value; t isoldd、Tnewd、ωolddAnd ωnewdIs a variable amount; s represents the number of successful communications; c represents the total number of communications;
node location trust value TlThe calculation method of (2) is as follows:
Tl＝1-Ds/R
wherein D isSRepresenting the distance between the node and the cluster head node, and R representing the communication range of the node;
node energy trust value TeThe calculation method of (2) is as follows:
Te＝Ec/Ei
wherein E isiRepresenting the initial energy of the node, EcRepresents the remaining energy;
node packet loss rate trust value TpThe calculation method is as follows:
wherein, PsIndicating the number of data packets transmitted, PrIndicating the number of packets received.
Preferably, the indirect trust value is expressed as follows:
wherein m represents the number of neighbor nodes,
representing the weight; (T)X,Y)ARepresenting the trust value of the node X to Y after being transmitted by the neighbor node A;
TA,Yrepresenting a direct trust value for nodes a and Y,a direct trust value sum representing a recommended node;
(TX,Y)A＝TX,A×TA,Y
wherein, TX,ARepresenting the direct trust values of nodes X and A; t isA,YRepresenting a direct trust value for nodes a and Y.
Preferably, the S20 specifically includes:
s201, a routing protocol is adopted to perform node clustering in the initial stage of the network to obtain a plurality of clusters, namely a plurality of cluster head nodes are generated;
s202, after the network works for a period of time, all nodes in the cluster generate parameters required for calculating trust values, and the cluster head node calculates the trust values of all nodes in the cluster according to the generated parameters;
s203, after the cluster head nodes compress and fuse the data, sending the trust value data and the identity ID of each node in each cluster to a base station;
s204, the base station carries out statistical analysis on the data of the nodes including the cluster head nodes according to the received data to generate a trust value table of each node;
s205, in the next round of cluster head node election, nodes with high trust values are directly elected as cluster head nodes, and the cluster head nodes sequentially and circularly work.
Preferably, the S30 specifically includes:
giving an initial path, and continuously moving points on the initial path under the action of the resultant force of surrounding nodes; the action points of the force of the nodes around the moving path are projection points of the nodes on a path curve, the direction of the attraction force points to the nodes from the action points, and the direction of the repulsion force points to the opposite direction of the nodes, so that the moving edge nodes can always move along a route area with the highest credibility under the resultant force of the credible virtual forces mapped by the node trust values;
defining a node with a trust value greater than 0 as a trusted node, and endowing attraction; defining the node with the trust value less than 0 as an untrusted node and endowing the node with repulsive force; meanwhile, the acting force of the node is in direct proportion to the trust value, the larger the trust value is, the larger the attraction force is, and the smaller the absolute value is, the smaller the repulsion force is, for the untrusted node.
Preferably, the virtual force applied to the projection point of the moving path node is represented as follows:
wherein p and q respectively represent the number of credible nodes and incredible nodes on the same straight line position; fa、FrRespectively representing the attraction force and the repulsion force of a single node, and the values are equal to the trust values; k is an adjustment coefficient; fvThe virtual force is a virtual force applied to a projection point on the path, and the virtual force comprises a resultant force of an attractive force and a repulsive force.
After the scheme is adopted, the invention has the beneficial effects that:
the invention discloses a credible data collection method based on node trust value virtual force, which aims at the problems that the traditional wireless sensor network only considers the influence of network energy and time delay in data collection and simultaneously ignores various internal attacks faced by nodes. By the method, more credible data can be collected while reducing the energy consumption of the nodes in various applications of the sensor, and the purpose of providing reliable guarantee for upper-layer user decision and data application is achieved; the method can be applied to the application with higher requirements on information safety and data reliability of a large-scale wireless sensor network.
The present invention is described in further detail with reference to the drawings and the embodiments, but the method for collecting trusted data based on node trust value virtual power is not limited to the embodiments.
