Mobile ad hoc network clustering networking strategy based on GWO in satellite-ground fusion scene

Abstract

The invention relates to the technical field of mobile ad hoc network communication in satellite-ground fusion, in particular to a mobile ad hoc network clustering networking strategy based on GWO in a satellite-ground fusion scene, and the networking strategy comprises an information collection stage, an information transmission stage, a cluster establishment stage and a cluster maintenance stage. A dual-mode communication node transmits network information to a command center through a low-orbit satellite, and performs clustering by executing a wild wolf optimization algorithm with an optimized fitness function from a global perspective; according to the method, the problems of low convergence speed and large resource consumption caused by distributed calculation in a traditional mobile ad hoc network clustering algorithm are solved, adaptive optimization is performed on the fitness function of the grey wolf optimization algorithm, the algorithm is prevented from falling into local optimum, and the efficiency and rationality of the algorithm are improved. The energy consumption of the whole network can be reduced, so that the service life of the whole network is prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of mobile ad hoc network communication in satellite-ground fusion, and in particular relates to a mobile ad hoc network clustering networking strategy based on GWO in a satellite-ground fusion scenario. Background Art

[0002] Mobile ad hoc networks are widely used in disaster relief, military communications, and distributed collaborative communications, due to their self-organization, temporary, multi-hop transmission, and independence from infrastructure. At the same time, low-orbit satellite networks, due to their wide coverage, large communication capacity, and flexible network construction, can effectively expand and supplement the shortcomings of ground-based mobile ad hoc networks when combined with ground-based mobile ad hoc networks: in dynamically changing environments (such as battlefields or disaster areas), the wide coverage of satellite networks can support the flexibility and mobility of mobile nodes and adapt to rapid changes in network topology; dual-mode communication terminals that integrate low-orbit satellite and ad hoc network communications can avoid the network limitations caused by the need for dedicated satellite gateways for traditional satellite-to-ground communications, thereby improving the flexibility and scalability of the entire network.

[0003] In the scenario of satellite network and ground mobile ad hoc network integration, existing research often uses network technologies such as SDN and NFV to decouple heterogeneous networks, thereby designing mobile ad hoc networks and satellite networks respectively. For ground ad hoc networks, in order to obtain global network information, nodes will flood a large amount of detection information, which not only increases the computing overhead of nodes, but also leads to problems such as slow network convergence. For this reason, researchers have proposed a hierarchical network structure, that is, nodes are divided into small groups called clusters, and one node in the cluster is elected as the cluster head. The current clustering algorithms mainly include: based on the minimum ID algorithm, the highest degree algorithm, etc., comprehensively consider multiple factors and perform on-demand weighting, and finally perform clustering according to weights. However, the factors considered in the algorithm weights depend on the environment. When the network situation changes, the weight calculation cost will also increase accordingly. At the same time, the nodes of the mobile ad hoc network are distributed. Each node decides to establish clusters, enter clusters, and elect cluster heads based on the information interacted with neighboring nodes. When the number of ad hoc network nodes increases, the ad hoc network nodes will consume a lot of resources for clustering operations, resulting in slow network convergence and increased energy consumption. With the development of intelligent algorithms, bionic clustering algorithms designed based on the idea of ​​biological clustering have attracted widespread attention. While ensuring the advantages of traditional clustering algorithms, they also absorb the characteristics of bionic algorithms such as strong adaptability and self-organization. For example, ant colony clustering algorithms, gray wolf optimization algorithms (GWO) and firefly algorithms have been applied to clustering algorithms. However, these algorithms are mostly used in scenarios with poor mobility such as wireless sensor networks, and may easily fall into local optimality; at the same time, in emergency scenarios where energy may not be supplied, self-organizing network nodes running such algorithms may consume a lot of resources, thereby reducing the life of the entire network. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the present invention provides a mobile ad hoc clustering networking strategy based on GWO in a satellite-ground fusion scenario, and the networking strategy includes: information collection stage, information transmission stage, cluster establishment stage and cluster maintenance stage;

[0005] In the information collection phase: the mobile ad hoc network node broadcasts a HELLO information packet within its communication range; the mobile ad hoc network node receives the HELLO message from other nodes and maintains a node information table, where the node information table includes: the node number, the node's remaining energy, the node's location, the node's moving speed, the node's communication strength with the satellite node, and the node's next hop node number;

[0006] In the information transmission stage: the dual-mode communication node in the mobile ad hoc network selects the optimal satellite node according to the uplink load between it and the satellite node, the signal strength between it and the satellite node, the visibility between it and the satellite node, and the communication delay between it and the satellite node, and sends its node information table to the command center, which parses and integrates the node information of the mobile ad hoc network;

[0007] In the cluster establishment stage, the command center uses the optimized gray wolf optimization algorithm to cluster the nodes in the mobile ad hoc network according to the node information of the mobile ad hoc network, and sends the clustering results to the dual-mode communication nodes through the satellite nodes, and the dual-mode communication nodes broadcast the clustering results in the mobile ad hoc network;

[0008] In the cluster maintenance phase, the node information table of each mobile ad hoc network node maintains the node information of the cluster to which it belongs, and performs cluster member leaving, joining and cluster head updating operations based on the maintained information. Among them, the cluster head node maintains the information of both nodes within the cluster and nodes outside the cluster.

