CN114245436B - Unmanned aerial vehicle auxiliary mobile edge network clustering method and device - Google Patents
Unmanned aerial vehicle auxiliary mobile edge network clustering method and device Download PDFInfo
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Abstract
The embodiment of the invention provides a clustering method and device for an unmanned aerial vehicle auxiliary mobile edge network, wherein the method comprises the following steps: step 1: initializing ground nodes into K clusters to obtain K cluster heads; step 2: acquiring the center of a point group of a cluster head polygon according to the K cluster heads, and taking the center of the point group as the coordinates of the unmanned aerial vehicle; step 3: the method comprises the steps of obtaining coordinates of an unmanned aerial vehicle, re-clustering ground nodes according to a preset algorithm to obtain clustering results, and traversing to obtain optimal cluster heads in each cluster; step 4: acquiring a cluster head coordinate corresponding to the optimal cluster head, and combining a greedy algorithm to acquire a corresponding unmanned plane coordinate; step 5: and (3) repeating the step (3) and the step (4) until the unmanned aerial vehicle coordinates reach the preset precision, and outputting the unmanned aerial vehicle coordinates. By adopting the method, the coverage area of the unmanned aerial vehicle can be effectively enlarged by optimizing the position deployment of the unmanned aerial vehicle and the clustering strategy of the ground nodes, so that the unmanned aerial vehicle can collect information from the ground nodes in all directions, and meanwhile, the total time delay of the system is minimized.
Description
Technical Field
The invention relates to the technical field of information and communication engineering, in particular to a clustering method and device for an unmanned aerial vehicle auxiliary mobile edge network.
Background
With the development of internet of things, many online mobile applications gradually enter people's life. However, the computing resources and battery resources of these devices are limited, and mobile devices cannot rely on their limited resources to meet all computing demands. The data generated by various mobile devices including the Internet of things device are massive, the traditional cloud computing can cause huge pressure on network bandwidth by transmitting all the data to the cloud, and meanwhile, the security of private data of a user is difficult to ensure by the cloud computing. Based on this, a Mobile Edge Computing (MEC) technique has been proposed. The MEC technology is a new calculation model for calculating at the network edge, and has the characteristics of ultra-low time delay, ultra-high bandwidth, strong real-time performance and the like.
In the existing edge computing network architecture, a fixed base station is generally used as an edge server. In many real life scenarios, such as after a large natural disaster has occurred, a large amount of communication infrastructure is destroyed; for example, in some field environments or battlefields, deployment of small fixed-location base stations becomes very difficult, and it is also difficult to reasonably determine the clustering strategy at deployment.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides an unmanned aerial vehicle auxiliary mobile edge network clustering method and device.
The embodiment of the invention provides an unmanned aerial vehicle auxiliary mobile edge network clustering method, which comprises the following steps:
step 1: acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads;
step 2: determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, obtaining point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle;
step 3: the method comprises the steps of obtaining coordinates of an unmanned aerial vehicle, re-clustering ground nodes according to a preset algorithm to obtain clustering results, and traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster;
step 4: acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and combining a preset four-direction variable step greedy algorithm;
step 5: and (3) repeating the steps (3) and (4) until the unmanned aerial vehicle coordinate is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate gap, and outputting the unmanned aerial vehicle coordinate when the coordinate gap is smaller than a preset gap.
In one embodiment, the method further comprises:
and respectively taking each node in the clustering result as a cluster head, calculating the total time delay from each node in the cluster to the unmanned aerial vehicle, and taking the node corresponding to the minimum time delay as an optimal cluster head.
In one embodiment, the method further comprises:
obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining the center of a point group of the cluster head polygon, and obtaining corresponding four-direction unmanned aerial vehicle coordinates by combining a preset four-direction variable step greedy algorithm;
and respectively calculating the time delay between the four-direction unmanned aerial vehicle coordinates and each optimal cluster head, and taking the unmanned aerial vehicle coordinate corresponding to the time delay with the minimum total time delay as the unmanned aerial vehicle coordinate output by the four-direction variable step greedy algorithm.
