CN113038564B - Non-uniform clustering low-power-consumption multi-hop routing control method based on fuzzy logic - Google Patents

Non-uniform clustering low-power-consumption multi-hop routing control method based on fuzzy logic Download PDF

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CN113038564B
CN113038564B CN202110164338.0A CN202110164338A CN113038564B CN 113038564 B CN113038564 B CN 113038564B CN 202110164338 A CN202110164338 A CN 202110164338A CN 113038564 B CN113038564 B CN 113038564B
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CN113038564A (en
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许峰
侯雅婷
吕昕
武淦
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Nanjing University of Aeronautics and Astronautics
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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
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • 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
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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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 a non-uniform clustering low-power consumption multi-hop routing control method based on fuzzy logic, which aims to solve the technical problems of energy consumption of a wireless sensor and weak adaptability of the existing energy-saving routing control method to a network. A fuzzy logic algorithm is adopted, the cluster size is dynamically adjusted according to the network energy consumption condition and the node density information, and the energy consumption of the cluster head is effectively balanced; and selecting the routing node with the optimal evaluation value according to the node energy and the position information, and determining a routing path. The concept of a network surviving node status detector is introduced to monitor the status of surviving nodes and extend the survival time of the network. The protocol has strong adaptability, has more flexible network scale, and is suitable for wireless sensor networks of various scales.

Description

Non-uniform clustering low-power-consumption multi-hop routing control method based on fuzzy logic
Technical Field
The invention relates to a non-uniform clustering low-power consumption multi-hop routing control method NCMBLF based on fuzzy logic, belonging to the field of wireless sensor network routing control methods. The protocol adopts an analytic hierarchy process based on fuzzy logic, and the cluster size is adjusted in a mode of dynamically adjusting the node competition radius according to the node energy consumption condition and the density information; and selecting the routing node with the optimal evaluation value according to the information such as the node energy density, the position and the like, and determining a routing path.
Background
In recent years, wireless sensor networks have become a research hotspot, and are widely applied to environmental monitoring, logistics, forest fire prevention, smart home and other aspects. The sensor nodes are powered by batteries, and the energy is limited, so that the reduction of energy consumption and the prolonging of service life are the primary targets of the design of the wireless sensor network routing control method.
At present, the main researches on prolonging the life cycle of the sensor network are as follows: redundant data is removed and communication load is reduced by adopting data fusion and data compression technologies; a clustering hierarchical routing structure is adopted to reduce the number of nodes which are in direct communication with a base station; and a multi-hop short-distance wireless communication mode is adopted, so that energy waste and the like caused by long-distance data transmission are avoided. These technical methods are further combined to form a series of research results.
In the past, the research of a routing control method of a large-scale wireless sensor network is usually directed at a scene of uniform deployment of nodes, and the distribution of the sensor nodes in practical application is usually complex and random and is in a non-uniform distribution state. Even in a uniformly distributed scene, over time, the topology of the network changes due to energy exhaustion and the like in some nodes, resulting in a situation where the nodes are not uniformly distributed. The problem to be solved urgently is how to prolong the life cycle of the network under the condition that the distribution of the wireless sensor network nodes is uneven.
Disclosure of Invention
The invention provides a non-uniform clustering low-power consumption multi-hop routing control method based on fuzzy logic, and aims to solve the technical problems of energy consumption of a wireless sensor and weak adaptability of the existing energy-saving routing control method to a network. The technical scheme adopted by the invention is as follows: a fuzzy logic algorithm is adopted, the cluster size is dynamically adjusted according to the network energy consumption condition and the node density information, and the energy consumption of the cluster head is effectively balanced; and selecting the routing node with the optimal evaluation value according to the node energy and the position information, and determining a routing path. The concept of a network surviving node status detector is introduced to monitor the status of surviving nodes and extend the survival time of the network. Compared with the prior art, the protocol has strong adaptability, has more flexible network scale and is suitable for wireless sensor networks of various scales.
