CN110784843A - Cluster forming method for large-scale wireless sensor network - Google Patents

Cluster forming method for large-scale wireless sensor network Download PDF

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CN110784843A
CN110784843A CN201911070102.XA CN201911070102A CN110784843A CN 110784843 A CN110784843 A CN 110784843A CN 201911070102 A CN201911070102 A CN 201911070102A CN 110784843 A CN110784843 A CN 110784843A
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cluster head
node
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陶洋
周远林
李正阳
杨柳
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Chongqing University of Post and Telecommunications
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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
    • 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
    • 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
    • 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

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Abstract

The invention discloses a cluster forming method for a large-scale wireless sensor network, which comprises the following steps: 1) initializing a network; 2) candidate cluster head election, each node in the network generates a random number, and then the random number is compared with a preset threshold value to determine whether the node becomes a candidate cluster head; 3) non-uniform clustering, wherein three factors of candidate cluster head nodes are processed by a fuzzy system, and the radius of unequal clusters is calculated; 4) according to the comparison between the comprehensive trust value of the candidate cluster head nodes and a set trust threshold value, the screening of the safe cluster heads is completed; 5) and calculating clustering factors of the candidate cluster head nodes according to the node trust value, the node density and the distance to complete the final cluster head selection. The invention adopts a non-uniform clustering structure, and cluster head nodes dynamically adjust the radius of a cluster according to the self condition; and introducing a trust value obtained by adopting a trust mechanism into cluster head election and node clustering. The method has obvious improvement in the aspects of balancing network energy consumption, ensuring routing safety, prolonging the life cycle of the network and the like.

