CN113596950A - Energy-balanced non-equilibrium clustering method for circular wireless sensor network - Google Patents

Energy-balanced non-equilibrium clustering method for circular wireless sensor network Download PDF

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CN113596950A
CN113596950A CN202110782534.4A CN202110782534A CN113596950A CN 113596950 A CN113596950 A CN 113596950A CN 202110782534 A CN202110782534 A CN 202110782534A CN 113596950 A CN113596950 A CN 113596950A
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cluster head
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energy
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CN113596950B (en
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林德钰
赵成坤
陈鑫浩
唐博
闵卫东
徐健锋
罗铭
李渭
欧阳浩
胡然
华鑫
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Nanchang University
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    • 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
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • 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
    • 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
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    • 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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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses an energy-balanced non-balanced clustering method for a round wireless sensor network, which aims to realize energy load balance of cluster head nodes and prolong the life cycle of the wireless sensor network; obtaining the optimal number of cluster head nodes on each layer by utilizing the load of the cluster head nodes and utilizing extensive theoretical derivation and energy calculation of the cluster head nodes; based on a fuzzy logic method, obtaining a cluster head node replacement mechanism; combining the two modes, providing a clustering method with balanced energy; by utilizing a simulation experiment, the effect of the algorithm on the improvement of the life cycle of the wireless sensor network is finally proved to be obvious.

Description

Energy-balanced non-equilibrium clustering method for circular wireless sensor network
Technical Field
The invention belongs to the field of wireless sensor networks, and particularly relates to an energy-balanced non-equilibrium clustering method for a circular wireless sensor network.
Background
A wireless sensor network is a network that has been widely used. In the past years, wireless sensor networks play an important role in environmental monitoring, target tracking, security maintenance, military defense, medical care and the like. Meanwhile, the wireless sensor network is used as a key technology, and contributes to hot topics in the academic and industrial fields, such as the internet of things, big data and the like.
A wireless sensor network is a network of thousands of miniature and inexpensive electronic devices, also known as sensor nodes. They have the ability to obtain the required data from the environment (e.g., temperature, humidity and light) and can send messages to other nodes over a wireless communication link. The ultimate goal of the wireless sensor network is to transmit the acquired data to the sink node. In practical applications, the sensor nodes have limited energy and are often deployed in harsh environments, and therefore, replacement of the sensor nodes is impractical.
In the academic field, various technologies have been developed to reduce the energy consumption of nodes to prolong the lifetime of the wireless sensor network, such as mobile relays and sink nodes, resource allocation relays designed across layers, and optimization algorithms. Among them, one of the most outstanding techniques is a clustering algorithm, which improves energy efficiency through multi-hop transmission. In the clustering protocol, the wireless sensor network is divided into different clusters, and each cluster consists of a cluster head node and corresponding cluster members. One sensor node in a cluster is selected as a cluster head, and other nodes in the same cluster are marked as cluster members. The function of the cluster members is to acquire valid data and transmit it to the cluster head. In addition to acquiring data, a cluster head may also need to collect data from its cluster members and transmit all data to other cluster heads that are closer to the sink node. Through the clustering algorithm, the sensor nodes with limited energy transmit effective data to the cluster heads instead of directly transmitting the effective data to the sink nodes, so that the total transmission energy overhead is reduced, and the life cycle of the network is effectively prolonged.
However, clustering algorithms are not perfect and one of the drawbacks is the "hot spot problem". For the clustering algorithm based on the same size cluster, the cluster head close to the sink node needs to transmit more data than the cluster head far away from the sink node, which means that the cluster head close to the sink node consumes more energy. Therefore, the distribution of the remaining energy is not uniform for the whole wireless sensor network, and the network will be divided into a plurality of parts prematurely, which is very disadvantageous for the data transmission of the wireless sensor network. However, in the clustering algorithm based on clusters of different sizes, the size of a cluster is different according to the difference of the positions of the clusters, and the cluster close to the sink node is usually smaller. By using the method, in the cluster close to the convergent node, the energy consumed by the cluster head for transmitting the data collected by the nodes in the cluster is greatly reduced, and more energy is distributed on the data transmission between the clusters. However, the existing clustering algorithm has shortcomings in energy efficiency and performance. Partial clustering algorithms can lead to randomness and uncertainty in energy efficiency and network performance. Furthermore, the "hot spot problem" is not effectively controlled in some clustering algorithms. Meanwhile, the mathematical calculation of some clustering algorithms is not accurate, and proper cluster head selection and replacement mechanisms are not utilized to balance energy consumption.
