CN114641049A - Unmanned aerial vehicle ad hoc network layered routing method based on fuzzy logic - Google Patents

Unmanned aerial vehicle ad hoc network layered routing method based on fuzzy logic Download PDF

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CN114641049A
CN114641049A CN202210260132.2A CN202210260132A CN114641049A CN 114641049 A CN114641049 A CN 114641049A CN 202210260132 A CN202210260132 A CN 202210260132A CN 114641049 A CN114641049 A CN 114641049A
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node
cluster head
factor
unmanned aerial
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李云
朱阳
李宁
张明鑫
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18504—Aircraft used as relay or high altitude atmospheric platform
    • 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
    • H04W40/026—Route selection considering the moving speed of individual devices
    • 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
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

The invention belongs to the field of unmanned aerial vehicle ad hoc networks, and relates to an unmanned aerial vehicle ad hoc network layered routing method based on fuzzy logic, which comprises two stages of clustering and routing, wherein firstly, nodes in a network are divided into different clusters by using a clustering algorithm in the clustering stage, a backbone network is constructed according to a clustering result, and then, in the routing stage, a route discovery process is started by using a routing-on-demand mechanism to find a path of a target node; the invention is applied to the large-scale unmanned aerial vehicle ad hoc network, can construct a hierarchical network structure through a distributed clustering method, reduces message flooding in the large-scale network, reduces the message collision probability, improves the communication performance, and has lower complexity.

Description

Unmanned aerial vehicle ad hoc network layered routing method based on fuzzy logic
Technical Field
The invention belongs to the field of unmanned aerial vehicle ad hoc networks, and relates to an unmanned aerial vehicle ad hoc network layered routing method based on fuzzy logic.
Background
The unmanned aerial vehicle is initially applied to the military field, and gradually expands to the civil field along with the development of technologies and the increase of various demands. One of the most critical challenges in drone application is task area coverage, with a low task coverage for a single drone, whereas a cluster of drones can utilize cooperative communication and relay technologies to expand the task coverage to accommodate more complex and varied environments and complex tasks. In the unmanned aerial vehicle cluster, all unmanned aerial vehicle nodes jointly constitute a completely distributed wireless Network, and each unmanned aerial vehicle node can all regard as the router, and can realize the high-efficient information transmission without the help of the basic station, and this kind of Network is called flight Ad-hoc Network (FANET), also called unmanned aerial vehicle Ad-hoc Network. Among them, the routing protocol provides critical communication support for FANET.
Data transmission in the FANET is mainly supported by multi-hop communication, the traditional routing method comprises an active type, a reactive type, a position type and the like, and the active type routing brings huge expenses for maintaining a routing table; reactive routing faces a high time delay problem caused by frequent starting of a route discovery process in a high-dynamic unmanned aerial vehicle ad hoc network; the position type routing needs high-precision positioning equipment and is easy to face the problems of local optimization, routing holes and the like. Some existing researches are mainly directed to improving the traditional routing method, but some simple and efficient routing protocols suitable for the FANET are still lacked. The problems of frequent topology change, limited node energy, large network scale and the like in the FANET cause that a plurality of characteristics such as node speed, energy consumption and the like need to be considered when designing a routing protocol of the FANET. Therefore, in order to better improve the communication capacity of the large-scale unmanned aerial vehicle networking, the invention aims to solve the problem of designing a simple, efficient and robust routing protocol according to the network state information.
Disclosure of Invention
In order to solve the problems and effectively improve the communication performance of a large-scale unmanned aerial vehicle ad hoc network, the invention provides a hierarchical routing method based on fuzzy logic, which comprises the following steps:
s1, using an unmanned aerial vehicle in an unmanned aerial vehicle ad hoc network as a node, periodically broadcasting HELLO messages by all nodes, maintaining a neighbor table by any node, and updating neighbor table information after the node receives the HELLO messages of neighbor nodes;
s2, the node calculates the mobility of the node, judges whether the mobility of the node is smaller than the mobility of (n +1)/2 neighbor nodes in a neighbor table, wherein n is the number of the neighbor nodes, if yes, the node is a candidate cluster head, and executes the step S3;
s3, calculating an energy factor, a bandwidth factor and a position factor of the candidate cluster head;
s4, taking the energy factor, the bandwidth factor and the position factor as input, and calculating the fitness of the candidate cluster head to become the cluster head through fuzzy logic;
s5, the candidate cluster heads add the self fitness to the HELLO message for broadcasting, judge whether the self fitness is greater than the fitness of all the neighbor nodes, if so, declare the self cluster head identity to the neighbor nodes, and finish clustering;
and S6, carrying out route discovery and data transmission by the nodes through the backbone network established in the clustering mode.