Drawings
FIG. 1 is a flow chart of a method for collecting trust data based on a node trust value virtual power according to the present invention;
FIG. 2 is a schematic diagram of indirect trust transfer in accordance with the present invention;
FIG. 3 is a schematic diagram of the movement path nodal force effect of the present invention;
FIG. 4 is a diagram of a movement path under the action of a virtual force mapped based on a node trust value according to the present invention;
FIG. 5 is a sparse distribution path diagram of 13 nodes of the present invention;
FIG. 6 is a high density distribution path diagram of 30 nodes according to the present invention;
FIG. 7 is a network energy consumption graph under different trust value evaluation methods of the present invention;
FIG. 8 is a graph comparing changes in trust values under different trust evaluation methods of the present invention;
FIG. 9 is a graph of distances between different types of nodes and trusted data collection paths of the present invention;
fig. 10 shows the detection rate of network malicious nodes under different strategies according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described and discussed in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the application of the data acquisition of the Internet of things, a node trust evaluation algorithm (such as direct trust and indirect trust) is designed according to the relationship between nodes, and the quantitative trust value of the node is calculated by the existing algorithm. And mapping the trust value of the node into physical force borne by the node, and giving attraction force to the trusted node and repulsion force to the untrusted node. The moving initial path is simulated into a magnetic soft rope, the initial path moves under the action of the joint force, and finally a credible data collection path with higher credibility is generated. The credible cluster head nodes are sequentially accessed within a limited moving distance through the moving edge nodes, credible sensing data are collected and sent to surrounding base stations, and then the sensing data are handed to upper-layer users, so that the purposes of credible system application and decision making are achieved.
Referring to fig. 1, the present invention provides a method for collecting trusted data based on a node trust value virtual power, including:
s10, obtaining the trust value of the node;
s20, determining a credible cluster head node based on the obtained trust value;
and S30, mapping the trust value of the node into a virtual acting force applied to the node to push the initial path to move, and generating a data collection path with higher credibility.
Specifically, the detailed implementation steps are as follows.
Step 1): and establishing a direct trust model and calculating the direct trust value of the node. In network operation, an initial reliability (set as T) of a node is seti) And a confidence threshold delta for the node. The trust value can be represented in various ways, and the interval [0,10 ] is adopted in consideration of the limited storage space of the sensor node]Representing the trust value of the node. Unsigned integers occupy 1 byte and real numbers 4 bytes, so 0,10 is used]75% of memory space can be saved, and data transmission between nodes is correspondingly reduced.
Defining a trust value rule that uses an interval to represent the trust range of a node, wherein the interval is limited to 0,10]The initial trust value of the node is set to 5. The trust value at a certain moment is denoted as TcWhen T isc0, meaning that the node is completely untrusted; t isc10 indicates that the node is fully trusted.
The direct trust value of the node is obtained by evaluating four fine-grained parameters of a communication trust value, a position trust value, a packet loss rate trust value and an energy trust value of the node as follows.
A. Communicating trust values
Two in the networkThe adjacent nodes communicate with each other, the times S of successful communication and the total times C of communication and the communication trust value T between the nodescomThe relationship of (1):
Tcom＝ωoldd×Toldd+ωnewd×Tnewd，Tnewd＝S/C
and omegaoldd+ωnewd＝1 (1)
Wherein, ω isolddAnd omeganewdRespectively representing the old and new trust value weight of the node, TnewdAnd TolddRespectively list trust value, initialization stage setting Toldd＝0，ωolddAnd ωnewdAre variable amounts.
B. Location trust value
When the node position is closer to the cluster head node, the probability of successfully transmitting the data packet to the node is higher, and the consumed energy is lower. The calculation of the node location trust value is represented as:
Tl＝1-Ds/R (2)
wherein, TlRepresenting a node location trust value, DSIndicating the distance of the node from the cluster head node, and R indicating the communication range of the node.