For the problems, the invention provides an energy-balanced non-equilibrium clustering method for a circular wireless sensor network, and the number of cluster heads suitable for each layer is obtained through extensive theoretical derivation and mathematical calculation; based on a fuzzy logic method, cluster head replacement is realized, and finally the purpose of prolonging the life cycle of the wireless sensor network is achieved.
Disclosure of Invention
The invention aims to solve the problems in the background art and provides an energy-balanced non-equilibrium clustering method for a circular wireless sensor network.
The technical scheme of the invention is an energy-balanced non-equilibrium clustering method for a circular wireless sensor network, which specifically comprises the following steps:
step 1, determining the distribution conditions of sensor nodes and sink nodes, and determining the layering of a wireless sensor network;
step 2, obtaining the total energy consumed by each layer of cluster head nodes;
step 3, calculating the interlayer cluster head proportion by using the same energy consumption of the cluster head nodes;
step 4, obtaining the number of the first-layer cluster head nodes by utilizing the minimum total energy consumption;
step 5, calculating the specific number of cluster heads and clusters on each layer by combining the number of cluster heads on the first layer;
step 6, selecting cluster heads and forming clusters;
step 7, data transmission in clusters and between clusters;
step 8, based on fuzzy logic, performing cluster head rotation;
and 9, circulating the step 7 and the step 8 until the wireless sensing network cannot work continuously.
It should be noted that, in step 1, the model of the wireless sensor network is shown in fig. 1. When the wireless sensor network is layered, firstly, a Cartesian coordinate system needs to be established by taking the sink node as a circle center. If the coordinate (x, y) of the current node, the node is at the second position
Figure BDA0003157643960000021
Layers, where r represents the fixed width of each layer. The value of the width r can be found in the literature: M.Xiaong, W.r.Shi, C.j.Jiang, and Y.Zhang, "Energy efficiency clustering for mapping life time of wireless sensor networks," Aeu-International Journal of Electronics and communicationsions,vol.64,no.4,pp.289-298,2010.
In step 2, first, the number C of clusters and cluster heads per layer needs to be obtainediThe calculation method is shown as formula I, wherein gammaiIndicating the size of the central angle corresponding to the cluster in the ith layer.
Figure BDA0003157643960000022
Secondly, the cluster head node needs to transmit the sum P of data each timeiThe calculation method is shown as formula two, where ρ represents the density of the nodes in the wireless sensor network, λ represents the number of bits in one data packet in a fixed time interval, i represents the number of layers where the current cluster head node is located, n represents the total number of layers, and r represents the fixed width of each layer.
Figure BDA0003157643960000031
Then, respectively, to obtaini-RCHIndicated as the energy consumed by the current cluster head to receive data transmitted by other cluster heads, where EelecRepresenting the electron energy, which depends on factors such as digital coding and modulation, is calculated as shown in equation three:
Figure BDA0003157643960000032
with Ei-RCMRepresented by the energy consumed by the current cluster head to receive data transmitted by a cluster member, where EelecRepresenting the electron energy, and the calculation mode is shown as the formula four:
Figure BDA0003157643960000033
with Ei-TRIndicated as the energy consumed by the current cluster head to forward all received data, where EelecRepresenting the electron energy, EampRepresenting the transmit amplifier, r represents the fixed width of each layer, and is calculated as shown in equation five:
Ei-TR＝Pi(Eelec+Eamprr2) Equation five
The total energy E consumed by the cluster head can be obtained according to the formula sixi-CH，
Figure BDA0003157643960000034
It should be noted that, in step 3, according to that the energy of the cluster head nodes is the same, and one cluster only contains one cluster head node, therefore, the calculation method of the number ratio of the cluster head nodes between layers, that is, the number ratio of the clusters, is as follows:
E1-CH≈E2-CH≈…≈En-CHseven formula
It should be noted that, in step 4, the total energy E consumedtotalThe calculation method is as follows: with EiRepresenting the energy consumed by each layer of the wireless sensor network, and representing the total number of layers of the wireless sensor network by n, wherein the calculation method is shown as the formula eight.