Furthermore, the HELLO message sent by each node includes the node identity, the node position and the node mobility of the node; each node maintains a neighbor table that includes the identity, mobility, and fitness of each neighbor node to the node.
Further, after receiving the HELLO message of the neighbor node, the node extracts the node position thereof, and calculates the mobility of the node by adopting a distancing function, which is expressed as:
Figure BDA0003550438610000021
wherein, Mi(t) represents the mobility factor of node i at time t, n represents the total number of neighbor nodes of node i, dij(t) and dij(t ') denotes the Euclidean distance between node i and the neighboring node j at times t and t', respectively.
Further, the calculation formula in step S3 is:
energy factor:
Figure BDA0003550438610000031
bandwidth factor: BF (BF) generatori(t)＝CITR；
Position factor:
Figure BDA0003550438610000032
wherein E isr(t) is the energy remaining at node i at time t; einitTotal energy of initial communication of the node i, CITR is the channel idle time ratio of the node i at the time t, RdRepresents the maximum communication distance of node i, davg(t) represents the average distance of node i from its neighbors.
Further, the process of calculating the fitness of the candidate cluster head to become the cluster head through fuzzy logic comprises the following steps:
s11, fuzzy membership functions of the energy factor, the bandwidth factor and the position factor are respectively defined, and the numerical values of the energy factor, the bandwidth factor and the position factor of the candidate cluster head are respectively converted into fuzzy values through the fuzzy membership functions;
s12, calculating the grade of the candidate cluster head by using an IF/THEN rule based on fuzzy values of the energy factor, the bandwidth factor and the position factor;
and S13, defining an output membership function, and generating a final numerical value, namely fitness, through the output membership function and the grade of the candidate cluster head.
Further, the process of generating the fitness in step S13 includes:
s21, cutting and outputting a membership function by using a horizontal line according to the grade of the candidate cluster head;
s22, calculating the gravity center of a graph formed by enclosing of the cutting line and the x coordinate axis;
and S23, taking the abscissa of the gravity center as the fitness of the candidate cluster head.
Further, after network clustering is completed, if a node is in two or more clusters at the same time, the node becomes a gateway node.
Further, an on-demand mechanism is adopted for communication in the backbone network, when the source node sends data, whether the source node and the destination node are mutually neighbor nodes is judged, and if yes, the data is directly sent; if not, a route to the destination node is established by broadcasting a route request message to the backbone network.
The invention has the beneficial effects that:
the invention provides an unmanned aerial vehicle ad hoc network layered routing method based on fuzzy logic, which is used for dividing nodes in a network into different clusters by utilizing a clustering algorithm to construct a layered network architecture and reduce message flooding in a large-scale network aiming at the problems of high node speed, limited energy, frequent topology change and the like in the large-scale unmanned aerial vehicle ad hoc network.
In a clustering algorithm, candidate cluster head nodes are selected according to node mobility, so that nodes with high mobility can be prevented from participating in election of the cluster head nodes, network resource occupation is reduced, and cluster survival time is prolonged; aiming at the problems of delay and uncertainty of information acquisition in FANET, a fuzzy logic-based method is provided to comprehensively consider indexes such as node residual energy, bandwidth resources and distances between nodes to select an optimal cluster head node, and clustering efficiency is improved. In the routing stage, the route discovery and communication are carried out through the backbone network established by the cluster head and the gateway node, so that the expenditure caused by network flooding is reduced.
In summary, the invention can be applied to the large-scale unmanned aerial vehicle ad hoc network to construct a hierarchical network structure through a distributed clustering method, thereby reducing message flooding in the large-scale network, reducing message collision probability, improving communication performance, and simultaneously having lower complexity.