C. Packet loss rate trust value
In a wireless communication environment, the packet loss rate indirectly reflects the quality of a link, and a higher packet loss rate indicates a poorer link quality. In order to determine whether the communication behavior of the node on the data transmission link is abnormal, the packet loss rate trust value of the node needs to be calculated. The number of transmitted data packets is PsThe number of received data packets is PrIf the packet loss rate is greater than the threshold, the packet loss rate trust value is:
D. energy trust value
Energy as a key index of a sensor node reflects the service life of the node, and the initial energy of the node is set as EiThe residual energy is EcThen the energy confidence value calculation of the node can be expressed as:Te＝Ec/Ei。
Therefore, the trust evaluation of this section on the node can be expressed as:
wherein the content of the first and second substances,
expressed as the total direct trust value, ω, of node i and node jcom，ωl，ωe，ωPRespectively representing the weight given by communication trust, distance trust, energy trust and packet loss rate, and simultaneously having omegacom+ωl+ωe+ωP＝1。
Step 2): and establishing an indirect trust transfer model and constructing a node trust value calculation framework. When the adjacent nodes can not obtain the direct trust value, the nodes need to obtain the indirect trust value by relying on the history of the interaction with the trust object. In the indirect trust calculation, the trust value of one node may be recommended by a plurality of nodes, and because the trust values of the nodes are different and the recommended nodes may be malicious nodes or manually manipulated, the recommended nodes need to be given weight values, and the weight values are defined as
Referring to FIG. 2, to compute the trust of node X at node Y, an indirect trust value is obtained through the trust passing of the neighboring nodes of X and Y. For example the following formula:
(TX,Y)A＝TX,A×TA,Y(5)
wherein (T)X,Y)AIs the trust value of node X to Y after passing through the neighbor node a.
The weight of the recommended node is proportional to the direct trust value of the adjacent node, namely:
wherein m represents the number of neighbor nodes, TA,YRepresenting a direct trust value for nodes a and Y,
representing the direct trust value sum of the recommended nodes.
The indirect trust of X obtained at node Y is:
we use equation (5) to compute the trust value of a node when it can directly obtain the direct trust value, otherwise we use equation (7) to compute the trust value of the node based on the direct trust value.
Step 3): and calculating the trust value of the node by using the step 1) and the step 2). The trust value evaluation algorithm of the node calculates the node communication trust value, the position intimacy, the packet loss rate and the energy remaining four parameters to finally obtain the trust evaluation value of the node. The input of the algorithm is related variables related to the calculation of the direct trust value and the indirect trust value, the node energy and the data packet sent and received, and the output is the final evaluation trust value of the node.
First, for any given stable network, a trust value (including cluster head nodes) is computed for each node. The method mainly comprises the steps of calculating parameters such as communication trust values, position intimacy, packet loss rate and energy surplus of the sensor nodes, and obtaining direct trust values of the area nodes by combining weights. In the calculation, judgment is carried out firstly to see whether the node is a neighbor node of the node or not and whether the data packet received by the node at this time meets the requirement or not. And calculating the direct trust value of the node meeting the condition, otherwise, considering the indirect trust value.
The algorithm mainly comprises two steps: first, a direct trust value T is calculatedX,Y(trust value between node X and neighbor node Y), followed by TX,YBased on the relation of the node communication range, whether the direct trust value or the indirect trust relation is adopted to solve the trust value is determined, and finally the node trust value T is obtainedc。
And for nodes which are not in the own neighbor node list or are used as neighbor nodes, but the number of data packets received by the nodes at this time is less than a threshold value, indirect trust is also adopted to calculate the credible value of the nodes.
Step 4): and determining the credible cluster head node by using the obtained trust value. And based on the cluster head generated by the low-power-consumption self-adaptive routing protocol, the trust value is used as a condition for electing the cluster head node. The generation process of the cluster head node based on the trust value is as follows:
1) in the initial stage of the network, a routing protocol is adopted to perform node clustering to obtain a plurality of clusters, namely a plurality of cluster head nodes are generated.