[0009] Preferably, the dual-mode communication node selects the optimal satellite node including:

[0010] Dual-mode communication node N i According to its relationship with satellite node S j The uplink load of the satellite node S j The signal strength of the satellite node S j The visibility of the satellite node S j The communication delay calculation of satellite node S j The satellite node with the highest score is selected as the optimal satellite node. j Scores include:

[0011] SV ij =ω1·(1-U ij )+ω2·RSS ij +ω3·V ij-ω4·D ij

[0012] Among them, SV ij For dual-mode communication node N i , satellite node S j The score of each factor; ω1, ω2, ω3 and ω4 represent the weight parameters of each factor; U ij Represents dual-mode communication node N i With satellite node S j Uplink load; RSS ij Represents dual-mode communication node N i With satellite node S j The signal strength of V ij Represents dual-mode communication node N i With satellite node S j Visibility; D ij Represents dual-mode communication node N i With satellite node S j communication delay.

[0013] Preferably, the dual-mode communication node N i With satellite node S j Visibility V ij include:

[0014]

[0015] Among them, θ ij Represents dual-mode communication node N i With satellite node S j The zenith angle is expressed as:

[0016]

[0017] in, and Respectively represent the dual-mode communication node N i and satellite node S j The longitude and latitude, θ threshold Indicates setting a threshold.

[0018] Preferably, clustering the nodes in the mobile ad hoc network using the optimized grey wolf optimization algorithm comprises:

[0019] Step 101: Initialize the population size, population dimension and maximum number of iterations, and randomly initialize the position of each individual gray wolf, wherein the position of each individual gray wolf corresponds to a cluster head node selection scheme;

[0020] Step 102: construct an optimized fitness function. The optimized fitness function F is as follows:

[0021] F=ψ1·f1+ψ2·f2+ψ3·f3+ψ4·f4-ψ5·f5

[0022] Among them, f1 represents the relative speed between the cluster head node and its neighboring nodes; f2 represents the residual energy fraction of the cluster head node; f3 represents the shortest distance between cluster head nodes; f4 and f5 represent the reward factor and penalty factor, which are used to ensure that there are dual-mode communication nodes in each cluster and encourage dual-mode communication nodes to become cluster heads; ψ1, ψ2, ψ3, ψ4 and ψ5 are their weights, and ψ1+ψ2+ψ3+ψ4+ψ5=1;

[0023] Step 103: Select the nearest cluster head node from other nodes according to the geographical location distance, and calculate the fitness value of each gray wolf based on the constructed fitness function; determine the α wolf, β wolf and δ wolf according to the fitness value of each gray wolf;

[0024] Step 104: Update the position of the gray wolf according to the positions of the α wolf, the β wolf and the δ wolf;

[0025] Step 105: Calculate the fitness value of the gray wolf after the position is updated; re-determine the α wolf, β wolf and δ wolf according to the fitness value of the gray wolf after the update;

[0026] Step 106: If the current round reaches the maximum number of iterations, the iteration ends, the optimal individual is output, and the clustering solution with the final maximum fitness value is obtained;

[0027] Step 107: If the current round does not reach the maximum number of iterations, go to step 104.

[0028] Preferably, updating the position of the gray wolf according to the positions of the α wolf, the β wolf and the δ wolf comprises:

[0029]

[0030]

[0031] Among them, F1, F2 and F3 are the optimized weight factors, F α 、F β and F δ They represent the fitness of α wolf, β wolf and δ wolf in the grey wolf algorithm respectively; Indicates the location of the gray wolf individual after update.

[0032] Preferably, the relative speed between the cluster head node and its neighbor nodes includes:

[0033]

[0034] Among them, NUM i Represents the cluster head node N i The number of neighbor nodes; represents the moving speed of the cluster head node; V j Cluster head node The moving speed of neighbor nodes.

[0035] Preferably, the residual energy score of the node includes:

[0036]

[0037] Among them, E max represents the maximum energy of the cluster head node; Represents the residual energy of the cluster head node.

[0038] Preferably, the distance between the cluster head nodes includes:

[0039]

[0040]

[0041] in, and Represents the cluster head node CH i′ The longitude and latitude coordinates of and Represents the cluster head node CH i The longitude and latitude coordinates of the earth Represents the radius of the Earth.

[0042] Preferably, the reward factor and penalty factor include:

[0043] f4=1+ρ1·τ

[0044] f5=ρ2(1-τ)

[0045]

[0046] Wherein, ρ1 and ρ2 represent a constant greater than 0.