The invention relates to a non-uniform clustering low-power consumption multi-hop routing control method based on fuzzy logic, which comprises a system model, a survival node state monitor, non-uniform clustering based on fuzzy logic and routing based on fuzzy logic. The system model provides a model for protocol implementation, wherein the model comprises a network model, an energy model and a network topological structure; the survival node state monitor provides the residual energy information and the node distribution condition of each node in the network for the base station; the non-uniform clustering based on the fuzzy logic is to dynamically adjust the cluster size by adopting a hierarchical analysis method based on the fuzzy logic according to the network energy consumption condition, and to make the number of cluster members close to a base station relatively smaller by utilizing the non-uniform competition radius, so as to achieve the purpose of balancing the energy consumption of cluster heads; the routing selection based on the fuzzy logic is to select the most appropriate transit route by considering node energy and distance factors and selecting a drift angle between the transit node and the transit node which is directly transmitted to the base station and applying an analytic hierarchy process based on the fuzzy logic.
The network model in the system model has the following properties: (1) The sensor network is monitored to be a square area, the base station is positioned outside the monitoring area, and the positions of the base station and the sensor nodes are not changed after the network is laid; (2) The nodes in the network area are isomorphic, the initial energy of the nodes is consistent, and each sensor node has a unique label; (3) The sensor nodes are randomly distributed in a network area, and the base station stores network area node initialization distribution information; (4) The communication links between the sensor nodes are symmetrical, the distance can be approximately calculated according to the signals of the signal receiving nodes, and the transmitting power can be flexibly adjusted according to the actual position distance during sending; (5) The nodes have certain computing and processing capacity and moderate storage space, can perform basically limited operation and store a small amount of information.
The energy model employs a first-order radio mode typical of wireless sensor networks. Assuming a threshold value d0 in the model, and setting the distance between the sending node and the receiving node as d, when d < d0, the node uses a free space energy consumption model, the energy consumption of the sending data is in direct proportion to the square of d, and when d > d0, the node uses a multipath fading energy consumption model, the energy consumption of the sending data is in direct proportion to the fourth power of d.
The network topological structure is a typical hierarchical structure of a wireless sensor network, sensing information is sent to corresponding cluster heads by common nodes in a stable stage, each cluster head is responsible for collecting information transmitted by nodes in a fusion cluster and receiving data packets sent by other cluster heads, and finally data are sent to a base station along a constructed multi-hop routing path.
The survival node state monitor is established in the base station and provides state information of each node in the network for the base station, wherein the state information comprises a node number, a node position and node residual energy. The base station monitors the change of the network topology structure according to the node state information, senses the consumption of the node energy, optimizes the cluster head selection and the route construction, and saves the energy consumption of the node for sending and receiving the broadcast information to obtain the local information.
The non-uniform clustering algorithm based on the fuzzy logic comprises the following steps:
(1) The algorithm selects candidate cluster heads randomly in the first round, and selects the candidate cluster heads in the later rounds according to the node residual energy, the node density and the base station distance factors.
(2) Converting the three linguistic variables of the residual energy, the node density and the distance from the candidate cluster head node to the base station into corresponding linguistic values by utilizing a fuzzified thought, wherein the residual energy is divided into three grades of low, medium and high, the node density is divided into three grades of small, medium and large, and the distance from the node density to the base station is divided into three grades of close, medium and far; and expressing the boundary linguistic value by adopting a trapezoidal membership function, and expressing the intermediate linguistic value by adopting a triangular membership function.
(3) According to the rule that the higher the energy of the candidate cluster head is, the higher the node density is, and the closer the node is to the base station, the better the candidate cluster head is, the normalization processing is performed on the linguistic value of each variable to obtain the membership degree of each single factor to the evaluation set { "poor", "general", "excellent" }, and a single factor judgment matrix is constructed.
(4) And determining the weight of each factor to the evaluation candidate cluster head by adopting an Analytic Hierarchy Process (AHP) to construct a weight vector.
(5) And applying an M (·, +) weighted average model to obtain a candidate cluster head evaluation vector.
(6) And (5) defuzzification is carried out by adopting a gravity center method to obtain a final evaluation value of the candidate cluster head.
(7) And calculating the competition radius according to the final evaluation value of the candidate cluster head, and executing cluster head competition. And (4) when the candidate cluster head with the highest residual energy is elected and marked as a cluster head node, the candidate cluster head in the competition radius range quits the competition and becomes a common node. And (4) continuously repeating the operations (1) to (7) until no candidate cluster head exists in the network area.
(8) And after the cluster head is selected, the common node is added into the cluster where the cluster head with the shortest distance to the common node is located.