2) After the network works for a period of time, each node in the cluster generates parameters required for calculating the trust value, and the cluster head node calculates the trust value of each node in the cluster according to the generated parameters.
3) After the cluster head node performs necessary compression and fusion on the data, only the trust value data and the identity ID of each node in each cluster are sent to the base station.
4) The base station carries out statistical analysis on the data of the nodes including the cluster head nodes according to the received data, and a trust value table of each node can be generated.
5) And in the next round of cluster head node election, directly electing the node with higher trust value as the cluster head node, and sequentially and circularly working.
Step 5): and mapping the trust value of the node into a virtual acting force borne by the node to push the initial path to move by using the trust values from the step 1) to the step 4), so as to generate a data collection path with higher credibility. Based on the trust evaluation of the nodes, the mobile edge node can avoid the untrusted nodes for mobile data collection. The path planning is carried out by considering not only a single node but also the overall trust condition of a region and simultaneously considering the requirement of data timeliness (moving to a region with high credibility as much as possible within a limited moving distance). The nodes are divided into different areas according to the belonged geographic areas or the clustering mode, the credible nodes are endowed with attractive force according to the evaluation of the credible values of the nodes, while the untrustworthy nodes are endowed with repulsive force, and the force is related to the credible values of the nodes. The path of movement is modeled as a magnetic cord that "pushes" the path of movement towards trusted areas and away from untrusted areas by the combined forces of attraction and repulsion.
According to the node trust value evaluation, the rules of the node trust value corresponding to the acting force and the planning path are described as follows:
defining a trust value Tc>0 is a credible node, and is endowed with attraction, TcIf less than 0, the node is an untrusted node, and repulsive force is given; simultaneously the magnitude of the node acting force and the trust value TcProportional ratio, TcGreater values for greater attraction, TcThe smaller the absolute value, the smaller the repulsive force.
The main objective is to give an initial path, and the point on the initial path moves continuously under the action of the resultant force of the surrounding nodes, so as to generate a moving path with higher reliability within a specified path length (or a specified time). The action points of the force of the nodes around the moving path are projection points of the nodes on a path curve, the direction of the attraction force points to the nodes from the action points, and the direction of the repulsion force points to the opposite direction of the nodes, so that the moving edge nodes can always move along a route area with the highest credibility under the resultant force of the credible virtual forces mapped by the node trust values. Referring to fig. 4, the structural diagram shows a simple trusted path generation process, where an initial mobile data collection path (simulated as a magnetic flexible rope) is given, points on the path continuously move under the action of the virtual force effect mapped by the trust values of surrounding nodes, so that the path is continuously updated, and finally a more trusted mobile collection path meeting the mobile distance limit is generated. And the mobile edge node moves along the trusted path from the selected starting point, and collects the data of the trusted node within a limited moving distance.
The acting forces of a plurality of nodes on the same side (or two sides) of the curve on the curve path are defined to be algebraically synthesized, and the direction is the direction of the attractive force or the repulsive force of the resultant force. Referring to fig. 3, the force action points of the nodes around the moving path are projection points of the nodes on the path curve, the direction of the attraction force is directed to the nodes from the action points, the direction of the repulsion force is directed to the opposite direction of the nodes, the acting forces of a plurality of nodes on the same straight line on the same side (or two sides) of the curve on the path are algebraically synthesized, and the direction is the direction of the attraction force or the repulsion force of the resultant force. The mobile edge node can always move along the route area with the highest credibility under the action of the credibility virtual force of the node trust value mapping.
The virtual force (including attractive force and repulsive force) applied to the projected point of the moving path node can be expressed as:
wherein p and q respectively represent the number of credible nodes and incredible nodes on the same straight line position; fa、FrRespectively representing the attraction and repulsion of a single node, numerically equal to the trust value Tc(ii) a k is an adjustment coefficient; fvThe resultant force on the projected point on the path.