[0047] Preferably, the cluster member leaving, joining and cluster head updating operations include:

[0048] After the mobile ad hoc network is clustered, the cluster head CH i The cluster head identifier is inserted into the HELLO message, and the ordinary node N i According to the HELLO information, the location and moving speed of the cluster head are obtained, and the distance between the cluster head and itself is known. and relative speed

[0049] Departure of cluster members: When there are nodes in the cluster that satisfy When thresholdIf is the relative speed threshold, the node is removed from the cluster;

[0050] Cluster member joining: When there is a node outside the cluster that satisfies When , the out-cluster node is added to the cluster;

[0051] Cluster head update: When the remaining energy of the cluster head node is lower than the threshold, the cluster head broadcasts the cluster head update information, and the nodes in the cluster calculate the score R according to their own remaining energy and the relative movement speed with other nodes in the cluster. i The specific scoring calculation formula is as follows:

[0052]

[0053] Among them, R i Represents the nodes N in the cluster i The score of NOC is σ1 and σ2, which represent the weight of each factor. i Represents the number of nodes in the cluster; V i and V j N represents the number of nodes in the cluster i and N j Movement speed; E max is the maximum energy of the node; E i Represents the nodes N in the cluster i The remaining energy

[0054] After each node in the cluster calculates its own score, it broadcasts a HELLO message with its own score;

[0055] When each node in the cluster receives a HELLO message with a higher score than its own, the node will no longer broadcast the HELLO message with its own score;

[0056] When a node broadcasts a HELLO message with a score and does not receive HELLO messages with scores from other nodes within a preset time period, the node becomes the new cluster head.

[0057] The present invention has at least the following beneficial effects

[0058] In the satellite-ground fusion scenario, the present invention collects network information from dual-mode communication terminal devices by utilizing the wide coverage of the satellite network, and then transmits it to a high-computing-power command center. The command center uses GWO to perform clustering networking from a global network perspective, thereby improving the efficiency of clustering operations of mobile ad hoc networks, reducing clustering computing overhead and energy consumption of ad hoc network nodes, and thus improving the life of the entire network; in addition, through the optimized gray wolf optimization algorithm, the fitness function of the gray wolf optimization algorithm is adaptively optimized to avoid the algorithm from falling into a local optimum, thereby improving the efficiency and rationality of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of a satellite-ground fusion scenario in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0061] See also Figure 1 The present invention provides a mobile ad hoc network clustering strategy based on GWO in a satellite-ground fusion scenario, including:

[0062] The networking strategy includes: information collection phase, information transmission phase, cluster establishment phase and cluster maintenance phase;

[0063] In the information collection phase: the mobile ad hoc network node broadcasts a HELLO information packet within its communication range; the mobile ad hoc network node receives the HELLO message from other nodes and maintains a node information table, where the node information table includes: the node number, the node's remaining energy, the node's location, the node's moving speed, the node's communication strength with the satellite node, and the node's next hop node number;

[0064] In the information transmission stage: the dual-mode communication node in the mobile ad hoc network selects the optimal satellite node according to the uplink load between it and the satellite node, the signal strength between it and the satellite node, the visibility between it and the satellite node, and the communication delay between it and the satellite node, and sends its node information table to the command center, which parses and integrates the node information of the mobile ad hoc network;

[0065] In the cluster establishment stage, the command center uses the optimized gray wolf optimization algorithm to cluster the nodes in the mobile ad hoc network according to the node information of the mobile ad hoc network, and sends the clustering results to the dual-mode communication nodes through the satellite nodes, and the dual-mode communication nodes broadcast the clustering results in the mobile ad hoc network;

[0066] In the cluster maintenance phase, the node information table of each mobile ad hoc network node maintains the node information of the cluster to which it belongs, and performs cluster member leaving, joining and cluster head updating operations based on the maintained information. Among them, the cluster head node maintains the information of both nodes within the cluster and nodes outside the cluster.

[0067] In the present invention, the clustering strategy of mobile ad hoc networking includes: information collection stage, information transmission stage, cluster establishment stage and cluster maintenance stage.

[0068] In the information collection phase, the mobile ad hoc network nodes transmit their own node information by broadcasting HELLO information packets. By receiving HELLO information, each node builds and maintains a node information table;

[0069] During the information transmission stage, the dual-mode communication nodes in the mobile ad hoc network send network node information to the command center via satellite, and the command center integrates the network node information.

[0070] In the cluster establishment stage, the command center executes the Grey Wolf Optimization Algorithm to perform clustering calculations and selects cluster head nodes through the optimized fitness function. Finally, the command center broadcasts the clustering results to the mobile ad hoc network through satellites and dual-mode communication nodes.

[0071] In the cluster maintenance phase, the node information table of each mobile ad hoc network node only needs to maintain the node information within the cluster, thereby saving energy consumption. At the same time, cluster members leave and join and cluster head update operations are performed based on mutual information exchange.