The fuzzy logic based routing rule is as follows: (1) if the distance between the cluster head CHi and the base station is smaller than the threshold value d0, the cluster head does not need a transfer node, and the data is directly transmitted to the base station in a single-hop mode. (2) And if the distance between the cluster head CHi and the base station exceeds d0, the cluster head CHi selects a transit node in the routing candidate node set for data forwarding. When the candidate route set of the cluster head CHi is empty, the base station is the next hop node of the CHi; when the candidate route set only has one node, the node is directly selected as the next hop route; and when a plurality of candidate nodes exist in the candidate route set, evaluating each candidate route node by using a fuzzy logic-based analytic hierarchy process, and selecting the node with the highest evaluation value as the next hop route.
A large number of simulation experiments show that compared with other routing control methods, the protocol has strong adaptability, more flexible network scale, low energy consumption and long life cycle, and is suitable for wireless sensor networks of various scales.
Drawings
FIG. 1 is a flow chart of calculating a candidate cluster head contention radius;
FIG. 2 is a graph depicting membership functions of evaluation factors of candidate cluster heads;
FIG. 3 is a schematic of node energy density;
FIG. 4 is a schematic view of a nodal deflection angle;
FIG. 5 is a schematic diagram of a multi-hop path construction;
fig. 6 is a graph of experimental simulation results of the wireless sensor network at two scales.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
The invention relates to a non-uniform clustering low-power consumption multi-hop routing control method based on fuzzy logic, which comprises a system model, a survival node state monitor, non-uniform clustering based on fuzzy logic and routing based on fuzzy logic. The system model provides a model for protocol implementation, wherein the model comprises a network model, an energy model and a network topology structure; the survival node state monitor provides the residual energy information and the node distribution condition of each node in the network for the base station; the non-uniform clustering based on the fuzzy logic is to dynamically adjust the cluster size by adopting a hierarchical analysis method based on the fuzzy logic according to the network energy consumption condition, and to make the number of cluster members close to a base station relatively smaller by utilizing the non-uniform competition radius, so as to achieve the purpose of balancing the energy consumption of cluster heads; the routing selection based on the fuzzy logic is to select the most appropriate transfer route by considering node energy, distance factors and a bias angle between the transfer node and the bias angle directly transmitted to the base station and applying an analytic hierarchy process based on the fuzzy logic.
The network model in the system model has the following properties: (1) The sensor network is monitored to be a square area, the base station is positioned outside the monitoring area, and the positions of the base station and the sensor nodes are not changed after the network is laid; (2) The nodes in the network area are isomorphic, the initial energy of the nodes is consistent, and each sensor node has a unique label; (3) The sensor nodes are randomly distributed in a network area, and the base station stores network area node initialization distribution information; (4) The communication links between the sensor nodes are symmetrical, the distance can be approximately calculated according to the signals of the signal receiving nodes, and the transmitting power can be flexibly adjusted according to the actual position distance during sending; (5) The nodes have certain computing and processing capacity and moderate storage space, can perform basically limited operation and store a small amount of information.
The energy model employs a first-order radio mode typical of wireless sensor networks. The threshold value d0 is assumed in the model, the distance between the sending node and the receiving node is set as d, when d is less than d0, the node uses a free space energy consumption model, the energy consumption of sending data is in direct proportion to the square of d, when d is greater than d0, the node uses a multipath attenuation energy consumption model, and the energy consumption of sending data is in direct proportion to the fourth power of d. The energy consumed when a sensor node sends x bits of data is:
Figure GDA0003860867250000051
the energy consumed by the node for receiving x bits of data is as follows:
E RX (x)＝E RX-elec (x)＝E elec ×x#(2)
wherein TX represents a sectionTotal energy consumption of point communication, TX-elec represents total energy consumption for operating the receiving circuit and the transmitting circuit, TX-amp represents total energy consumption of the transmission power amplifier, E elec Represents the energy consumption of a transmitting circuit and a receiving circuit in communication when transmitting or receiving 1-bit data, epsilon fs ，ε mp Represents the energy consumption of the signal amplifier to transmit 1 bit data per unit distance under the free space and multipath fading model.