In the algorithm 2, the new coordinates of the nodes are defined as two-dimensional coordinates, specifically, the new coordinates are calculated as addition and subtraction of horizontal and vertical coordinates, the action of the resultant force pushes the movement of the path to be finally reflected on the change of the coordinates, and the different magnitudes of the movement speed and the resultant force also influence the change speed of the coordinates.
In this embodiment, a simulation platform is constructed by using MATLAB R2018a, and performance evaluation and analysis are performed on the proposed trusted data collection algorithm. The simulation environment is set to be that 100 nodes are randomly deployed in an area of 300m × 300m, and 30 nodes are selected as cluster head nodes, it is assumed that the mobile edge nodes start to move at a constant speed from a selected starting point, and the moving speed and the communication radius are adjustable. The minimum energy threshold of a node is set to one-thousandth of the initial energy of the node, and the minimum packet reception threshold is two-thousandth of the threshold for node data generation. An initial path without intersection is set, and then a path with the maximum trust value is generated within the specified moving distance according to the virtual force algorithm. In our experiment we selected 13 reference points on the moving path to segment the initial path (virtual magnetic cord). Based on these reference points, the path moves by half a unit distance under a unit force according to the action of the virtual force resultant mapped by the calculated trust value of the node, and the movement path of the specified distance is obtained by running 10 time units, as shown in fig. 5.
As the length of the initial path and the number of nodes increase, the planned trusted data collection path also becomes complex, as shown in fig. 6. The initial path nodes are set to be 30 for experiment, and meanwhile, the distribution density of the nodes in the network is correspondingly increased.
It can be seen that the length of the newly generated path is smaller than that of the original path, and the new path is located closer to the trusted node. The comprehensive intuitive path generation experiment shows that the credible data collection method based on the virtual force can push the mobile path to a credible area and is far away from an incredible area, thereby achieving the purpose of efficiently collecting credible data.
To verify the validity of the trust evaluation algorithm, the present embodiment simulates an internal attack of the node (mainly selfish node). The performance of the algorithm is verified by deploying a certain proportion of malicious nodes in the network. As can be seen from fig. 7, as malicious nodes increase from 20% to 80%, the network energy consumption increases rapidly with the method named the ETRES trust evaluation system. Especially when the proportion of malicious nodes in the network reaches around 40%, the rate of energy consumption increases considerably with this approach. The energy consumption growth trend with the method named BTEM trust evaluation mechanism is very similar to ETRES, both much larger than the VFDC method proposed by the present invention. Meanwhile, through experimental results, the energy consumption of the VFDC method provided by the invention is only slightly increased under different malicious node proportion conditions, but the overall consumption is stabilized below 45. The method has the advantages of good robustness and stability, and capability of effectively resisting malicious attacks from the interior of the node. From fig. 8, it can be seen that when the proportion of malicious nodes in the network reaches about 50%, the trust value of the node is rapidly reduced, which also reflects the credibility of the trust evaluation method. The results of the method adopting ETRES and the method adopting BTEM are similar, and when the ratio of network malicious nodes is high, the trust value of the nodes is reduced to a neutral level (the trust value is 5 which is also the initial value of the nodes). However, compared with the other two methods, the VFDC method provided herein has a significant advantage when the malicious node ratio is about 60.
To intuitively illustrate the validity of the proposed trusted data collection method from the data, the distance between a point on the trusted path and the trusted and untrusted nodes at different times (rounds) is calculated, as shown in fig. 9. As the initial path iterates (the experimental time increases), the distance of the newly generated trusted path from the untrusted nodes increases rapidly. Although the curve from the trusted node is relatively flat, the distance is slowly reduced. Through the experiment, the credible data collection method based on the virtual force can push the moving path to a credible area and move away from an incredible area through the action of force. As can be seen from fig. 10, as the number of iterations of the movement path increases, the identification rate of the trust evaluation method for the malicious node also increases accordingly. The method using CTRUST has a similar rate of identifying malicious nodes to the VFDC method proposed herein, but is slightly lower, especially in the early stages of network operation, and the detection rate is much lower than that of the method herein. Compared with the BTEM method, the method provided by the invention has a much higher detection rate of the malicious node.