In one embodiment, the method further comprises:
step 4.1, according to the distribution density, acquiring a node with the highest density in the ground nodes as an initial node, and dividing an initial cluster according to the influence range of the initial node, wherein the initial node is used as a cluster head;
step 4.2, removing all nodes in the initial cluster from the ground nodes, and acquiring the node with the highest density in the ground nodes again as a second initial node, wherein the second initial cluster is divided according to the influence range of the second initial node, and the second initial node is used as a cluster head;
and 4.3, repeating the step 4.2 until the ground node is initialized to K clusters to obtain K cluster heads.
In one embodiment, the method further comprises:
and re-clustering the ground nodes through a K-means clustering algorithm according to the coordinates of the unmanned aerial vehicle to obtain clustering results.
The embodiment of the invention provides an unmanned aerial vehicle auxiliary mobile edge network clustering device, which comprises the following steps:
the first acquisition module is used for acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads;
the second acquisition module is used for determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, acquiring point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle;
the clustering module is used for acquiring the coordinates of the unmanned aerial vehicle, re-clustering the ground nodes according to a preset algorithm to obtain clustering results, and traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster;
the coordinate calculation module is used for acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and combining a preset four-direction variable step greedy algorithm;
and the repeating module is used for repeating the steps of the clustering module and the coordinate calculating module until the coordinate of the unmanned aerial vehicle is compared with the coordinate of the previous unmanned aerial vehicle in the previous repeating step to obtain a coordinate gap, and outputting the coordinate of the unmanned aerial vehicle when the coordinate gap is smaller than a preset gap.
In one embodiment, the apparatus further comprises:
the computing module is used for computing the total time delay from each node in the cluster to the unmanned aerial vehicle by taking each node in the clustering result as a cluster head, and taking the node corresponding to the time delay with the minimum total time delay as an optimal cluster head.
In one embodiment, the apparatus further comprises:
the algorithm module is used for obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining the center of a point group of the cluster head polygon, and obtaining a corresponding four-direction unmanned aerial vehicle coordinate by combining a preset four-direction variable step greedy algorithm;
and the second calculation module is used for calculating the time delay between the four-direction unmanned aerial vehicle coordinates and each optimal cluster head respectively, and taking the unmanned aerial vehicle coordinate corresponding to the time when the total time delay is minimum as the unmanned aerial vehicle coordinate output by the four-direction variable step greedy algorithm.
The embodiment of the invention provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the unmanned aerial vehicle auxiliary mobile edge network clustering method when executing the program.
An embodiment of the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the unmanned aerial vehicle assisted mobile edge network clustering method described above.
The embodiment of the invention provides an unmanned aerial vehicle auxiliary mobile edge network clustering method and device, wherein the method comprises the following steps of: acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads; step 2: determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, obtaining point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle; step 3: the method comprises the steps of obtaining coordinates of an unmanned aerial vehicle, re-clustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster; step 4: acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and by combining a preset four-direction variable step greedy algorithm; step 5: and (3) repeating the steps (3) and (4) until the unmanned aerial vehicle coordinate is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate gap, and outputting the unmanned aerial vehicle coordinate when the coordinate gap is smaller than the preset gap. Therefore, the coverage area of the unmanned aerial vehicle can be effectively enlarged by optimizing the position deployment of the unmanned aerial vehicle and the clustering strategy of the ground nodes, the unmanned aerial vehicle can be ensured to collect information from the ground nodes in all directions, and meanwhile, the total time delay of the system is minimized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for clustering an unmanned aerial vehicle-assisted mobile edge network in an embodiment of the invention;
fig. 2 is a block diagram of an unmanned aerial vehicle auxiliary mobile edge network clustering device in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a schematic flow chart of an unmanned aerial vehicle auxiliary mobile edge network clustering method provided by an embodiment of the present invention, and as shown in fig. 1, the embodiment of the present invention provides an unmanned aerial vehicle auxiliary mobile edge network clustering method, including:
s101, step 1: and acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads.