Figure BDA0003157643960000035
Therefore, when EiNamely, when the energy consumed by each layer in the wireless sensing network is minimum, the total energy consumed is minimum. EiThe calculation method of (2) is shown as formula nine.
Ei＝Ci×(Ei-CH+Ei-nonCH) Nine form
Wherein, CiTo correspond to the number of clusters in the layer, Ei-CHRepresenting the energy consumed by cluster head nodes in the corresponding layer, Ei-nonCHRepresenting the total energy consumed by a cluster member within a cluster in the corresponding layer. Ei-CHCan be obtained from the formula six, and Ei-nonCHThe calculation is shown in formula ten.
Figure BDA0003157643960000041
dtCHDenotes the distance, x, of a cluster member to a cluster head node2+y2And p represents the density of the sensor nodes, and assuming that the cluster head is positioned at the center of the cluster, the expression of the average distance from the cluster members to the cluster head is shown as formula eleven.
Figure BDA0003157643960000042
The cartesian coordinate system in equation eleven may be converted into a polar coordinate system, where ρ represents the density of the sensor nodes, as shown in equation twelve
Figure BDA0003157643960000043
If a cluster is considered to be a circle of radius R, the area of the circle is π R2. The actual size of each cluster is
Figure BDA0003157643960000044
With the thirteen formula, the value corresponding to R can be obtained.
Figure BDA0003157643960000045
Converting the rectangular plane coordinate system in the formula eleven into a polar coordinate system, and combining the rectangular plane coordinate system obtained by the formula twelve
Figure BDA0003157643960000046
Can obtain dtCHThe corresponding value is shown in formula fourteen.
Figure BDA0003157643960000047
The combined formula six, the formula ten and the formula fourteen can obtain EiIs given by equation fifteen.
Figure BDA0003157643960000048
In the formula fifteen to CiDerivative to obtain
Figure BDA0003157643960000049
Thus, E isiWith CiSo that C must be minimized to minimize the total energyiI.e. the number of clusters reaches a maximum. The cluster proportion between all layers is known from formula seven, and the proportion of the first layer cluster is found to be the largest. Therefore, under the constraint formula seven, in order to maximize the total number of clusters, all the nodes in the first layer are used as cluster head nodes, that is, each node is self-formed into a cluster.
In step 5, the specific number of clusters in each layer can be calculated by combining the proportion obtained by the formula seven and the number of clusters in the first layer.
In step 6, since the initial energy is the same, only the relative positions of the node and other nodes in the cluster are considered when selecting the cluster head node. From the references a. foerster, and a.l. murphy, "Optimal Cluster Sizes for Wireless Sensor Networks," in Ad Hoc Networks, Niagara Falls, CANADA,2009, pp.49- + it is known that the Cluster head node is located in the center of the Cluster and that the energy loss can be reduced by 15% compared to the other cases. Therefore, when selecting a cluster head, a node close to the center of the cluster is selected as much as possible. After determining the cluster head, the cluster head will transmit a confirmation message to its cluster members, and after receiving the message, the cluster members will reply to the cluster head confirmation message. In the clustering algorithm, once the cluster is determined, the cluster is not changed in the subsequent working process.
In step 7, intra-cluster and inter-cluster data are transmitted. When data in the cluster is transmitted, cluster members can transmit the data to the cluster head nodes of the cluster; and during inter-cluster data transmission, the cluster head selects the most appropriate next hop cluster head node, and requires that the next hop node must be located in an adjacent inner layer of the current layer. And if the cluster head is in the first layer, directly transmitting the data to the sink node. The selection of the next hop cluster head node is based on the distance and the residual energy, and the cluster head node with shorter distance and more residual energy is easier to select as the next hop node.