The network topological structure is a typical hierarchical structure of a wireless sensor network, sensing information is sent to corresponding cluster heads by common nodes in a stable stage, each cluster head is responsible for collecting information transmitted by nodes in a fusion cluster and receiving data packets sent by other cluster heads, and finally data are sent to a base station along a constructed multi-hop routing path.
The flow chart for calculating the competition radius of the candidate cluster heads based on the fuzzy logic non-uniform clustering algorithm is shown in fig. 1, and comprises the following steps:
(1) And (3) randomly selecting candidate cluster heads in the first round of the algorithm, and selecting the candidate cluster heads in the later rounds according to the factors of node residual energy, node density and base station distance.
(2) Converting the three linguistic variables of the residual energy, the node density and the distance from the candidate cluster head node to the base station into corresponding linguistic values by utilizing a fuzzified thought, wherein the residual energy is divided into three grades of low, medium and high, the node density is divided into three grades of small, medium and large, and the distance from the node density to the base station is divided into three grades of close, medium and far; a trapezoidal membership function is adopted to express a boundary linguistic value, a triangular membership function is adopted to express an intermediate linguistic value, and the distribution of the membership function is obtained and is shown in figure 2, wherein E0 represents an initial energy value of a sensor node, and dmax represents the maximum value of the distance between the node and a base station in the network.
Let us assume that there is actually one candidate cluster head A1 with a residual energy of 0.7e0, a node density of 0.15, and a distance to the base station of 0.6dmax. According to fig. 2, the degree of membership of the candidate cluster head remaining energy to the language values { "low", "medium", "high" } can be obtained as [0,1/3,1/6]; the degree of membership of the candidate cluster head node density to the language values { "small", "medium", "large" } is [0,3/4,1/3]; the candidate cluster head to base station distance has a degree of membership of 0,1/3,2/3 to the speech value { "close", "medium", "far" }.
(3) According to the rule that the higher the energy of the candidate cluster head is, the higher the node density is, and the closer the node is to the base station, the better the candidate cluster head is, the normalization processing is performed on the linguistic value of each variable to obtain the membership degree of each single factor to the evaluation set { "poor", "general", "excellent" }, and a single factor judgment matrix is constructed. Taking A1 as an example, the membership fuzzy matrix of A1 to the comment set is:
Figure GDA0003860867250000071
(4) And determining the weight of each factor to the evaluation candidate cluster head by adopting an Analytic Hierarchy Process (AHP) to construct a weight vector. The method comprises the following specific steps:
(1) and constructing a judgment matrix. And comparing and judging every two factors to determine the relative importance degree of the factors, and expressing the relative importance degree by using a proper scale value. The relative importance of the factors Mi and Mj is expressed by mij, which is shown in Table 1.
TABLE 1 common relative importance Scale values mij when constructing a decision matrix
Figure GDA0003860867250000072
And comparing the residual energy factor, the node density factor and the distance factor pairwise relative to the candidate cluster head election probability target to obtain a judgment matrix as follows:
Figure GDA0003860867250000073
(2) calculating the importance of each factor by adopting a characteristic vector method, and normalizing the matrix A to obtain a final weight vector X A ＝(0.6586,0.0786,0.2628) T That is, the weight of the remaining energy factor is 0.6586, the weight of the node density factor is 0.0786, and the weight of the node itself to base station distance factor is 0.2628.
(5) And obtaining candidate cluster head evaluation vectors by applying an M (, +) weighted average model, wherein the M (, +) weighted average model is as follows:
Figure GDA0003860867250000074
wherein W 1×n The fuzzy comprehensive evaluation of the A1 is shown,
Figure GDA0003860867250000081
indicates that the final weight vector, R, is obtained by normalizing the judgment matrix A m×n Representing membership fuzzy matrix, x 'of A1 to the set of comments' i The value, r, of each element representing the final weight vector ij A value representing each element of the membership ambiguity matrix. From the M (·, +) model, a fuzzy comprehensive evaluation of A1 was calculated as W = (0.1752, 0.5811, 0.2437).
From the M (·, +) model, a fuzzy comprehensive evaluation of A1 was calculated as W = (0.1752, 0.5811, 0.2437).