[0072] See also Figure 1 , the satellite-ground fusion scenario diagram used in the embodiment of the present invention, in which N mobile ad hoc network nodes N are deployed i (i=1,2,...,N), the nodes of the mobile ad hoc network can be divided into dual-mode communication nodes Nd according to their communication capabilities i (i=1,2,...,d) and common ad hoc network nodes Ns i (i=1,2,...,s), the communication range is r, and they can establish communication connections with other ad hoc network nodes within the communication range. Since the deployment cost of dual-mode communication nodes is higher than that of ordinary ad hoc network nodes, the number of dual-mode communication nodes d is less than that of ordinary ad hoc network nodes s, and d+s=N.

[0073] In the satellite-ground fusion scenario, M low-orbit satellites S are deployed. j (j=1,2,...,M), which has wide coverage and can establish communication connection with dual-mode communication nodes in mobile ad hoc networks. A command center is also deployed, and its energy is almost unlimited.

[0074] At the same time, in order to ensure that the mobile ad hoc network is more comprehensive and accurate in the information collection stage, the dual-mode communication nodes are evenly distributed in the entire mobile ad hoc network, which also ensures that in the subsequent clustering operation, there are dual-mode communication nodes in each cluster as much as possible.

[0075] Considering the beneficial role of low-orbit satellites in effectively expanding the communication range of mobile ad hoc networks, Figure 1The present invention makes full use of the satellite-ground fusion scenario, and delegates the clustering computing task to the command center with nearly unlimited energy through dual-mode communication nodes and low-orbit satellites. The mobile self-organizing network nodes only need to perform information collection tasks, thereby removing the computing burden of the mobile self-organizing network nodes and reducing the energy consumption of the entire network. This will increase the life of the entire network.

[0076] When the mobile ad hoc network is just established, all nodes are in an independent state. Each node first obtains its own node information, including the node number and the node's remaining energy e i , the location of the node ( and φ i They are ad hoc network nodes N i The geographical longitude and latitude of the node and the moving speed of the node Each dual-mode communication node N i Additional acquisition of its satellite node S j Communication Strength RSS ij , the present invention mainly considers free space path loss and atmospheric attenuation:

[0077] RSS ij =P t +G t +G r -FSPL-AAL

[0078] Among them, P t , G t and G r They are the transmit power of the transmitter on the satellite, the transmit antenna gain and the receive antenna gain of the dual-mode node. FSPL represents the free space path loss and AAL represents the atmospheric attenuation loss. FSPL is the signal strength attenuation caused by the increase in distance, which is specifically expressed as:

[0079] FSPL=201g(d)+20lg( / )+201g(4π / c)

[0080] Where d is the distance between the ground dual-mode communication node and the satellite, f is the frequency of the communication signal, and c is the speed of light, which is approximately 3×10 8 m / s, lg represents the logarithmic function whose base is a natural number.

[0081] The distance between the ground dual-mode communication node and the satellite node can be expressed as:

[0082]

[0083]

[0084] in, It is a satellite node S j The longitude and latitude, θ ij Satellite node S j With the ground dual-mode communication node N i The zenith angle between earth represents the radius of the earth; h represents the satellite node S j orbital altitude.

[0085] AAL mainly considers rain attenuation rain and cloud vapor attenuation A cloud The impact on the signal is specifically expressed as:

[0086] ALL=A rain +A cloud

[0087] A rain =γ r ·d r

[0088] A cloud =γ c ·d c

[0089] Among them, γ r and γ c is the rain and cloud attenuation coefficient, d r and d v Indicates the distance the signal travels through rain and clouds.

[0090] After the mobile ad hoc network node obtains its own node information, each node periodically broadcasts the HELLO message, which contains the node's own number and the node's own remaining energy E i , the location of the node itself RSS of the communication strength between the node itself and the satellite i To avoid HELLO message flooding, its TTL is set to 1.

[0091] In ordinary ad hoc network nodes, the communication strength between them and satellite nodes is set to empty. If the dual-mode communication node and the satellite node cannot communicate due to terrain or other reasons, the communication strength is 0. This can distinguish the two and is also helpful for identifying whether the node is a dual-mode communication node.

[0092] The mobile ad hoc network node builds and maintains a node information table by receiving HELLO messages from other nodes, which includes the node number, the node's remaining energy E i , the location of the node Node movement speed Communication strength RSS between the node and the satellite node ij, and the node's next hop node number. The next hop node number indicates the relay node to which the information is to be sent to the current entry node. If the current entry node number and the next hop node number are the same, it indicates a one-hop neighbor node.

[0093] Mobile ad hoc network nodes periodically broadcast node information tables. By receiving node information tables of other nodes, mobile ad hoc network nodes can obtain information of other nodes more than two hops away and update it to their own node information tables. The next hop node number is filled in with the node number that sent the node information table.

[0094] To avoid flooding of such message packets, the TTL is set to 2. At this time, the node can accurately know the correct previous hop node of its own three-hop node.

[0095] In the information transmission phase: After the information collection phase, the dual-mode communication node will periodically send its node information table to the command center via satellite. When the dual-mode communication node selects the transmission satellite, it comprehensively considers the uplink load U ij , Signal Strength RSS ij , Visibility V ij and communication delay D ij For uplink load level U ij , specifically described as:

[0096]

[0097] Among them, B used Indicates the current link bandwidth, B total Indicates the total bandwidth capacity.