Specifically, determining a preset range in which base station nodes need to be deployed, randomly generating a plurality of ground nodes required by tasks in the preset range, acquiring distribution density of the ground nodes in the preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads, wherein the specific steps can include:
step 4.1, according to the distribution density, acquiring the node with the highest density in the ground nodes as an initial node, dividing the initial cluster according to the influence range of the initial node, and taking the initial node as a cluster head;
step 4.2, removing all nodes in the initial cluster from the ground nodes, and acquiring the node with the highest density in the ground nodes again as a second initial node, and dividing the second initial cluster according to the influence range of the second initial node, wherein the second initial node is used as a cluster head;
and 4.3, repeating the step 4.2 until the ground node is initialized to K clusters to obtain K cluster heads.
S102, step 2: and determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, obtaining point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle.
Specifically, determining initial positions of initial cluster heads according to K cluster heads, connecting the initial positions to obtain corresponding cluster head polygons, and obtaining point group centers of the cluster head polygons, wherein the point group centers are points closest to each vertex of the cluster head polygons, and the point group centers are used as coordinates of the unmanned aerial vehicle.
S103, step 3: and acquiring coordinates of the unmanned aerial vehicle, re-clustering the ground nodes according to a preset algorithm to obtain clustering results, and traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster.
Specifically, coordinates of the unmanned aerial vehicle are obtained, clustering is conducted on ground nodes according to a K-means clustering algorithm to obtain clustering results, each cluster in the clustering results and each node in the clusters are facilitated to obtain optimal cluster heads in each cluster, each node in the clustering results is used as a cluster head, total time delay from each node in the clusters to the unmanned aerial vehicle is calculated, a node corresponding to the minimum total time delay is used as the optimal cluster head, in addition, in calculating the total time delay, other basic attribute data such as working carrier frequency of the unmanned aerial vehicle and the like are needed to be obtained, and details are omitted.
S104, step 4: and acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and combining a preset four-direction variable step greedy algorithm.
Specifically, a corresponding cluster head polygon is obtained according to the cluster head coordinates, the center of a point group of the cluster head polygon is obtained, the corresponding four-direction unmanned aerial vehicle coordinates are obtained by combining a preset four-direction variable step greedy algorithm, time delays between the four-direction unmanned aerial vehicle coordinates and each optimal cluster head are calculated respectively, the unmanned aerial vehicle coordinates corresponding to the time when the total time delay is minimum are used as unmanned aerial vehicle coordinates output by the four-direction variable step greedy algorithm, in addition, other basic attribute data such as the frequency of an unmanned aerial vehicle working carrier and the like are needed to be obtained when the total time delay is calculated, and the description is omitted.
In addition, the specific algorithm in the above step 3 and step 4 may be as follows, and the algorithm is divided into three steps a, b, and c:
step a, algorithm of total delay (including formula 1 to formula 13):
defining a set of ground nodes as. The three-dimensional Cartesian coordinates of any ground node n are denoted +.>. Let us assume that the altitude of the unmanned aerial vehicle remains unchanged, then unmanned aerial vehicle +.>Can be expressed as +.>。
Ground nodeAnd unmanned plane->The path loss of (2) is expressed as:
wherein the parameters are、/>、/>、/>Is a constant determined by the communication environment. />For the frequency of the unmanned aerial vehicle working carrier, +.>For the propagation speed of light, +.>Represents ground node n and unmanned plane +.>Distance between->Representing ground node->And unmanned plane->Elevation angle between. Specifically, is (L)>The following equation can be used to solve:
the following equation can be used to solve:
ground nodeAnd unmanned plane->The channel gain between can be defined as:
assume that the transmit power of the ground node isFrom any ground node->And unmanned plane->The signal-to-noise ratio (SNR) of (a) can be expressed as:
wherein the method comprises the steps ofRepresenting the power of the additive white gaussian noise.
In this embodiment, ground nodes are definedAnd unmanned plane->The transmission rate of (2) is:
wherein the method comprises the steps ofRepresenting ground node->And unmanned plane->Channel bandwidth therebetween.