Drawings
FIG. 1 is a flow chart of a fuzzy logic based hierarchical routing method of the present invention;
FIG. 2 is a fuzzy membership function for energy factors according to the present invention;
FIG. 3 is a fuzzy membership function for the bandwidth factor of the present invention;
FIG. 4 is a position factor fuzzy membership function of the present invention;
FIG. 5 is a fuzzy logic rule map according to one embodiment of the present invention;
FIG. 6 is the output membership function definition of the present invention;
FIG. 7 is a center of gravity defuzzification of an embodiment of the present invention;
fig. 8 is a schematic diagram of route discovery according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an unmanned aerial vehicle ad hoc network layered routing method based on fuzzy logic, which is suitable for a large-scale unmanned aerial vehicle ad hoc network data transmission scene, and as shown in figure 1, the method comprises the following steps:
s1, using an unmanned aerial vehicle in an unmanned aerial vehicle ad hoc network as a node, periodically broadcasting HELLO messages by all nodes, maintaining a neighbor table by any node, and updating neighbor table information after the node receives the HELLO messages of neighbor nodes;
s2, the node calculates the mobility of the node, judges whether the mobility of the node is smaller than that of half of neighbor nodes in a neighbor list or not, if yes, the node is a candidate cluster head, and executes the step S3;
s3, calculating an energy factor, a bandwidth factor and a position factor of the candidate cluster head;
s4, taking the energy factor, the bandwidth factor and the position factor as input, and calculating the fitness of the candidate cluster head to become the cluster head through fuzzy logic;
s5, the candidate cluster heads add the self fitness to the HELLO message for broadcasting, judge whether the self fitness is greater than the fitness of all the neighbor nodes, if so, declare the self cluster head identity to the neighbor nodes, and finish clustering;
and S6, carrying out route discovery and data transmission by the nodes through the backbone network established in the clustering mode.
Preferably, the structure of the neighbor table maintained by each node is as shown in table 1, and the identity, mobility and fitness of each neighbor node of the node are included in the neighbor table;
TABLE 1 neighbor Table
Figure BDA0003550438610000051
The nodes update information in the neighbor table through HELLO messages periodically broadcast by the neighbor nodes, the format of the HELLO message frame refers to the HELLO message in the AODV routing protocol, and the HELLO message frame sent by each node further includes three data fields of the node identity, the node position, the node mobility and the like, as shown in table 2:
table 2HELLO message frame format
Figure BDA0003550438610000061
In an embodiment, after receiving a HELLO message broadcasted by a neighbor node, a node first updates mobility information of the corresponding neighbor node in a neighbor table, and extracts position information of the neighbor node in the HELLO message to calculate mobility of the node. In order to measure the mobility of nodes in a network, the concept of distancing function is introduced, which is a standard measure of the mobility of nodes.
Specifically, the process of calculating the mobility of the node itself is as follows: defining the total number of unmanned aerial vehicles in the unmanned aerial vehicle ad hoc network as N, namely the total number of nodes as N, wherein each node can obtain the position information of the self at the time t through a positioning device, and the position information is represented as Pi(t)＝{xi,yi,zi},i∈[1,N]At time t, the euclidean distance between node i and node j is:
Figure BDA0003550438610000062
defining the distancing between the node i and the node j at the time t as: rij(t)＝F(dij(t)), F (-) is a function of distance. Distancing varies in real time as the node moves, so node mobility can be calculated from the inverse of distancing with respect to time, as:
Figure BDA0003550438610000063
in this embodiment, an identity function is used as the F (-) function, i.e., Rij(t)＝dij(t), substituting it into the above equation, to obtain the mobility of node i with respect to node j at time t as:
Figure BDA0003550438610000064
and the mobility of the node i at the time t is represented as follows relative to the average value of the mobility sum of each neighbor node of the node i at the time t:
Figure BDA0003550438610000071
where n represents the total number of neighbor nodes of node i. After the node calculates the mobility of the node, the mobility of the node is compared with the mobility of the neighbor node maintained in the neighbor table, if the mobility of the node is smaller than the mobility of half or more neighbor nodes in the neighbor table, the node becomes a candidate cluster head, and the node continues to participate in the election of the cluster head.
In one embodiment, energy factors, bandwidth factors and position factors of all candidate cluster heads are calculated, and whether the candidate cluster heads can become the cluster heads is measured through the energy factors, the bandwidth factors and the position factors.
Specifically, in the hierarchical network structure, the cluster head may bear more traffic flow when establishing and maintaining the cluster structure and the inter-cluster routing, which results in more energy loss. In order to prevent frequent cluster head election due to too low cluster head energy, a node with more energy left in the network should be selected as a cluster head during cluster head election. In this embodiment, the energy factor is a ratio of the initial energy and the remaining energy of the node, so the energy factor of the node i at time t is represented as:
Figure BDA0003550438610000072
wherein E isr(t) is the energy remaining at node i at time t; einitIs the initial total energy of communication of the node i.