[0098] Load level U ij Higher links are likely to be congested, so satellite nodes with low load levels are usually selected.

[0099] For signal strength RSS ij ,The collective characterization is the same as in the information collection phase, the stronger the signal strength, the better the communication quality and the lower bit error rate.

[0100] For visibility V ij , which can be obtained by the dual-mode communication node and the zenith angle θ of the satellite ij Make a judgment:

[0101]

[0102] Among them, the zenith angle θ ij The characterization is the same as in the information collection phase. When θ i Less than a certain threshold θ threshold , indicating that the satellite is within the visible range;

[0103]

[0104] in, and Respectively represent the dual-mode communication node N i and satellite node S j The longitude and latitude, θ threshold Indicates setting a threshold.

[0105] For the communication delay D ij , specifically described as:

[0106]

[0107] Dual-mode communication nodes will give priority to satellites with shorter distances and lower latency, where c represents the speed of light.

[0108] Therefore, taking the above factors into consideration, a multi-factor model is used for weighted summation to obtain the comprehensive score of each satellite, which is collectively described as follows:

[0109] SV ij =ω1·(1-U ij )+ω2·RSS ij +ω3·V ij -ω4·D ij

[0110] Among them, ω1, ω2, ω3 and ω4 are the weights of each factor, and ω1+ω2+ω3+ω4=1.

[0111] The ground dual-mode communication terminal determines one or more satellite SVs within the communication range. ij , select satellites with high comprehensive scores for transmission.

[0112] In the cluster formation stage: after the satellite relay transmission, the command center integrates the received node information table and restores the entire network topology, and then executes the gray wolf optimization algorithm to elect the mobile ad hoc network cluster head, and the cluster is formed. The specific steps are as follows:

[0113] The gray wolf optimization algorithm simulates the gray wolf hunting process and finds the optimal solution through the collaborative hunting of leading wolves and following wolves. In cluster formation, each gray wolf represents a cluster head selection scheme, and the optimal gray wolf is the optimal cluster head allocation.

[0114] Introducing the Grey Wolf Optimization Algorithm into the cluster head election of mobile ad hoc networks in the satellite-ground fusion scenario requires full consideration of the impact of mobility and dual-mode communication nodes. Therefore, it is necessary to adapt the fitness function in the Grey Wolf Optimization Algorithm.

[0115] The fitness function design comprehensively considers the node mobility, node residual energy fraction, node location and the special characteristics of dual-mode nodes in the mobile ad hoc network, and is designed as follows:

[0116] F=ψ1·f1+ψ2·f2+ψ3·f3+ψ4·f4-ψ5·f5

[0117] Among them, f1 represents the relative speed between the cluster head node and its neighboring nodes; f2 represents the residual energy fraction of the cluster head node; f3 represents the shortest distance between cluster head nodes; f4 and f5 represent reward factors and penalty factors, which are used to ensure that there are dual-mode communication nodes in each cluster and encourage dual-mode communication nodes to become cluster heads; ψ1, ψ2, ψ3, ψ4 and ψ5 are their weights, and ψ1+ψ2+ψ3+ψ4+ψ5=1; among them, when initializing the position of each gray wolf individual, the mobile ad hoc network nodes can be first mapped to the position space, for example, all nodes in the mobile ad hoc network are sorted, and their sorting numbers are used as their positions, and then the position of each gray wolf individual is initialized, and at the same time, it must be satisfied that the positions of the gray wolf individuals in all dimensional spaces are different.

[0118] For example, clustering nodes in a mobile ad hoc network using an optimized grey wolf optimization algorithm includes:

[0119] After the command center collects the information of the nodes in the ad hoc network, it determines the number of cluster heads according to the following steps:

[0120] The communication range of the ad hoc network node covers an area of ​​A = πr 2 , the area of ​​the entire ad hoc network is A total According to the approximate calculation based on the collected location information, the number of cluster heads is preliminarily estimated to be This value can be adjusted dynamically according to specific circumstances later.

[0121] The steps to execute the Gray Wolf optimization algorithm are as follows:

[0122] Step 101: Initialize the population size, population dimension and maximum number of iterations T, and initialize and in is the convergence coefficient that decreases linearly in the interval [2,0], and Are coefficient vectors, the calculation formulas are:

[0123]

[0124]

[0125] Among them, r1 and r2 are random numbers between 0 and 1.

[0126] At the same time, the position of each gray wolf individual is randomly initialized, where the position of each gray wolf individual corresponds to a cluster head selection scheme, which is represented as a k pre×1 matrix, each parameter corresponds to the ad hoc network node id, and the ids are arranged from small to large.