Description

Cluster forming method for large-scale wireless sensor network
Technical Field
The invention relates to the technical field of communication, in particular to a clustering routing method of a wireless sensor network.
Background
Since wireless sensor networks are often deployed in harsh external environments and the wireless channel is completely open, they are more vulnerable to various attacks. The traditional encryption and authentication technology can only resist external attacks, once a node breaks through the defense line, the node becomes a malicious node in the network, attacks are then initiated on the network, and serious damage is caused to the network. Therefore, how to realize the safe and reliable transmission of internal data is necessary to ensure the safety of data in the route. The energy limitation is another important characteristic of the wireless sensor network, because the nodes in the wireless sensor network usually supply energy through the battery, the nodes cannot be charged in time, and once the power consumption is finished, the nodes cannot participate in the network. Therefore, how to save the node energy consumption to prolong the overall service life of the network is another problem to be considered in the design process of the routing protocol. The present invention can solve the above problems well.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provides a cluster forming method facing a large-scale wireless sensor network, wherein the method adopts a non-uniform cluster structure in a clustering stage, the radius of a cluster is calculated by a fuzzy logic system according to the distance from a cluster head to a base station, the node degree and the residual energy, and the cluster head node dynamically adjusts the radius of the cluster according to the self condition so as to save the self energy; in the trusted cluster establishing stage, the trust value obtained by adopting a trust mechanism is introduced into the cluster head election and node clustering stages, so that the network security is improved.
The technical scheme adopted by the invention for solving the technical problems is as follows: a cluster forming method facing a large-scale wireless sensor network comprises the following steps:
step 1: initializing a network;
in the initialization phase, the BS sends a "start" packet to the sensor nodes in the network with a transmission power broadcast that can cover the entire monitoring area. After receiving the data packet, each sensor node estimates the distance from the sensor node to the BS according to the strength of the received signal. Each sensor node will then broadcast an "initial" packet within its broadcast distance, including its ID and distance to the BS. After receiving the initial data packet, the node constructs a local neighbor node table by analyzing the data packet, wherein the local neighbor node table comprises an ID number, a distance to a BS, a distance between nodes and an initial trust value of the node.
Step 2: candidate cluster head election, each node in the network generates a random number, and then the random number is compared with a preset threshold value to determine whether the node becomes a candidate cluster head;
and step 3: non-uniform clustering, processing three factors of candidate cluster head nodes by a fuzzy system, and calculating radius R of unequal clusters c；
And obtaining the competition radius of the candidate cluster head through a fuzzy inference system according to the residual energy of the nodes, the node degree and the distance from the node to the BS. The fuzzy inference system comprises three parts of fuzzification, fuzzy inference and deblurring processing:
(1) fuzzification is to convert input variables of accurate values into corresponding fuzzy sets;
(2) fuzzy inference is to map the fuzzy set of input variables to the fuzzy set of radii according to fuzzy rules;
(3) deblurring is the conversion of a set of radius ambiguities into a specific radius exact value.
And 4, step 4: according to the comparison between the comprehensive trust value of the candidate cluster head nodes and a set trust threshold value, the screening of the safe cluster heads is completed;
and 5: and calculating clustering factors of the candidate cluster head nodes according to the node trust value, the node density and the distance to complete the final cluster head selection.
Compared with the prior art, the invention has the following beneficial technical effects:
the method adopts a non-uniform clustering structure in a clustering stage, the radius of a cluster is calculated by a fuzzy logic system according to the distance from a cluster head to a base station, the node degree and the residual energy, and the radius of the cluster is dynamically adjusted by the cluster head node according to the self condition, so that the self energy is saved; in the trusted cluster establishing stage, the trust value obtained by adopting a trust mechanism is introduced into the cluster head election, node clustering and inter-cluster routing stages, so that the network security is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a diagram of a fuzzy inference system.
FIG. 3 is a graph of membership functions for fuzzy sets of input variables.
Fig. 4 is a graph of cluster radius membership functions for cluster head nodes.
Table 1 is a fuzzy rule table.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
As shown in fig. 1, the present invention provides a cluster forming method for a large-scale wireless sensor network, which is directed to a large-scale wireless sensor network and aims at internal data security and a cluster forming method for maximizing network lifetime, and the method includes the following steps:
step 1: initializing a network;
in the initialization phase, the BS sends a "start" packet to the sensor nodes in the network with a transmission power broadcast that can cover the entire monitoring area. After receiving the data packet, each sensor node estimates the distance from the sensor node to the BS according to the strength of the received signal. Each sensor node will then broadcast an "initial" packet within its broadcast distance, including its ID and distance to the BS. After receiving the initial data packet, the node constructs a local neighbor node table by analyzing the data packet, wherein the local neighbor node table comprises an ID number, a distance to a BS, a distance between nodes and an initial trust value of the node.
Step 2: candidate cluster head election, each node in the network generates a random number, and then the random number is compared with a preset threshold value to determine whether the node becomes a candidate cluster head;
at the beginning of each round, each node in the network generates a random number of [0,1], and if the random number is smaller than a preset threshold value, the node becomes a candidate cluster head node. Instead, the node becomes a normal node in the network. In the calculation process of the protocol threshold value T (n), the energy factor of the node is considered, and the preset threshold value T (n) is as follows:
Figure BDA0002260674680000031
wherein r represents the current turn, G represents the set of nodes which are not cluster heads in the current turn, p is the set proportion of cluster heads in the network, E residualIs the remaining energy of the node, E originalIs the initial energy of the node.
The nodes in the network only have the same initial energy at the initial moment, and along with the continuous operation of the network, the cluster head nodes not only need to receive and fuse the data in the cluster, but also need to forward other cluster head data, so that more energy is consumed, the residual energy of the cluster head nodes is less, and the residual energy of the nodes in the network is uneven. Therefore, when the threshold value t (n) is calculated, the ratio of the residual energy to the initial energy is taken into the calculation, the more the residual energy is, the greater the chance of becoming a candidate cluster head is, and conversely, the smaller the probability of selecting a candidate cluster head node is, the more the node energy consumption is balanced, and the network life cycle is prolonged.