From any ground nodeTo another ground node->The signal-to-noise ratio (SNR) of (a) can be expressed as:
wherein the method comprises the steps ofRepresenting ground node->To another oneA ground node->Is subject to an exponential distribution with an average value of 1.Representing the path loss index. />Representing the ground node to ground node distance.
Ground nodeTo ground node->The transmission rate of (2) can be expressed as
In the middle ofRepresenting the channel bandwidth between ground node n and ground node m.
For ground node n, the available cluster is denoted by k,this means that the ground node can only form K clusters at maximum. Node->The cluster selection of (2) can be expressed as +.>. For any cluster k there is a cluster head +.>. The optimization problem of the present invention is defined to minimize the overall delay for the ground user. The specific optimization problem is as follows:
assuming that the unmanned aerial vehicle only serves one node in each time slot, normalizing the file size to be transmitted by the ground node, and then the ground nodeTime delay of uploading data of (2) to unmanned aerial vehicle>Represented as
In the middle ofRepresenting cluster head node +.>To unmanned plane->Transmission rate of>Representing ground node->To a cluster head nodeIs used for the transmission rate of (a). Cluster->The total time delay of the data transmission of all nodes in the network is
The data throughput of all ground nodes is then summed to yield the formula:
from the formulaIt can be seen that->。
Step b, the interactive unmanned aerial vehicle position deployment and ground node clustering are carried out, and unmanned aerial vehicle coordinates (comprising a formula 14 to a formula 20) are determined through clustering and cluster heads:
for unmanned aerial vehicle, unmanned aerial vehicle's position has decided the time delay of space-to-ground transmission. The method comprises the steps of optimizing the position deployment of the unmanned aerial vehicle under the condition of assuming the division of the known clustersThe corresponding optimization problem is as follows:
the unmanned aerial vehicle has limited coverage in practical application. Under the condition that the position of the unmanned aerial vehicle is known, the ground nodes form stable clusters according to the position of the unmanned aerial vehicle so as to improve the transmission rate of the unmanned aerial vehicle, and therefore the total time delay of the system is reduced, namely:
for equation (14), the drone needs to be as close to the cluster head node as possible in order to minimize the overall latency of the system. Assuming that the ground nodes are divided intoClusters, i.e. generate->And a cluster head node. Cluster head node coordinates are expressed as
The drone location requires a distance and minimum to all cluster heads. Then equation (14) is to find the bit center problem in the K-sided dot group.
Searching the maximum value and the minimum value of the horizontal coordinates and the vertical coordinates in all cluster head coordinates, namely、/>、/>、/>. Defining step sizeIs defined as
Wherein the method comprises the steps ofIs a step change factor.
Selecting the average center of the point group as the initial coordinate position of the unmanned plane:
because the drone is not changing in height, the drone is considered to be moving in a two-dimensional plane. With the coordinates of the unmanned aerial vehicle as the center,for moving step length the drone is directed to +.>Moving in four directions to obtain a position matrix of the unmanned aerial vehicle +.>，
Where each row represents a position coordinate of the drone. Calculated in matrixThe sum of the distances between the coordinates of the five unmanned aerial vehicles and all cluster head nodes. If you are->Updating the coordinates of the unmanned aerial vehicle for the coordinate distance after the step length movement and the shortest, and repeating the steps; if the initial coordinates +.>Update step size +.>
Step c, determining clusters and cluster heads (comprising formulas 21 to 24) through unmanned plane coordinates;
according to formula (9), calculate ground nodeTo ground node->Transmission rate of->. Let ground node->Transmission rate to itself->Obtaining a transmission rate matrix->
Ground nodeThe average transmission rate to the remaining ground nodes is defined as:
in the middle ofRepresenting except ground node->All but the ground nodes.
The average transmission rate of all the ground nodes is ordered from big to small, the node with the largest average transmission rate in the sequence is selected as the cluster head node, and the node with the largest average transmission rate in the sequence is deleted as the central radiusWherein>Representing the radius of coverage of the surface node. And repeating the steps on the rest nodes until all the nodes are searched. Finally, the cluster number of the ground node clusters is obtainedAnd->And (5) cluster head node coordinates.