[0127] Step 102: Then, an optimized fitness function is constructed. The optimized fitness function F is as follows:

[0128] F=ψ1·f1+ψ2·f2+ψ3·f3+ψ4·f4-ψ5·f5

[0129] Step 103: Select the nearest cluster head node from other nodes according to the geographical location distance, and calculate the fitness value of each gray wolf based on the constructed fitness function; determine the α wolf, β wolf and δ wolf according to the fitness value of each gray wolf;

[0130] Step 104: Update the position of the gray wolf according to the positions of the α wolf, the β wolf and the δ wolf;

[0131] Step 105: Calculate the fitness value of the gray wolf after the position is updated; re-determine the α wolf, β wolf and δ wolf according to the fitness value of the gray wolf after the update, and update and

[0132] Step 106: If the current round reaches the maximum number of iterations, the iteration ends, the optimal individual is output, and the clustering solution with the final maximum fitness value is obtained;

[0133] Step 107: If the current round does not reach the maximum number of iterations, go to step 104.

[0134] Preferably, updating the position of the gray wolf according to the positions of the α wolf, the β wolf and the δ wolf comprises:

[0135]

[0136]

[0137] Among them, F1, F2 and F3 are the optimized weight factors, F α 、F β and F δ They represent the fitness of α wolf, β wolf and δ wolf in the grey wolf algorithm respectively; Indicates the location of the individual gray wolf after update.

[0138] After the iteration is completed, the final α wolf is obtained, that is, the final cluster head set is obtained.

[0139] For node mobility, the relative speed between the cluster head candidate node and other neighboring nodes is mainly considered. When the relative speed between the node and its neighboring nodes is low, it should be more suitable to become a cluster head, and its intra-cluster connection will be more stable, thereby reducing cluster head reselection. The specific characterization is as follows:

[0140]

[0141] Among them, NUM i Represents the cluster head node N i The number of neighbor nodes; represents the moving speed of the cluster head node; V j Cluster head node The moving speed of neighbor nodes.

[0142] For the node residual energy score, nodes with higher residual energy scores should be selected as much as possible to ensure that the cluster head can continue to work. The specific description is:

[0143]

[0144] Among them, E max represents the maximum energy of the cluster head node; Represents the residual energy of the cluster head node.

[0145] For the node location, cluster heads should be distributed as evenly as possible in the mobile ad hoc network. Therefore, the distance between cluster heads is introduced into the fitness function. i and N i' The distance between them is specifically described as follows:

[0146]

[0147]

[0148] in, and Cluster head node CHi' The longitude and latitude coordinates of and Cluster head node CHi The longitude and latitude coordinates of the earth Represents the radius of the Earth.

[0149] For dual-mode communication nodes, they are special. We should try to make dual-mode communication nodes exist in each cluster to maintain the connection between the mobile ad hoc network and the low-orbit satellite. Therefore, we introduce a reward factor to encourage dual-mode communication nodes to become cluster heads, and introduce a penalty factor to increase the penalty when there are no dual-mode communication nodes in the cluster. The specific description is:

[0150] Factors and penalty factors include:

[0151] f4=1+ρ1·τ

[0152] f5=ρ2(1-τ)

[0153]

[0154] Wherein, ρ1 and ρ2 represent a constant greater than 0.

[0155] In summary, the final optimized fitness function F can be written as:

[0156]

[0157] Using the optimized fitness function F, the original position update strategy in the gray wolf optimization algorithm is Updated as follows:

[0158]

[0159]

[0160] Among them, F1, F2 and F3 are the optimized weight factors, F α 、F β and F δ They represent the fitness of α wolf, β wolf and δ wolf in the grey wolf algorithm respectively; Indicates the location of the individual gray wolf after update.

[0161] According to the optimized fitness function, the fitness of each gray wolf (i.e., ad hoc network node) is calculated, and finally the cluster head election is completed.

[0162] After the cluster head election is completed, other nodes select the nearest cluster head node to join based on the geographical distance.

[0163] Subsequently, the command center broadcasts the clustering results to the mobile ad hoc network via satellite, and the ad hoc network nodes execute the clustering results.

[0164] Through the information collection phase, the mobile ad hoc network nodes learn the node information of the entire network, but the mobile ad hoc network nodes move randomly, and the topological connection is time-varying. Maintaining frequently changing network information will seriously consume the node energy. Therefore, in order to reduce the storage overhead of the node, the cluster member nodes only need to maintain the node information within the cluster.

[0165] Step 4. After the cluster establishment phase, the nodes in the mobile ad hoc network know the cluster and cluster head they are in. However, the nodes in the mobile ad hoc network have random mobility, and the establishment and disconnection of links between nodes are very common. Based on the relationship between position and relative speed, the reconstruction of clusters or links can be reduced as much as possible, thereby reducing the number of control information transmissions and thus reducing the overhead of the entire network.