Specifically, in order to obtain good inter-cluster communication quality, effectively improve communication performance, and prevent congestion of a cluster head from causing a performance bottleneck, a node with good bandwidth resources should be selected as the cluster head. The bandwidth resource condition of the drone may be represented by a Channel Idle Time Ratio (CITR), where the drone obtains its own CITR value from the MAC layer, and after moving average by using a weighted index, the Channel Idle Time Ratio at this Time is defined as:
CITR←(1-δ)×CITRt-1+δ×CITRt
wherein the coefficient δ is set to 0.7; CITRt-1And CITRtRespectively represent the channel idle time ratio of the previous time and the current channel idle time ratio, and in this embodiment, the channel idle time ratio is used to represent the available bandwidth condition of the node, so the bandwidth factor of the node i at time t is represented as:
BFi(t)＝CITR。
specifically, in the hierarchical network structure, when a cluster head forwards data to a node in a cluster, the node farther from the cluster head has a higher signal attenuation degree, which results in unreliability of information interaction between clusters, so that the positions of members in the cluster need to be considered when selecting the cluster head. The position factor of the node i at the time t is defined as:
Figure BDA0003550438610000081
wherein R isdRepresents the maximum communication distance of node i, davg(t) represents the average distance between the node i and its neighbor nodes, and the calculation formula of the average distance is:
Figure BDA0003550438610000082
wherein n represents a neighbor node of node iThe number of points; dij(t) represents the Euclidean distance between node i and node j at time t.
In one embodiment, based on the energy factor, bandwidth factor and location factor of the candidate cluster head, the fitness of the candidate cluster head to the cluster head is calculated by using fuzzy logic, which includes fuzzification, IF/THEN rule mapping and defuzzification, and the specific process includes:
s11, fuzzy membership functions of the energy factor, the bandwidth factor and the position factor are respectively defined, and the numerical values of the energy factor, the bandwidth factor and the position factor of the candidate cluster head are respectively converted into fuzzy values through the fuzzy membership functions;
in particular, fuzzification in fuzzy logic is the process of converting numerical values into fuzzy values by defining fuzzy membership functions. The membership function of the energy factor is defined as shown in fig. 2, the energy level is divided into three degrees of { good, medium and bad }, the candidate cluster head judges the energy level of the candidate cluster head through the energy factor and the membership function of the energy factor, and as can be seen in fig. 2, when the energy factor is 0.8, the corresponding fuzzy value is that the membership degree is { good: 0.6, middle: 0.4, bad: 0 };
the membership functions of the bandwidth factors and the membership functions of the position factors are shown in fig. 3-4, the bandwidth level is divided into three degrees of good, medium and bad, the position level is divided into three degrees of near, medium and far, the candidate cluster head judges the bandwidth and position level of the candidate cluster head through the bandwidth factors, the position factors and the corresponding membership functions, and it can be seen in fig. 3-4 that when the bandwidth factors are 0.3, the corresponding fuzzy values, namely the membership degrees are good: 0.5, medium: 0.5, bad: 0, and when the position factor is 0.8, the corresponding fuzzy value, namely the membership is { near: 0.6, medium: 0.4, far: 0, wherein each membership function is defined based on the simulation results.
S12, calculating the grade of the candidate cluster head by using an IF/THEN rule based on fuzzy values of the energy factor, the bandwidth factor and the position factor;
specifically, after the candidate cluster head calculates the fuzzy value of each factor through the state information of the candidate cluster head, the candidate cluster head obtains the corresponding grade by using the IF/THEN rule, and the grade linguistic variable is defined as { excellent, good, medium, poor, and very poor }. The IF/THEN rule mapping is specifically defined as shown in Table 3:
TABLE 3IF/THEN rule mapping definitions
Figure BDA0003550438610000091
Figure BDA0003550438610000101
The rule 1 indicates that both the energy factor and the bandwidth factor of the candidate cluster head at this time are evaluated as good, and meanwhile, each neighbor node is closer to the candidate cluster head, so that the grade corresponding to the candidate cluster head is excellent.
In the IF/THEN rule mapping, the IF part is called "cause", the THEN part is called "result", and IF a plurality of rules are mapped at the same time, the evaluation results of the plurality of rule mappings need to be combined. Therefore, in the present embodiment, a Min-Max method is used, and for each rule, the minimum value of the cause is used as the result of the current rule; when different rules are combined, the maximum of the results is used.