For ground nodesIs available->I.e. +.>. When->At this time, the ground node +.>Is expressed as +.>. Traversing the processCalendar ground node->Is a ground node +.>The cluster selection strategy with the least time delay is selected, and the formula is as follows:
and updating the cluster heads in the clusters after the clustering is completed. ClusterAll ground nodes in (a) satisfy->. Traversing each intra-cluster node, and obtaining +_for each intra-cluster node as a cluster head according to a formula (12)>. Then cluster->The cluster head selection of (1) is:
s105, step 5: and (3) repeating the steps (3) and (4) until the unmanned aerial vehicle coordinate is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate gap, and outputting the unmanned aerial vehicle coordinate when the coordinate gap is smaller than a preset gap.
Specifically, step 3 and step 4 are repeated, iteration is performed until the coordinate difference between the unmanned aerial vehicle coordinates and the previous unmanned aerial vehicle coordinates in the previous repeated step in the iteration process is smaller than the preset difference, and when the position change amount of the unmanned aerial vehicle reaches the required precision, the optimal cluster, cluster head and unmanned aerial vehicle coordinates are obtained.
Substituting the clustering result of the ground nodes into a formula (14) to update the position deployment of the unmanned aerial vehicle; substituting the updated position of the unmanned aerial vehicle into a formula (15) to update the ground clustering. And iterating until the position of the unmanned aerial vehicle is unchanged, and obtaining the coordinate of the unmanned aerial vehicle and the ground clustering result when the total time delay of the system is minimum.
The embodiment of the invention provides an unmanned aerial vehicle auxiliary mobile edge network clustering method, which comprises the following steps of: acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads; step 2: determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, obtaining point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle; step 3: the method comprises the steps of obtaining coordinates of an unmanned aerial vehicle, re-clustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster; step 4: acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and by combining a preset four-direction variable step greedy algorithm; step 5: and (3) repeating the steps (3) and (4) until the unmanned aerial vehicle coordinate is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate gap, and outputting the unmanned aerial vehicle coordinate when the coordinate gap is smaller than the preset gap. Therefore, the coverage area of the unmanned aerial vehicle can be effectively enlarged by optimizing the position deployment of the unmanned aerial vehicle and the clustering strategy of the ground nodes, the unmanned aerial vehicle can be ensured to collect information from the ground nodes in all directions, and meanwhile, the total time delay of the system is minimized. .
Fig. 2 is a schematic diagram of an apparatus for clustering an unmanned aerial vehicle assisted mobile edge network according to an embodiment of the present invention, including: a first acquisition module S201, a second acquisition module S202, a clustering module S203, a coordinate calculation module S204, and a repetition module S205, wherein:
the first obtaining module S201 is configured to obtain a distribution density of the ground nodes in a preset range, and initialize the ground nodes to K clusters according to the distribution density, so as to obtain K cluster heads.
And the second acquisition module S202 is used for determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, acquiring point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle.
And the clustering module S203 is used for acquiring the coordinates of the unmanned aerial vehicle, re-clustering the ground nodes according to a preset algorithm to obtain a clustering result, and traversing each cluster in the clustering result and each node in the cluster to obtain an optimal cluster head in each cluster.
The coordinate calculation module S204 is configured to obtain a cluster head coordinate corresponding to the optimal cluster head, and obtain a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and by combining a preset four-direction variable step greedy algorithm.
And the repeating module S205 is used for repeating the steps of the clustering module and the coordinate calculating module until the coordinate of the unmanned aerial vehicle is compared with the coordinate of the previous unmanned aerial vehicle in the previous repeating step to obtain a coordinate gap, and outputting the coordinate of the unmanned aerial vehicle when the coordinate gap is smaller than a preset gap.
In one embodiment, the apparatus may further include:
the computing module is used for computing the total time delay from each node in the cluster to the unmanned aerial vehicle by taking each node in the clustering result as a cluster head, and taking the node corresponding to the time delay with the minimum total time delay as an optimal cluster head.