It should be noted that, in step 8, by using fuzzy logic, multiple factors may be considered comprehensively, so as to select a suitable node as a next cluster head node. In the clustering algorithm, the fuzzy logic (as shown in fig. 2) uses two inputs, namely the residual energy of the node and the relative distance of the node, and the output is the possibility that the node becomes the next cluster head node. Fig. 2 and 3 show the membership functions of two inputs, respectively, and fig. 4 shows the membership functions of outputs.
Compared with the prior art, the invention has the advantages and beneficial effects that: the invention provides an energy-balanced non-equilibrium clustering method for a circular wireless sensor network. Compared with the prior art, the method has the advantages that the energy consumption of each layer of cluster head is kept consistent as much as possible by utilizing wide theoretical derivation and mathematical calculation; and the fuzzy logic is combined to realize cluster head rotation, so that the life cycle of the wireless sensor network is obviously prolonged.
Drawings
FIG. 1 is a network model of the present invention;
FIG. 2 is a fuzzy logic system of the present invention;
FIG. 3 is a membership function of the residual energy of nodes in the fuzzy logic system of the present invention;
FIG. 4 is a membership function of the residual energy of nodes in the fuzzy logic system of the present invention;
FIG. 5 is a flow chart of the present invention.
Detailed Description
The technical solution of the present invention can be implemented by a person skilled in the art using computer software technology. The following provides a detailed description of embodiments of the invention, taken in conjunction with the accompanying drawings.
The embodiment provides an energy-balanced non-balanced clustering method for a circular wireless sensor network, which can effectively improve the condition of unbalanced energy distribution in a clustering protocol and improve the life cycle of the wireless sensor network. The wireless sensing network is the key of network technology and can be applied to a plurality of fields such as environment monitoring, military field, medical care and the like. Meanwhile, the wireless sensor network is also the foundation of the current popular research fields such as big data, the internet of things and the like. The example utilizes Matlab to carry out technical simulation, develops experiments under different initial energies and node numbers, explains the method, and compares the method with other classical clustering methods. It should be noted that the present invention is not limited to the application support of the above-mentioned devices and samples, but is equally applicable to any device of equivalent nature and in a practical environment, which is capable of performing the functions of the above-mentioned devices.
As shown in fig. 5, an energy-balanced non-uniform clustering method for a circular wireless sensor network according to an embodiment of the present invention mainly includes the following steps:
1) and determining the distribution conditions of the sensor nodes and the sink nodes, and determining the layering of the wireless sensor network.
And the wireless sensor network model is determined, so that the positions of all nodes in the wireless sensor network can be conveniently determined. When the wireless sensor network is layered, firstly, a Cartesian coordinate system needs to be established by taking the sink node as a circle center. If the coordinate (x, y) of the current node, the node is at the second position
Figure BDA0003157643960000061
And the layer, wherein r is the fixed width of each layer in the wireless sensor network.
2) And obtaining the total energy consumed by each layer of cluster head nodes.
First, the number of clusters and cluster heads C per layer needs to be obtainediThe calculation method is shown as formula I, wherein gammaiIndicating the size of the central angle corresponding to the cluster in the ith layer.
Figure BDA0003157643960000062
Secondly, the cluster head node needs to transmit the sum P of data each timeiThe calculation mode is shown as the formula II, wherein rho represents wirelessThe density of nodes in the sensing network, λ represents the number of bits in a data packet in a fixed time interval, i represents the number of layers where the current cluster head node is located, and n represents the total number of layers.
Figure BDA0003157643960000063
Then, respectively, to obtaini-RCHIndicated as the energy consumed by the current cluster head to receive data transmitted by other cluster heads, where EelecRepresenting the electron energy, which depends on factors such as digital coding and modulation, is calculated as shown in equation three:
Figure BDA0003157643960000064
with Ei-RCMRepresented by the energy consumed by the current cluster head to receive data transmitted by a cluster member, where EelecRepresenting the electron energy, and the calculation mode is shown as the formula four:
Figure BDA0003157643960000071
with Ei-TRIndicated as the energy consumed by the current cluster head to forward all received data, where EelecRepresenting the electron energy, EampRepresenting the transmit amplifier, r represents the fixed width of each layer, and is calculated as shown in equation five:
Ei-TR＝Pi(Eelec+Eamprr2) Equation five
The total energy E consumed by the cluster head can be obtained according to the formula sixi-CH，
Figure BDA0003157643960000072
3) And calculating the interlayer cluster head proportion by using the same energy consumption of the cluster head nodes.