And step 3: non-uniform clustering, processing three factors of candidate cluster head nodes by a fuzzy system, and calculating radius R of unequal clusters c；
And obtaining the competition radius of the candidate cluster head through a fuzzy inference system according to the residual energy of the nodes, the node degree and the distance from the node to the BS. The fuzzy inference system shown in fig. 2 comprises three parts of fuzzification, fuzzy inference and deblurring processing:
(1) fuzzification is to convert input variables of accurate values into corresponding fuzzy sets;
the distances from three input variables of accurate values to the BS, the residual energy and the density are converted into a spoken fuzzy set for subsequent fuzzy processing. Equivalent fuzzy linguistic variables should be used for the variables used in the method. Fuzzy language descriptions of cluster head node to BS distances are "far", "medium", "near"; fuzzy linguistic variables of the residual energy of the cluster head nodes are 'high', 'medium', 'low'; the fuzzy language of the node degree is three types of high, middle and low, and the fuzzy membership function of each input variable is shown in figure 2.
(2) Fuzzy inference is to map the fuzzy set of input variables to the fuzzy set of radii according to fuzzy rules;
fig. 4 shows a fuzzy set membership function corresponding to a cluster radius of a cluster head node. The output variable of the fuzzy inference system is the competition radius of the cluster head nodes, and the fuzzy language thereof has 7 types of ' small ', ' medium ', ' large ', ' medium large ', ' and ' large '. The fuzzy rule is specifically defined as shown in table 1.
(3) Deblurring is the conversion of a set of radius ambiguities into a specific radius exact value.
And performing deblurring processing by using a gravity center method to obtain a final accurate value. The calculation formula is as follows:
wherein Z is the output of the ambiguity resolution, Ci (Z) is the fusion membership function, Z is the output variable, and is the final accurate value R c。
And 4, step 4: according to the comparison between the comprehensive trust value of the candidate cluster head nodes and a set trust threshold value, the screening of the safe cluster heads is completed;
the method can calculate the comprehensive trust value of each node, and reasonably judges whether the node is safe or not according to the trust value of the node, thereby ensuring the safety of the network. Setting confidence threshold CT THAnd (4) carrying out safe cluster head screening, and ensuring the safety and reliability of the cluster head by the mode. Setting the comprehensive trust value of a certain cluster head node i as CT iAfter the node becomes a candidate cluster head node, the node broadcasts a CH _ MSG message which is a cluster head to surrounding neighbor nodes, the message content comprises the ID of the node, and the node residual energy E residual. After receiving the CH _ MSG message sent by the candidate cluster head, the common node firstly checks the comprehensive trust value CT of the candidate cluster head node i in the trust value table of the neighbor node iWhen the integrated trust value CT iCT less than confidence threshold THThen it is denied its request as a cluster head. Otherwise, storing the node i in the candidate cluster head set PrelimCH.
And 5: and calculating clustering factors of the candidate cluster head nodes according to the node trust value, the node density and the distance to complete the final cluster head selection.
And finishing the final cluster head selection according to the node trust value, the node density and the distance. If the cluster head node trust value stored in the common node trust table is higher, it indicates that the node is more trusted, and the node is safer to join the cluster. The method defines the node density as the number of nodes that survive within its contention radius Rc. If the density of the cluster head nodes is high, the number of neighbor nodes is large, so that the energy consumption of sending data to the cluster head nodes by the nodes in the cluster is reduced, and the energy consumption of the whole cluster is reduced. According to the wireless transmission model, the energy consumption of the nodes in the cluster has an important relation with the distance from the nodes in the cluster to the cluster head nodes, and the energy consumption is increased by the quadratic power of the distance in the communication range. And considering the distance from the common node to the cluster head node, defining a distance factor, wherein the distance factor is inversely proportional to the distance from the common node to the cluster head node and is directly proportional to the competition radius. Thus, the method is designed to cluster the factor CF iAs shown in equation (3):
CF i＝γ 1CT i+γ 2ND i+γ 3MD iformula (3)
Wherein, CT iRepresenting a trust factor, ND iDenotes the density factor, MD iDenotes a distance factor, gamma 1,γ 2,γ 3Weights for the confidence factor, density factor and distance factor, gamma 1,γ 2,γ 3> 0 and gamma 1+γ 2+γ 3＝1。
The method defines the density factor ND of the candidate cluster head node i iAs shown in formula (4), the number of neighboring nodes NodeDensity around the neighboring nodes can be found iIs in direct proportion.
Figure BDA0002260674680000051
Wherein, NodeDensity iAnd the number of neighbor nodes of the candidate cluster head node i is shown.
The method defines candidate clustersThe distance factor of the head node is shown in formula (5), and can be found to be inversely proportional to the distance of the nodes in the cluster and the competition radius R of the cluster head node cIs in direct proportion.
Figure BDA0002260674680000052
Wherein D is i,jRepresents the distance R between the candidate cluster head node i and the common node j cRepresenting the contention radius of the candidate cluster head node i.
The common node will select the largest clustering factor CF from the candidate cluster head set PrelimCH iAnd sends it a join _ MSG message, such as a cluster request, to choose to join the cluster. Meanwhile, the cluster head node receives a JoincH _ MSG message of a clustering request of a common node, queries a local trust table, judges whether the node is a malicious node or not, and refuses to cluster if the node is the malicious node. Otherwise, the cluster entering request of the node is agreed, and the cluster entering agreement message RelycH _ MSG is replied to the common node. And finishing the clustering of the trusted nodes.
The invention establishes a cluster forming method facing a large-scale wireless sensor network according to the characteristics of a distributed structure of the large-scale wireless sensor network, calculates the competition radius of a cluster head node through a fuzzy logic system according to the self residual energy, the node degree and the distance from the node to a BS, and dynamically adjusts the competition radius. And introducing the decision trust value into a cluster head election stage and a node clustering stage, and designing a cluster factor from the aspects of node safety, node energy consumption and balanced load energy consumption. The method can ensure the routing safety in the network, balance the network energy consumption and prolong the service life of the network.
TABLE 1 fuzzy rules
Figure BDA0002260674680000053
Figure BDA0002260674680000061