In one embodiment, the apparatus may further include:
the algorithm module is used for obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining the center of a point group of the cluster head polygon, and obtaining the corresponding four-direction unmanned aerial vehicle coordinates by combining a preset four-direction variable step greedy algorithm.
And the second calculation module is used for calculating the time delay between the four-direction unmanned aerial vehicle coordinates and each optimal cluster head respectively, and taking the unmanned aerial vehicle coordinate corresponding to the time when the total time delay is minimum as the unmanned aerial vehicle coordinate output by the four-direction variable step greedy algorithm.
For specific limitations on the unmanned aerial vehicle assisted mobile edge network clustering apparatus, reference may be made to the above limitation on the unmanned aerial vehicle assisted mobile edge network clustering method, and no further description is given here. The above-mentioned various modules in the unmanned aerial vehicle-assisted mobile edge network clustering device may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: a processor (processor) 301, a memory (memory) 302, a communication interface (Communications Interface) 303 and a communication bus 304, wherein the processor 301, the memory 302 and the communication interface 303 perform communication with each other through the communication bus 304. The processor 301 may call logic instructions in the memory 302 to perform the following method: step 1: acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads; step 2: determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, obtaining point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle; step 3: the method comprises the steps of obtaining coordinates of an unmanned aerial vehicle, re-clustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster; step 4: acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and by combining a preset four-direction variable step greedy algorithm; step 5: and (3) repeating the steps (3) and (4) until the unmanned aerial vehicle coordinate is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate gap, and outputting the unmanned aerial vehicle coordinate when the coordinate gap is smaller than the preset gap.
Further, the logic instructions in memory 302 described above may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present invention further provide a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform the transmission method provided in the above embodiments, for example, including: step 1: acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads; step 2: determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, obtaining point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle; step 3: the method comprises the steps of obtaining coordinates of an unmanned aerial vehicle, re-clustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster; step 4: acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and by combining a preset four-direction variable step greedy algorithm; step 5: and (3) repeating the steps (3) and (4) until the unmanned aerial vehicle coordinate is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate gap, and outputting the unmanned aerial vehicle coordinate when the coordinate gap is smaller than the preset gap.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. An unmanned aerial vehicle assisted mobile edge network clustering method is characterized by comprising the following steps:
step 1: acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads;
step 2: determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, obtaining point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle;
step 3: the method comprises the steps of obtaining coordinates of an unmanned aerial vehicle, re-clustering ground nodes according to a preset algorithm to obtain clustering results, and traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster;
step 4: acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and combining a preset four-direction variable step greedy algorithm;
step 5: repeating the steps 3 and 4 until the unmanned aerial vehicle coordinates are compared with the previous unmanned aerial vehicle coordinates in the previous repeated step to obtain coordinate differences, and outputting the unmanned aerial vehicle coordinates when the coordinate differences are smaller than preset differences;
traversing each cluster in the clustering result and each node in the cluster to obtain an optimal cluster head in each cluster, wherein the method comprises the following steps:
each node in the clustering result is used as a cluster head, the total time delay from each node in the cluster to the unmanned aerial vehicle is calculated, and the node corresponding to the time delay with the minimum total time delay is used as an optimal cluster head;
the method for obtaining the corresponding unmanned aerial vehicle coordinates by combining a preset four-direction variable step greedy algorithm according to the cluster head coordinates comprises the following steps:
obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining the center of a point group of the cluster head polygon, and obtaining corresponding four-direction unmanned aerial vehicle coordinates by combining a preset four-direction variable step greedy algorithm;
respectively calculating time delay between the four-direction unmanned aerial vehicle coordinates and each optimal cluster head, and taking the unmanned aerial vehicle coordinate corresponding to the time when the total time delay is minimum as the unmanned aerial vehicle coordinate output by the four-direction variable step greedy algorithm;
initializing the ground node into K clusters according to the distribution density to obtain K cluster heads, wherein the method comprises the following steps:
step 4.1, according to the distribution density, acquiring a node with the highest density in the ground nodes as an initial node, and dividing an initial cluster according to the influence range of the initial node, wherein the initial node is used as a cluster head;
step 4.2, removing all nodes in the initial cluster from the ground nodes, and acquiring the node with the highest density in the ground nodes again as a second initial node, wherein the second initial cluster is divided according to the influence range of the second initial node, and the second initial node is used as a cluster head;
step 4.3, repeating the step 4.2 until the ground node is initialized to K clusters to obtain K cluster heads;
the obtaining of the coordinates of the unmanned aerial vehicle, the re-clustering of the ground nodes according to a preset algorithm, and the clustering result is obtained, including:
and re-clustering the ground nodes through a K-means clustering algorithm according to the coordinates of the unmanned aerial vehicle to obtain clustering results.