The calculation method is characterized in that the energy consumed by the cluster head nodes is the same, and the ratio of the number of the cluster head nodes, namely the clusters, among all layers is as follows:
E1-CH≈E2-CH≈…≈En-CHseven formula
4) And obtaining the number of the first-layer cluster head nodes by utilizing the minimum total energy consumption.
It should be noted that, in step 4, the total energy E consumedtotalThe calculation method is as follows: with EiRepresenting the energy consumed by each layer of the wireless sensor network, and representing the total number of layers of the wireless sensor network by n, wherein the calculation method is shown as the formula eight.
Figure BDA0003157643960000073
Therefore, when EiNamely, when the energy consumed by each layer in the wireless sensing network is minimum, the total energy consumed is minimum. EiThe calculation method of (2) is shown as formula nine.
Ei＝Ci×(Ei-CH+Ei-nonCH) Nine form
Wherein, CiTo correspond to the number of clusters in the layer, Ei-CHRepresenting the energy consumed by cluster head nodes in the corresponding layer, Ei-nonCHRepresenting the total energy consumed by a cluster member within a cluster in the corresponding layer. Ei-CHCan be obtained from the formula six, and Ei-nonCHThe calculation is shown in formula ten.
Figure BDA0003157643960000074
dtCHDenotes the distance, x, of a cluster member to a cluster head node2+y2And p represents the density of the sensor nodes, and assuming that the cluster head is positioned at the center of the cluster, the expression of the average distance from the cluster members to the cluster head is shown as formula eleven.
Figure BDA0003157643960000075
The cartesian coordinate system in equation eleven may be converted into a polar coordinate system, where ρ represents the density of the sensor nodes, as shown in equation twelve
Figure BDA0003157643960000076
If a cluster is considered to be a circle of radius R, the area of the circle is π R2. The actual size of each cluster is
Figure BDA0003157643960000081
With the thirteen formula, the value corresponding to R can be obtained.
Figure BDA0003157643960000082
Converting the rectangular plane coordinate system in the formula eleven into a polar coordinate system, and combining the rectangular plane coordinate system obtained by the formula twelve
Figure BDA0003157643960000083
Can obtain dtCHThe corresponding value is shown in formula fourteen.
Figure BDA0003157643960000084
The combined formula six, the formula ten and the formula fourteen can obtain EiIs given by equation fifteen.
Figure BDA0003157643960000085
In the formula fifteen to CiDerivative to obtain
Figure BDA0003157643960000086
Thus, E isiWith CiIs increased and decreased, so that the total energy is decreasedTo a minimum, C must be madeiI.e. the number of clusters reaches a maximum. The cluster proportion between all layers is known from formula seven, and the proportion of the first layer cluster is found to be the largest. Therefore, under the constraint formula seven, in order to maximize the total number of clusters, all the nodes in the first layer are used as cluster head nodes, that is, each node is self-formed into a cluster.
5) And calculating the specific number of the cluster heads and clusters on each layer by combining the number of the cluster heads on the first layer.
The specific number of clusters in each layer can be calculated by combining the proportion obtained by the seventh formula and the number of clusters in the first layer.
6) Cluster head election and cluster formation.
Because the initial energy is the same, when selecting the cluster head node, only the relative positions of the node and other nodes in the cluster are considered. And selecting the node which is as close to the center of the cluster as possible as a cluster head node. After determining the cluster head, the cluster head will transmit a confirmation message to its cluster members, and after receiving the message, the cluster members will reply to the cluster head confirmation message. In the clustering algorithm, once the cluster is determined, the cluster is not changed in the subsequent working process.
7) Intra-cluster and inter-cluster data transfer.