[0166] 11. The cluster member leaving, joining and cluster head updating operations include:

[0167] After the mobile ad hoc network is clustered, the cluster head CH iThe cluster head identifier is inserted into the HELLO message, and the ordinary node N i According to the HELLO information, the location and moving speed of the cluster head are obtained, and the distance between the cluster head and itself is known. and relative speed

[0168] Departure of cluster members: When there are nodes in the cluster that satisfy When threshold If is the relative speed threshold, the node is removed from the cluster;

[0169] Cluster member joining: When there is a node outside the cluster that satisfies When , the out-cluster node is added to the cluster;

[0170] Cluster head update: When the remaining energy of the cluster head node is lower than the threshold, the cluster head broadcasts the cluster head update information, and the nodes in the cluster calculate the score R according to their own remaining energy and the relative movement speed with other nodes in the cluster. i The specific scoring calculation formula is as follows:

[0171]

[0172] Among them, R i Represents the nodes N in the cluster i The score of NOC is σ1 and σ2, which represent the weight of each factor. i Represents the number of nodes in the cluster; V i and V j N represents the number of nodes in the cluster i and N j Movement speed; E max is the maximum energy of the node; E i Represents the nodes N in the cluster i The remaining energy

[0173] After each node in the cluster calculates its own score, it broadcasts a HELLO message with its own score;

[0174] When each node in the cluster receives a HELLO message with a higher score than its own, the node will no longer broadcast the HELLO message with its own score;

[0175] When a node broadcasts a HELLO message with a score and does not receive HELLO messages with scores from other nodes within a preset time period, the node becomes the new cluster head.

[0176] In summary, in the satellite-ground fusion scenario, the present invention utilizes the wide coverage of the satellite network to collect network information from dual-mode communication terminal devices, and then transmits it to a high-computing-power command center. The command center uses GWO to perform clustering networking from a global network perspective, thereby improving the efficiency of mobile ad hoc network clustering operations, reducing clustering computing overhead and energy consumption of ad hoc network nodes, and thus improving the life of the entire network; in addition, through the optimized gray wolf optimization algorithm, the fitness function of the gray wolf optimization algorithm is adaptively optimized to avoid the algorithm from falling into the local optimum, thereby improving the efficiency and rationality of the algorithm.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A clustering networking strategy for mobile ad hoc networks based on GWO in a satellite-ground fusion scenario, characterized in that: The networking strategy includes: information collection phase, information transmission phase, cluster establishment phase and cluster maintenance phase; In the information collection phase: the mobile ad hoc network node broadcasts a HELLO information packet within its communication range; the mobile ad hoc network node receives the HELLO message from other nodes and maintains a node information table, where the node information table includes: the node number, the node's remaining energy, the node's location, the node's moving speed, the node's communication strength with the satellite node, and the node's next hop node number; In the information transmission stage: the dual-mode communication node in the mobile ad hoc network selects the optimal satellite node according to the uplink load between it and the satellite node, the signal strength between it and the satellite node, the visibility between it and the satellite node, and the communication delay between it and the satellite node, and sends its node information table to the command center, which parses and integrates the node information of the mobile ad hoc network; In the cluster establishment stage, the command center uses the optimized gray wolf optimization algorithm to cluster the nodes in the mobile ad hoc network according to the node information of the mobile ad hoc network, and sends the clustering results to the dual-mode communication nodes through the satellite nodes, and the dual-mode communication nodes broadcast the clustering results in the mobile ad hoc network; In the cluster maintenance phase, the node information table of each mobile ad hoc network node maintains the node information of the cluster to which it belongs, and performs cluster member leaving, joining and cluster head updating operations based on the maintained information. Among them, the cluster head node maintains the information of both nodes within the cluster and nodes outside the cluster.

2. According to the GWO-based mobile ad hoc network clustering strategy in the satellite-ground fusion scenario of claim 1, it is characterized in that: The optimal satellite nodes selected by dual-mode communication nodes include: Dual-mode communication node N i According to its relationship with satellite node S j The uplink load of the satellite node S j The signal strength of the satellite node S j The visibility of the satellite node S j The communication delay calculation of satellite node S j The satellite node with the highest score is selected as the optimal satellite node. Satellite node S j Scores include: SV ij =ω1·(1-U ij )+ω2·RSS ij +ω3·V ij -ω4·D ij Among them, SV ij For dual-mode communication node N i , satellite node S j The score of each factor; ω1, ω2, ω3 and ω4 represent the weight parameters of each factor; U ij Represents dual-mode communication node N i With satellite node S j Uplink load; RSS ij Represents dual-mode communication node N i With satellite node S j The signal strength of V ij Represents dual-mode communication node N i With satellite node S j Visibility; D ij Represents dual-mode communication node N i With satellite node S j communication delay.

3. According to the GWO-based mobile ad hoc network clustering strategy in the satellite-ground fusion scenario of claim 2, it is characterized in that: The dual-mode communication node N i With satellite node S j Visibility V ij include: Among them, θ ij Represents dual-mode communication node N i With satellite node S j The zenith angle is expressed as: in, and Respectively represent the dual-mode communication node N i and satellite node S j The longitude and latitude, θ threshold Indicates setting a threshold.