The present embodiment tests network energy consumption using different methods for different network architectures and deployments. As the number of nodes (cluster heads) increases from 10 to 60, the energy consumption of the network nodes increases. The node using the BTEM method has the fastest average energy consumption, and the CTRUST method follows the node, and the growth rate is also the largest. However, when the network structure based on the trusted cluster head nodes is adopted, the energy consumption of the network is the minimum and the average value is the most stable, so that the energy consumption of each node of the network can be balanced, and the life cycle of the network is effectively prolonged.
To sum up, in one aspect, the invention provides a multi-dimensional trust evaluation method to comprehensively evaluate the trust value of a node, and the trust model includes direct trust and indirect trust, parameters (such as energy, processing capability and the like) related to the state of the node and the behavior of the node participating in interaction. And providing detailed indexes such as a communication trust value, position intimacy, packet loss rate, energy surplus and the like in a multidimensional trust model to quantify the trust value of the node, so that malicious attacks from the interior of the node can be effectively resisted. Meanwhile, an integer interval is adopted for representing in the quantification of the trust value, so that the memory space of the network node can be well saved. On the other hand, the invention provides a novel credible path generation method, which combines the trust value of the node with the action of the physical neutral force, so as to generate a data collection path with higher credibility by utilizing the action of the resultant force of the virtual force mapped by the trust value of the node. The moving path is simulated into a magnetic soft rope, the reliable node is endowed with attraction force, the unreliable node is endowed with repulsion force, the moving path is pushed to a region with higher reliability and is far away from the unreliable region through the resultant force of the interaction of the attraction force and the repulsion force, the moving distance is shortened, the network life cycle of the node is prolonged, and therefore the purpose of efficiently collecting reliable data is achieved.
The above is only one preferred embodiment of the present invention. However, the present invention is not limited to the above embodiments, and any equivalent changes and modifications made according to the present invention, which do not bring out the functional effects beyond the scope of the present invention, belong to the protection scope of the present invention.
Claims (7)
1. A credible data collection method based on node trust value virtual force is characterized by comprising the following steps:
s10, obtaining the trust value of the node;
s20, determining a credible cluster head node based on the obtained trust value;
and S30, mapping the trust value of the node to the virtual acting force applied to the node so as to push the initial path to move, and generating a data collection path with higher reliability.
2. The method for collecting trusted data based on virtual power of a node trust value according to claim 1, wherein the step S10 specifically includes:
s101, judging whether the distance from the current node to the cluster head node is within a preset communication range of the node or not, if so, calculating a direct trust value of the current node, and taking the direct trust value obtained by calculation as a trust value of the node; if not, go to S102;
s102, judging whether the sum of the distance from the current node to any other node in the cluster and the distance from the other node to the cluster head node is within a preset communication range of the node, if so, marking the other node as a neighbor node of the current node, calculating an indirect trust value of the current node, and taking the calculated indirect trust value as the trust value of the node.