Intra-cluster and inter-cluster data transfer. When data in the cluster is transmitted, cluster members can transmit the data to the cluster head nodes of the cluster; and during inter-cluster data transmission, the cluster head selects the most appropriate next hop cluster head node, and requires that the next hop node must be located in an adjacent inner layer of the current layer. And if the cluster head is in the first layer, directly transmitting the data to the sink node. The selection of the next hop cluster head node is based on the distance and the residual energy, and the cluster head node with shorter distance and more residual energy is easier to select as the next hop node.
8) Based on fuzzy logic, cluster head rotation is performed.
By using fuzzy logic, a plurality of factors can be comprehensively considered so as to select a proper node as a next cluster head node. In the clustering algorithm, the fuzzy logic (as shown in fig. 2) uses two inputs, namely the residual energy of the node and the relative distance of the node, and the output is the possibility that the node becomes the next cluster head node. Fig. 2 and 3 show the membership functions of two inputs, respectively, and fig. 4 shows the membership functions of outputs. The following table shows the fuzzy rule set. The Energy represents a result after fuzzification of residual Energy of the current node, the Distance represents a result after fuzzification of the relative position of the current node in the cluster, and the PBCH represents a result after fuzzification of the probability that the current node becomes a cluster head node.
Figure BDA0003157643960000091
9) And (5) circulating the step 7 and the step 8 until the wireless sensing network cannot work continuously.
And circularly executing the intra-cluster and inter-cluster transmission steps, and performing cluster head rotation to ensure that the whole network continuously works until the wireless sensor network cannot continue to work normally.
The above description of the embodiments is only intended to facilitate the understanding of the method of the invention and its core idea. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (8)

1. An energy-balanced non-equilibrium clustering method for a circular wireless sensor network is characterized by comprising the following steps:
step 1, determining the distribution conditions of sensor nodes and sink nodes, and determining the layering of a wireless sensor network;
step 2, obtaining the total energy consumed by each layer of cluster head nodes;
step 3, calculating the interlayer cluster head proportion by using the same energy consumption of the cluster head nodes;
step 4, obtaining the number of the first-layer cluster head nodes by utilizing the minimum total energy consumption;
step 5, calculating the specific number of cluster heads and clusters on each layer by combining the number of cluster heads on the first layer;
step 6, selecting cluster heads and forming clusters;
step 7, data transmission in clusters and between clusters;
step 8, based on fuzzy logic, performing cluster head rotation;
and 9, circulating the step 7 and the step 8 until the wireless sensing network cannot work continuously.
2. The method for unequally clustering the energy-balanced circular wireless sensor network according to claim 1, wherein: in step 1, when the wireless sensor network is layered, firstly, a Cartesian coordinate system is established by taking a sink node as a circle center, and if the coordinates (x, y) of the current node are determined, the node is positioned at the first
Figure FDA0003157643950000011
Layers, where r represents the fixed width of each layer.
3. The method for unequally clustering the energy-balanced circular wireless sensor network according to claim 1, wherein: in step 2, the number C of clusters and cluster heads in each layer is obtained firstiThe calculation method is shown as formula I, wherein gammaiRepresenting the size of a central angle corresponding to a cluster in the ith layer;
Figure FDA0003157643950000012
secondly, the cluster head node needs to transmit the sum P of data each timeiThe calculation mode is shown as formula two, wherein rho represents the density of nodes in the wireless sensor network, lambda represents the number of bits in one data packet in a fixed time interval, i represents the number of layers where the current cluster head node is located, n represents the total number of layers, and r represents the fixed width of each layer;
Figure FDA0003157643950000013
then, respectively, to obtaini-RCHIndicated as the energy consumed by the current cluster head to receive data transmitted by other cluster heads, where EelecThe electron energy is expressed, and the calculation mode is shown as the formula III:
Figure FDA0003157643950000014
with Ei-RCMThe energy consumed by the current cluster head to receive the data transmitted by the cluster member is calculated as shown in the following formula:
Figure FDA0003157643950000021
with Ei-TRIndicated as the energy consumed by the current cluster head to forward all received data, where EampRepresenting the transmit amplifier, r represents the fixed width of each layer, and is calculated as shown in equation five:
Ei-TR＝Pi(Eelec+Eamp×r2) Formula five
The total energy E consumed by the cluster head can be obtained according to the formula sixi-CH，
Figure FDA0003157643950000026
4. The energy-balanced round wireless sensor network unbalanced clustering method according to claim 3, wherein: in step 3, according to the same energy of the cluster head nodes, the method for calculating the cluster head nodes between layers, namely the number ratio of the clusters, is as follows:
E1-CH≈E2-CH≈…≈En-CHand a seventh expression.