Specifically, as shown in fig. 5, the energy level of the candidate cluster head is { good: 0.8, medium: 0.2, bad: 0, bandwidth level { good: 1, in: 0, bad: 0, position level { near: 0.6, medium: 0.4, far: 0, so the fuzzy set of the candidate cluster head maps four rules of 1, 2, 10 and 11. Where rule 1 is { good: 0.8, good: 1, near: 0.6, based on the Min-Max method, the minimum value of the cause is taken as the rule result, so the result of rule 1 is { excellent: 0.6}. Similarly, the results of rule 2, rule 10, and rule 11 are { good: 0.4}, { good: 0.2} and { medium: 0.2}. Since the rank of both rule 2 and rule 4 is "good", the maximum value of the results is used according to the rule of the Min-Max method, and therefore the degree of rank "good" is 0.4. In the above manner, all rules are combined to give a fuzzy result.
And S13, defining an output membership function, and generating a final numerical value, namely fitness, through the output membership function and the grade of the candidate cluster head.
And the candidate cluster head obtains a final numerical value result generated by the corresponding grade degree according to the output membership function and the rule mapping result, and the defuzzification process is completed. The value is used as a judgment standard for cluster head election and represents the suitability degree of the node becoming a cluster head.
The output membership function in the defuzzification is defined as shown in fig. 6, and the defuzzification first needs to cut the output membership function with a horizontal line according to the degree of rank obtained in the previous step, and for the rank { excellent: 0.6, good: 0.4, medium: 0.2, and a graph surrounded by the cutting line and the x-axis is shown in fig. 7, and the center of gravity of the shaded portion in fig. 7 is calculated, and the abscissa of the center of gravity is taken as the output value of the defuzzification. The abscissa of the center of gravity is calculated as follows:
Figure BDA0003550438610000111
wherein, FI represents the suitability degree of the candidate cluster head as the cluster head, and the higher the value is, the higher the priority of the candidate cluster head as the cluster head is; f (x) represents a curve of a hatched portion.
Preferably, after the HELLO message transmission period arrives, the candidate cluster head appends its FI value to the HELLO message and broadcasts the HELLO message to the neighboring node, and the HELLO message of the candidate cluster head appends a node as a suitability degree data field of the cluster head compared with the HELLO message of the common node. Through the method, all nodes in the network can obtain the FI values of the neighbor nodes, wherein the HELLO message of the neighbor node of the non-candidate cluster head does not contain an FI data field, and the FI value is defaulted to be 0. And then judging whether the FI value of the node is larger than the FI values of all the neighbor nodes, if so, changing the node identity data field in the HELLO message into a cluster head node, declaring the node to be a cluster head to the neighbor nodes, if not, the candidate cluster head becomes a thick inner common member, and the FI data field is not reserved in the HELLO message. If a node is in two or more clusters, the node becomes a gateway node and is responsible for data forwarding between the clusters, and the other nodes in the clusters are common member nodes of the clusters. The unmanned aerial vehicle ad hoc network in this embodiment carries out periodic clustering, accomplishes the clustering after the cluster head is selected, before carrying out next time clustering, the cluster head can not change, and the node in the cluster can leave this cluster, and the node outside the cluster also can join this cluster.
In one embodiment, after the clustering stage is completed, a backbone network is formed through clustering results, when nodes communicate in the backbone network, an on-demand mechanism of a reactive routing protocol is used, when a source node needs to transmit data, whether the source node and a destination node are mutually neighbor nodes is judged, and if yes, the data is directly sent; if not, a route to the destination node is established by broadcasting a route request message to the backbone network.