3. The method for collecting the trusted data based on the virtual power of the node trust value according to claim 2, wherein the direct trust value is expressed as follows:
wherein the content of the first and second substances,
expressed as the total direct trust value of node i and node j; omegacom、ωl、ωeAnd ωPRespectively representing communication trustDistance trust, energy trust and packet loss rate trust, omegacom+ωl+ωe+ωP＝1；TcomRepresenting a node communication trust value; t islRepresenting a node location trust value; t iseRepresenting a node energy trust value; t ispRepresenting a node packet loss rate trust value;
node communication trust value TcomThe calculation method of (2) is as follows:
Tcom＝ωoldd×Toldd+ωnewd×Tnewd，
wherein, Tnewd＝S/C；ωoldd+ωnewd＝1；ωolddAnd omeganewdRespectively representing the old and new trust value weights of the node, TolddIndicating old trust value, initialization phase setting Toldd＝0，TnewdRepresenting a new trust value; t isoldd、Tnewd、ωolddAnd ωnewdIs a variable amount; s represents the number of successful communications; c represents the total number of communications;
node location trust value TlThe calculation method of (2) is as follows:
Tl＝1-Ds/R
wherein D issRepresenting the distance between the node and the cluster head node, and R representing the communication range of the node;
node energy trust value TeThe calculation method of (2) is as follows:
Te＝Ec/Ei
wherein E isiRepresenting the initial energy of the node, EcRepresents the remaining energy;
node packet loss rate trust value TpThe calculation method is as follows:
wherein, PsIndicating the number of data packets transmitted, PrIndicating the number of packets received.
4. The method for collecting the trusted data based on the virtual power of the node trust value according to claim 2, wherein the indirect trust value is expressed as follows:
wherein m represents the number of neighbor nodes,
representing the weight; (T)X，Y)ARepresenting the trust value of the node X to Y after being transmitted by the neighbor node A;
TA，Yrepresenting a direct trust value for nodes a and Y,
a direct trust value sum representing a recommended node;
(TX，Y)A＝TX，A×TA，Y
wherein, TX，ARepresenting the direct trust values of nodes X and A; t isA，YRepresenting a direct trust value for nodes a and Y.
5. The method for collecting trusted data based on virtual power of a node trust value according to claim 1, wherein the step S20 specifically includes:
s201, a routing protocol is adopted to perform node clustering in the initial stage of the network to obtain a plurality of clusters, namely a plurality of cluster head nodes are generated;
s202, after the network works for a period of time, all nodes in the cluster generate parameters required for calculating trust values, and the cluster head node calculates the trust values of all nodes in the cluster according to the generated parameters;
s203, after the cluster head nodes compress and fuse the data, sending the trust value data and the identity ID of each node in each cluster to a base station;
s204, the base station carries out statistical analysis on the data of the nodes including the cluster head nodes according to the received data to generate a trust value table of each node;
s205, in the next round of cluster head node election, nodes with high trust values are directly elected as cluster head nodes, and the cluster head nodes sequentially and circularly work.
6. The method for collecting trusted data based on virtual power of a node trust value according to claim 1, wherein the step S30 specifically includes:
giving an initial path, and continuously moving points on the initial path under the action of the resultant force of surrounding nodes; the action points of the force of the nodes around the moving path are projection points of the nodes on a path curve, the direction of the attraction force points to the nodes from the action points, and the direction of the repulsion force points to the opposite direction of the nodes, so that the moving edge nodes can always move along a route area with the highest credibility under the resultant force of the credible virtual forces mapped by the node trust values.
Defining a node with a trust value greater than 0 as a trusted node, and endowing attraction; defining the node with the trust value less than 0 as an untrusted node and endowing the node with repulsive force; meanwhile, the acting force of the node is in direct proportion to the trust value, the larger the trust value is, the larger the attraction force is, and the smaller the absolute value is, the smaller the repulsion force is, for the untrusted node.
7. The method for collecting the trusted data based on the virtual force of the node trust value according to claim 6, wherein the virtual force applied to the projected point of the node of the moving path is represented as follows:
wherein p and q respectively represent the number of credible nodes and incredible nodes on the same straight line position; fa、FrRespectively represent the attractive force of a single nodeA repulsive force, numerically equal to the confidence value; k is an adjustment coefficient; fvThe virtual force is a virtual force applied to a projection point on the path, and the virtual force comprises a resultant force of an attractive force and a repulsive force.
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