5. The energy-balanced round wireless sensor network unbalanced clustering method according to claim 3, wherein: in step 4The total energy Etotal consumed is calculated as follows: with EiRepresenting the energy consumed by each layer of the wireless sensor network, and representing the total number of layers of the wireless sensor network by n, wherein the calculation method is shown as the formula eight;
Figure FDA0003157643950000022
therefore, when EiI.e. the total energy consumed is minimum when the energy consumed by each layer in the wireless sensor network is minimum, EiThe calculation method of (a) is shown as formula nine;
Ei＝Ci×(Ei-CH+Ei-nonCH) Nine-degree of expression
Wherein, CiTo correspond to the number of clusters in the layer, Ei-CHRepresenting the energy consumed by cluster head nodes in the corresponding layer, Ei-nonCHRepresenting the total energy consumed by a cluster member within a cluster in the corresponding layer; ei-CHCan be obtained from the formula six, and Ei-nonCHThe calculation mode is shown as formula ten;
Figure FDA0003157643950000023
dtCHdenotes the distance, x, of a cluster member to a cluster head node2+y2The square of the distance from a cluster member to a cluster head is represented, rho represents the density of a sensor node, and if the cluster head is located at the center of a cluster, the expression of the average distance from the cluster member to the cluster head is shown as formula eleven;
Figure FDA0003157643950000024
converting a Cartesian coordinate system in the formula eleven into a polar coordinate system, wherein rho represents the density of the sensor node, and is shown as a formula twelve;
Figure FDA0003157643950000025
if a cluster is considered to be a circle of radius R, the area of the circle is π R2Each cluster having an actual size of
Figure FDA0003157643950000031
With the thirteen formula, the value corresponding to R can be obtained;
Figure FDA0003157643950000032
converting the rectangular plane coordinate system in the formula eleven into a polar coordinate system, and combining the rectangular plane coordinate system obtained by the formula twelve
Figure FDA0003157643950000033
Can obtain dtCHCorresponding values are shown in formula fourteen;
Figure FDA0003157643950000034
the combined formula six, the formula ten and the formula fourteen can obtain EiIs as shown in equation fifteen;
Figure FDA0003157643950000035
in the formula fifteen to CiDerivative to obtain
Figure FDA0003157643950000036
Thus, E isiWith CiSo that C must be minimized to minimize the total energyiThat is, the number of clusters reaches the maximum, the proportion of clusters between all layers can be known from the formula seven, and the proportion of clusters in the first layer is found to be the maximum, so that, under the constraint formula seven, all nodes in the first layer are taken as clusters to maximize the total number of clustersThe head node, i.e., each node, is itself a cluster.
6. The energy-balanced round wireless sensor network unbalanced clustering method according to claim 4, wherein: and 5, calculating the specific number of clusters in each layer by combining the proportion obtained by the formula seven and the number of clusters in the first layer.
7. The method for unequally clustering the energy-balanced circular wireless sensor network according to claim 1, wherein: step 7, when the data in the cluster is transmitted, the cluster members transmit the data to the cluster head nodes of the cluster; when data is transmitted between clusters, the cluster head selects the most appropriate next hop cluster head node and requires that the next hop node is necessarily positioned in the adjacent inner layer of the current layer; and if the cluster head is in the first layer, directly transmitting the data to the sink node, wherein the basis for selecting the next hop cluster head node is the distance and the residual energy.
8. The method for unequally clustering the energy-balanced circular wireless sensor network according to claim 1, wherein: in step 8, a suitable node is selected as a next cluster head node by using fuzzy logic, the fuzzy logic adopts two inputs, namely the residual energy of the node and the relative distance of the node, and the output is the possibility that the node becomes the next cluster head node.
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