Specifically, during inter-cluster communication, if there is no direct route to the destination node, a route discovery process is started, where the route discovery process is as shown in fig. 8, and in the route discovery process in this embodiment, instead of finding a route by flooding an RREQ message to the entire network, an RREQ message is only broadcast to a backbone network established by a cluster head and a gateway node, that is, only the cluster head and the gateway node in the backbone network can forward the RREQ message, and the route discovery process is performed through the backbone network, so that overhead caused by network flooding can be relatively reduced. If the cluster head detects that the destination node is in the cluster, the RREQ message is forwarded to the destination node, and the destination node replies a RREP (route reply) message to the source node so as to establish a forward route.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (8)

1. An unmanned aerial vehicle ad hoc network layered routing method based on fuzzy logic is characterized by comprising the following steps:
s1, using an unmanned aerial vehicle in an unmanned aerial vehicle ad hoc network as a node, periodically broadcasting HELLO messages by all nodes, maintaining a neighbor table by any node, and updating neighbor table information after the node receives the HELLO messages of neighbor nodes;
s2, the node calculates the mobility of the node, judges whether the mobility of the node is smaller than the mobility of (n +1)/2 neighbor nodes in a neighbor table, wherein n is the number of the neighbor nodes, if yes, the node is a candidate cluster head, and executes the step S3;
s3, calculating an energy factor, a bandwidth factor and a position factor of the candidate cluster head;
s4, taking the energy factor, the bandwidth factor and the position factor as input, and calculating the fitness of the candidate cluster head to become the cluster head through fuzzy logic;
s5, the candidate cluster head adds the self fitness to a HELLO message for broadcasting, judges whether the self fitness is larger than the fitness of all the neighbor nodes of the candidate cluster head, and declares the self cluster head identity to the neighbor nodes if the self fitness is larger than the fitness of all the neighbor nodes, so that clustering is completed;
and S6, carrying out route discovery and data transmission by the nodes through the backbone network established in the clustering mode.
2. The unmanned aerial vehicle ad hoc network hierarchical routing method based on fuzzy logic of claim 1, wherein a HELLO message sent by each node includes its own node identity, node location and node mobility; each node maintains a neighbor table that includes the identity, mobility, and fitness of each neighbor node to the node.
3. The unmanned aerial vehicle ad hoc network hierarchical routing method based on fuzzy logic according to claim 2, wherein a node receives HELLO messages of neighbor nodes, extracts the node position of the neighbor nodes, and calculates the mobility of the node by adopting a distancing function, which is expressed as:
Figure FDA0003550438600000011
wherein M isi(t) represents the mobility factor of node i at time t, n represents the total number of neighbor nodes of node i, dij(t) and dij(t') is represented at t andand t' is the Euclidean distance between the node i and the neighbor node j.
4. The unmanned aerial vehicle ad hoc network hierarchical routing method based on fuzzy logic according to claim 1, wherein the calculation formula in step S3 is:
energy factor:
Figure FDA0003550438600000021
bandwidth factor: BF (BF) generatori(t)＝CITR；
Position factor:
Figure FDA0003550438600000022
wherein E isr(t) is the energy remaining at node i at time t; einitIs the initial total communication energy of the node i, CITR is the channel idle time ratio of the node i at the time t, RdRepresents the maximum communication distance of node i, davg(t) represents the average distance of node i from its neighbors.
5. The unmanned aerial vehicle ad hoc network hierarchical routing method based on fuzzy logic according to claim 1, wherein the process of calculating the suitability of the candidate cluster head to become the cluster head through fuzzy logic comprises:
s11, fuzzy membership functions of the energy factor, the bandwidth factor and the position factor are respectively defined, and the numerical values of the energy factor, the bandwidth factor and the position factor of the candidate cluster head are respectively converted into fuzzy values through the fuzzy membership functions;
s12, calculating the grade of the candidate cluster head by using an IF/THEN rule based on fuzzy values of the energy factor, the bandwidth factor and the position factor;
and S13, defining an output membership function, and generating a final numerical value, namely fitness, through the output membership function and the grade of the candidate cluster head.
6. The unmanned aerial vehicle ad hoc network hierarchical routing method based on fuzzy logic of claim 5, wherein the step S13 generating fitness comprises:
s21, cutting and outputting a membership function by using a horizontal line according to the grade of the candidate cluster head;
s22, calculating the gravity center of a graph formed by enclosing of the cutting line and the x coordinate axis;
and S23, taking the abscissa of the gravity center as the fitness of the candidate cluster head.
7. The unmanned aerial vehicle ad hoc network hierarchical routing method based on fuzzy logic according to claim 1, wherein after network clustering is completed, if a node is in two or more clusters at the same time, the node becomes a gateway node.
8. The unmanned aerial vehicle ad hoc network hierarchical routing method based on fuzzy logic of claim 1, wherein an on-demand mechanism is adopted for communication in a backbone network, when a source node sends data, whether the source node and a destination node are mutually neighbor nodes is judged, and if yes, the data is directly sent; if not, a route to the destination node is established by broadcasting a route request message to the backbone network.
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CN115865775A (en) * 2022-11-29 2023-03-28 南京航空航天大学 Unmanned aerial vehicle network fast routing recovery method based on OLSR
CN115865775B (en) * 2022-11-29 2024-01-05 南京航空航天大学 Unmanned aerial vehicle network rapid route recovery method based on OLSR

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