(6) And (5) defuzzification is carried out by adopting a gravity center method to obtain a final evaluation value of the candidate cluster head. The formula of the gravity center method is as follows:
Figure GDA0003860867250000082
wherein N is the number of evaluation factors, y i Fuzzy value, μ (y) for the ith comment in the set of comments i ) Are the corresponding degrees of membership. The fuzzy values of the comment sets { "poor", "general", "excellent" } are 1,3,6, respectively. Defuzzification is carried out by a gravity center method, and the grade value of A1 is obtained as follows:
Figure GDA0003860867250000083
(7) And calculating the competition radius according to the final evaluation value of the candidate cluster head, and executing cluster head competition. The calculation method of the competition radius is as follows:
Figure GDA0003860867250000084
wherein, y * Means cluster head candidate evaluation value, y N Fuzzy value of excellent comment, d 0 Is the transmission distance threshold.
After the competition radius is determined, the candidate cluster head with the highest residual energy is elected and marked as a cluster head node, and the candidate cluster head in the competition radius range quits competition to become a common node. And (4) continuously repeating the operations (1) to (7) until no candidate cluster head exists in the network area.
(8) And after the cluster head is selected, the common node is added into the cluster where the cluster head with the shortest distance to the common node is located.
The fuzzy logic based routing rule is as follows: (1) if the distance between the cluster head CHi and the base station is smaller than the threshold value d0, the cluster head does not need to transfer a node, and the data is directly transmitted to the base station in a single-hop mode. (2) And if the distance between the cluster head CHi and the base station exceeds d0, the cluster head CHi selects a transit node in the routing candidate node set for data forwarding. When the candidate route set of the cluster head CHi is empty, the base station is the next hop node of the CHi; when the candidate route set only has one node, the node is directly selected as a next hop route; and when a plurality of candidate nodes exist in the candidate route set, evaluating each candidate route node by using a fuzzy logic-based analytic hierarchy process, and selecting the node with the highest evaluation value as the next hop route.
And evaluating index sets of the candidate routes as node residual energy, energy density and deflection angles. Candidate routing cluster head RCH i The energy density of (2) is the candidate route cluster head RCH i The sum of all the residual energy of the adjacent nodes and the residual energy of the adjacent nodes is recorded as RCH i ED, the calculation formula is as follows:
Figure DEST_PATH_IMAGE002
wherein RCH i .
Figure DEST_PATH_IMAGE004
RE represents candidate routing cluster head RCH i Residual energy, RCH, of the jth sensor node in the neighbor set i RE represents a candidate routing cluster head RCH i The remaining energy of (c). A schematic diagram of energy density of a candidate routing cluster head is shown in fig. 3.
As shown in FIG. 4, the candidate routing cluster head RCH i The drift angle refers to a line segment connecting the node TCH and the base station BS, and the node TCH and the candidate routing cluster head RCH i The angle between the line segments is denoted as RCH i The DA. The calculation formula of the candidate routing cluster head deflection angle is as follows:
Figure GDA0003860867250000092
wherein d (TCH, RCH) i ) D (TCH, BS) are respectively the target cluster head TCH to the candidate routing cluster head RCH i Distance to base station BS, d (RCH) i BS) is a candidate routing cluster head RCH i Distance to the base station BS.
The cluster head SCH multi-hop routing construction process is shown in fig. 5, where nodes identified in black represent source cluster head nodes SCH, nodes identified in dark gray represent candidate routing cluster heads, and nodes identified in gray represent routing nodes. Fig. 5 (a) illustrates an initial state of searching for a multi-hop path between the source cluster head SCH and the base station. Firstly, finding out a candidate routing cluster head set of SCH according to the definition of the candidate routing cluster head. Can find that only one candidate routing cluster head RCH in the graph 1 Therefore RCH 1 Is directly selected as the next hop route for the SCH. In fig. 5 (b), the next hop routing node of the SCH is identified in black and renamed as R 1 . Like SCH, R 1 And a candidate route cluster head set is required to be found, and then the most suitable next-hop route cluster head is selected based on an analytic hierarchy process of fuzzy logic. And searching routing nodes by the subsequent cluster heads according to the principle of routing node selection in sequence until the routing nodes reach the base station. Shown in fig. 5 (c) is an optimized, low energy consumption multi-hop path from the source cluster head SCH to the base station.
The simulation results are shown in fig. 6, and it can be seen that compared with LEACH, EEUC, FBUC, MR-LEACH, FD-LEACH, the energy consumption of NCMBLF is improved significantly, and the method is suitable for small-scale and large-scale wireless sensor network environments.