2. An unmanned aerial vehicle assisted mobile edge network clustering device, the device comprising:
the first acquisition module is used for acquiring the distribution density of the ground nodes in a preset range, initializing the ground nodes into K clusters according to the distribution density, and obtaining K cluster heads;
the second acquisition module is used for determining initial positions of initial cluster heads according to the K cluster heads, obtaining corresponding cluster head polygons according to the initial positions, acquiring point group centers of the cluster head polygons, and taking the point group centers as coordinates of the unmanned aerial vehicle;
the clustering module is used for acquiring the coordinates of the unmanned aerial vehicle, re-clustering the ground nodes according to a preset algorithm to obtain clustering results, and traversing each cluster in the clustering results and each node in the clusters to obtain an optimal cluster head in each cluster;
the coordinate calculation module is used for acquiring a cluster head coordinate corresponding to the optimal cluster head, and acquiring a corresponding unmanned aerial vehicle coordinate according to the cluster head coordinate and combining a preset four-direction variable step greedy algorithm;
the repeating module is used for repeating the steps of the clustering module and the coordinate calculating module until the unmanned aerial vehicle coordinate is compared with the previous unmanned aerial vehicle coordinate in the previous repeating step to obtain a coordinate difference, and outputting the unmanned aerial vehicle coordinate when the coordinate difference is smaller than a preset difference;
the computing module is used for computing the total time delay from each node in the cluster to the unmanned aerial vehicle by taking each node in the clustering result as a cluster head, and taking the node corresponding to the time delay with the minimum total time delay as an optimal cluster head;
the algorithm module is used for obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining the center of a point group of the cluster head polygon, and obtaining a corresponding four-direction unmanned aerial vehicle coordinate by combining a preset four-direction variable step greedy algorithm;
the second calculation module is used for calculating time delay between the four-direction unmanned aerial vehicle coordinates and each optimal cluster head respectively, and taking the unmanned aerial vehicle coordinate corresponding to the time when the total time delay is minimum as the unmanned aerial vehicle coordinate output by the four-direction variable step greedy algorithm;
initializing the ground node into K clusters according to the distribution density to obtain K cluster heads, wherein the method comprises the following steps:
step 4.1, according to the distribution density, acquiring a node with the highest density in the ground nodes as an initial node, and dividing an initial cluster according to the influence range of the initial node, wherein the initial node is used as a cluster head;
step 4.2, removing all nodes in the initial cluster from the ground nodes, and acquiring the node with the highest density in the ground nodes again as a second initial node, wherein the second initial cluster is divided according to the influence range of the second initial node, and the second initial node is used as a cluster head;
step 4.3, repeating the step 4.2 until the ground node is initialized to K clusters to obtain K cluster heads;
the obtaining of the coordinates of the unmanned aerial vehicle, the re-clustering of the ground nodes according to a preset algorithm, and the clustering result is obtained, including:
and re-clustering the ground nodes through a K-means clustering algorithm according to the coordinates of the unmanned aerial vehicle to obtain clustering results.
3. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the unmanned aerial vehicle assisted mobile edge network clustering method of claim 1.
4. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the unmanned aerial vehicle assisted mobile edge network clustering method of claim 1.
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