Claims (5)

1. A cluster forming method for a large-scale wireless sensor network is characterized by comprising the following steps:
step 1: initializing a network;
step 2: candidate cluster head election, each node in the network generates a random number, and then the random number is compared with a preset threshold value to determine whether the node becomes a candidate cluster head;
and step 3: non-uniform clustering, processing three factors of candidate cluster head nodes by a fuzzy system, and calculating radius R of unequal clusters c；
And 4, step 4: according to the comparison between the comprehensive trust value of the candidate cluster head nodes and a set trust threshold value, the screening of the safe cluster heads is completed;
and 5: and calculating clustering factors of the candidate cluster head nodes according to the node trust value, the node density and the distance to complete the final cluster head selection.
2. The method as claimed in claim 1, wherein in the step 2, when the threshold value T is calculated, a ratio of the residual energy to the initial energy is included in the calculation, and the more the residual energy becomes, the greater the probability of becoming the candidate cluster head is, the more the node energy consumption is balanced, and the network life cycle is prolonged.
3. The method as claimed in claim 1, wherein in step 3, a fuzzy inference model for the cluster head competition radius is established, and the cluster head nodes dynamically adjust the cluster radius according to their own conditions, so as to save their own energy.
4. The cluster forming method for the large-scale wireless sensor network according to claim 1, wherein in the step 4, cluster head election is introduced by using a trust value obtained by a trust mechanism, so that the security of candidate cluster head nodes is improved.
5. The method for forming clusters oriented to the large-scale wireless sensor network according to claim 1, wherein in the step 5, clustering factors are designed based on node safety, node energy consumption and balanced load energy consumption, so as to realize safe cluster head and safe and reliable internal data transmission and prolong the life cycle of the network.
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Application publication date: 20200211