4. According to the GWO-based mobile ad hoc network clustering strategy in the satellite-ground fusion scenario of claim 1, it is characterized in that: The clustering of nodes in the mobile ad hoc network using the optimized grey wolf optimization algorithm includes: Step 101: Initialize the population size, population dimension and maximum number of iterations, and randomly initialize the position of each individual gray wolf, wherein the position of each individual gray wolf corresponds to a cluster head node selection scheme; Step 102: construct an optimized fitness function. The optimized fitness function F is as follows: F=ψ1·f1+ψ2·f2+ψ3·f3+ψ4·f4-ψ5·f5 Among them, f1 represents the relative speed between the cluster head node and its neighboring nodes; f2 represents the residual energy fraction of the cluster head node; f3 represents the shortest distance between cluster head nodes; f4 and f5 represent the reward factor and penalty factor, which are used to ensure that there are dual-mode communication nodes in each cluster and encourage dual-mode communication nodes to become cluster heads; ψ1, ψ2, ψ3, ψ4 and ψ5 are their weights, and ψ1+ψ2+ψ3+ψ4+ψ5=1; Step 103: Select the nearest cluster head node from other nodes according to the geographical location distance, and calculate the fitness value of each gray wolf based on the constructed fitness function; determine the α wolf, β wolf and δ wolf according to the fitness value of each gray wolf; Step 104: Update the position of the gray wolf according to the positions of the α wolf, the β wolf and the δ wolf; Step 105: Calculate the fitness value of the gray wolf after the position is updated; re-determine the α wolf, β wolf and δ wolf according to the fitness value of the gray wolf after the update; Step 106: If the current round reaches the maximum number of iterations, the iteration ends, the optimal individual is output, and the clustering solution with the final maximum fitness value is obtained; Step 107: If the current round does not reach the maximum number of iterations, go to step 104.

5. According to the GWO-based mobile ad hoc network clustering strategy in the satellite-ground integration scenario of claim 4, it is characterized in that: The updating of the position of the gray wolf according to the positions of the α wolf, the β wolf and the δ wolf comprises: Among them, F1, F2 and F3 are the optimized weight factors, F α 、F β and F δ They represent the fitness of α wolf, β wolf and δ wolf in the grey wolf algorithm respectively; Indicates the location of the individual gray wolf after update.

6. According to the GWO-based mobile ad hoc network clustering strategy in the satellite-ground fusion scenario of claim 4, it is characterized in that: The relative speed between the cluster head node and its neighbor nodes includes: Among them, NUM i represents the cluster head node N i The number of neighbor nodes; represents the moving speed of the cluster head node; V j Cluster head node The moving speed of neighbor nodes.

7. According to claim 4, a mobile ad hoc network clustering strategy based on GWO in a satellite-ground integration scenario is characterized in that: The remaining energy score of the node includes: Among them, E max represents the maximum energy of the cluster head node; Represents the residual energy of the cluster head node.

8. According to the GWO-based mobile ad hoc network clustering strategy in the satellite-ground integration scenario of claim 4, it is characterized in that: The distance between the cluster head nodes includes: in, and Represents the cluster head node CH i' The longitude and latitude coordinates of and Represents the cluster head node CH i The longitude and latitude coordinates of the earth Represents the radius of the Earth.

9. According to claim 4, a mobile ad hoc network clustering strategy based on GWO in a satellite-ground integration scenario is characterized in that: The reward factor and penalty factor include: f4=1+ρ1·τ f5=ρ2(1-τ) Wherein, ρ1 and ρ2 represent a constant greater than 0.

10. The mobile ad hoc network clustering strategy based on GWO in a satellite-ground integration scenario according to claim 1, characterized in that: The cluster member leaving, joining and cluster head updating operations include: After the mobile ad hoc network is clustered, the cluster head CH i The cluster head identifier is inserted into the HELLO message, and the ordinary node N i According to the HELLO information, the location and moving speed of the cluster head are obtained, and the distance between the cluster head and itself is known. and relative speed Departure of cluster members: When there are nodes in the cluster that satisfy When threshold If is the relative speed threshold, the node is removed from the cluster; Cluster member joining: When there is a node outside the cluster that satisfies When , the out-cluster node is added to the cluster; Cluster head update: When the remaining energy of the cluster head node is lower than the threshold, the cluster head broadcasts the cluster head update information, and the nodes in the cluster calculate the score R according to their own remaining energy and the relative movement speed with other nodes in the cluster. i The specific scoring calculation formula is as follows: Among them, R i Represents the nodes N in the cluster i The score of NOC is σ1 and σ2, which represent the weight of each factor. i Represents the number of nodes in the cluster; V i and V j N represents the number of nodes in the cluster i and N j Movement speed; E max is the maximum energy of the node; E i Represents the nodes N in the cluster i The remaining energy After each node in the cluster calculates its own score, it broadcasts a HELLO message with its own score; When each node in the cluster receives a HELLO message with a higher score than its own, the node will no longer broadcast the HELLO message with its own score; When a node broadcasts a HELLO message with a score and does not receive HELLO messages with scores from other nodes within a preset time period, the node becomes the new cluster head.

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