Claims (4)

1. A non-uniform clustering low-power consumption multi-hop routing control method based on fuzzy logic is characterized by comprising the following steps:
(1) Establishing a surviving node state monitor in a base station;
(2) Dynamically adjusting the cluster size by adopting an analytic hierarchy process based on fuzzy logic, and enabling the number of cluster members close to a base station to be relatively small by utilizing non-uniform competitive radius according to the network energy consumption condition so as to achieve the aim of balancing the energy consumption of a cluster head;
(3) And selecting the most appropriate transfer route by using a fuzzy logic-based routing selection method and applying an analytic hierarchy process based on fuzzy logic, considering node energy and distance factors and a bias angle between the transfer node and the transfer node which is directly transmitted to the base station.
2. The method for controlling the nonuniform clustering, low-power consumption and multi-hop routing based on the fuzzy logic as claimed in claim 1, wherein the specific process of establishing the surviving node status monitor in the base station in the step (1) is as follows:
(101) The survival node state monitor is established in a base station and provides state information of each node in a network for the base station, wherein the state information comprises a node number, a node position and node residual energy;
(102) And the base station monitors the change of the network topology structure according to the node state information and senses the consumption of the node energy so as to optimize cluster head selection and route construction.
3. The method for controlling the non-uniform clustering low-power-consumption multi-hop routing based on the fuzzy logic as claimed in claim 1, wherein the fuzzy logic-based analytic hierarchy process in the step (2) comprises the following steps:
(201) Randomly selecting candidate cluster head nodes in the first round of the algorithm, and selecting the candidate cluster head nodes in each round of the algorithm according to the factors of node residual energy, node density and base station distance;
(202) Converting three linguistic variables including residual energy, node density and distance from a candidate cluster head node to a base station into corresponding linguistic values by utilizing a fuzzification idea, wherein the residual energy is divided into three grades of low, medium and high, the node density is divided into three grades of small, medium and large, and the distance from the node density to the base station is divided into three grades of close, medium and far; expressing boundary linguistic values by adopting a trapezoid membership function, and expressing intermediate linguistic values by adopting a triangular membership function;
(203) According to the rule that the higher the energy of the candidate cluster head is, the higher the node density is, and the closer the candidate cluster head is to the base station, the better the candidate cluster head is, the normalization processing is performed on the linguistic value of each variable to obtain the membership degree of each single factor to the evaluation set { "poor", "general", "excellent" }, and a single factor judgment matrix is constructed;
(204) Determining the weight of each factor to the evaluation candidate cluster head by adopting an Analytic Hierarchy Process (AHP), and constructing a weight vector;
(205) Obtaining candidate cluster head evaluation vectors by applying an M (·, +) weighted average model;
(206) Defuzzification is carried out by adopting a gravity center method to obtain a final evaluation value of the candidate cluster heads;
(207) Calculating the competition radius according to the final evaluation value of the candidate cluster head, and executing cluster head competition selection; the candidate cluster head with the highest residual energy is elected and marked as a cluster head node, and the candidate cluster head in the competition radius range quits the competition and becomes a common node; continuously repeating the steps (201) - (207) until no candidate cluster head exists in the network area;
(208) And after the cluster head is selected, the common node is added into the cluster where the cluster head with the shortest distance to the common node is located.
4. The method for controlling the non-uniform clustering low-power-consumption multi-hop routing based on the fuzzy logic as claimed in claim 1, wherein the specific process of the fuzzy logic based routing method in the step (3) is as follows:
(301) If the distance between the cluster head CHi and the base station is smaller than the threshold value d0, the cluster head does not need a transfer node, and data is directly transmitted to the base station in a single-hop mode; if the distance between the cluster head CHi and the base station exceeds d0, the cluster head CHi selects a transfer node in the route candidate node set for data forwarding;
(302) When the candidate route set of the cluster head CHi is empty, the base station is the next hop node of the CHi; when the candidate route set only has one node, the node is directly selected as a next hop route; when the candidate route set has a plurality of candidate nodes, evaluating each candidate route node by using a fuzzy logic-based analytic hierarchy process, and selecting the node with the highest evaluation value as the next hop route.
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