CN114245436A - Unmanned aerial vehicle-assisted mobile edge network clustering method and device - Google Patents
Unmanned aerial vehicle-assisted mobile edge network clustering method and device Download PDFInfo
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Abstract
The embodiment of the invention provides an unmanned aerial vehicle assisted mobile edge network clustering method and device, wherein the method comprises the following steps: step 1: initializing ground nodes into K clusters to obtain K cluster heads; step 2: acquiring a point cluster center of a cluster head polygon according to the K cluster heads, and taking the point cluster center as a coordinate of the unmanned aerial vehicle; and step 3: acquiring coordinates of the unmanned aerial vehicle, reclustering ground nodes according to a preset algorithm to obtain clustering results, and traversing to obtain an optimal cluster head in each cluster; and 4, step 4: acquiring cluster head coordinates corresponding to the optimal cluster head, and combining a greedy algorithm to obtain corresponding unmanned aerial vehicle coordinates; and 5: and (5) repeating the step (3) and the step (4) until the coordinate of the unmanned aerial vehicle reaches the preset precision, and outputting the coordinate of the unmanned aerial vehicle. 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, 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.
Description
Technical Field
The invention relates to the technical field of information and communication engineering, in particular to an unmanned aerial vehicle assisted mobile edge network clustering method and device.
Background
With the development of the internet of things technology, many online mobile applications gradually come into the lives of people. However, these devices have limited computing resources and limited battery resources, and mobile devices cannot rely on their limited resources to meet all computing needs. Data generated by various mobile devices including internet of things devices are massive, the traditional cloud computing transmits all the data to a cloud end, huge pressure is caused on network bandwidth, and meanwhile, the cloud computing is difficult to guarantee the safety of user privacy data. Based on this, a Moving Edge Computing (MEC) technique has been proposed. The MEC technology is a new calculation model which is calculated at the edge of a network and has the characteristics of ultra-low time delay, ultra-high bandwidth, strong real-time performance and the like.
Existing edge computing network architectures typically use fixed base stations as edge servers. In many real-life scenarios, for example after a large natural disaster, a large amount of the communication infrastructure is destroyed; for example, in some field environments or battlefields, the deployment of the small base stations with fixed positions becomes very difficult, and it is also difficult to reasonably determine the clustering strategy during deployment.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides an unmanned aerial vehicle assisted mobile edge network clustering method and device.
The embodiment of the invention provides an unmanned aerial vehicle assisted mobile edge network clustering method, which comprises the following steps:
step 1: acquiring distribution density of ground nodes in a preset range, and initializing the ground nodes into K clusters according to the distribution density to obtain 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 cluster centers of the cluster head polygons, and taking the point cluster centers as coordinates of the unmanned aerial vehicle;
and step 3: acquiring coordinates of the unmanned aerial vehicle, reclustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the cluster to obtain an optimal cluster head in each cluster;
and 4, step 4: acquiring cluster head coordinates corresponding to the optimal cluster head, and combining a preset four-direction variable-step greedy algorithm to obtain corresponding coordinates of the unmanned aerial vehicle according to the cluster head coordinates;
and 5: and repeating the step 3 and the step 4 until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate difference, and outputting the coordinate of the unmanned aerial vehicle when the coordinate difference is smaller than a preset difference.
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 total time delay as the optimal cluster head.
In one embodiment, the method further comprises:
obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining a point cluster center of the cluster head polygon, and obtaining corresponding four-direction unmanned aerial vehicle coordinates by combining a preset four-direction variable step length greedy algorithm;
and respectively calculating time delay between the coordinates of the four-direction unmanned aerial vehicle and each optimal cluster head, and taking the coordinates of the unmanned aerial vehicle corresponding to the minimum total time delay as the coordinates of the unmanned aerial vehicle output by the four-direction variable-step greedy algorithm.
In one embodiment, the method further comprises:
step 4.1, acquiring a node with the highest density in the ground nodes as an initial node according to the distribution density, 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, obtaining the nodes with the highest density in the ground nodes again as second initial nodes, dividing the second initial cluster according to the influence range of the second initial nodes, and taking the second initial nodes as cluster heads;
and 4.3, repeating the step 4.2 until the ground nodes are initialized into K clusters to obtain K cluster heads.
In one embodiment, the method further comprises:
and re-clustering the ground nodes by a K-means clustering algorithm according to the coordinates of the unmanned aerial vehicle to obtain a clustering result.
The embodiment of the invention provides an unmanned aerial vehicle assisted mobile edge network clustering device, which comprises:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the distribution density of ground nodes in a preset range and initializing the ground nodes into K clusters according to the distribution density to obtain K cluster heads;
a second obtaining module, configured to determine an initial position of an initial cluster head according to the K cluster heads, obtain a corresponding cluster head polygon according to the initial position, obtain a point group center of the cluster head polygon, and use the point group center as a coordinate of the unmanned aerial vehicle;
the clustering module is used for acquiring the coordinates of the unmanned aerial vehicle, clustering ground nodes again according to a preset algorithm to obtain a clustering result, 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 is used for acquiring cluster head coordinates corresponding to the optimal cluster head and obtaining corresponding unmanned aerial vehicle coordinates by combining a preset four-direction variable step length greedy algorithm according to the cluster head coordinates;
and the repeating module is used for repeating the steps of the clustering module and the coordinate calculation module until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeating step to obtain a coordinate difference, and when the coordinate difference is smaller than a preset difference, the coordinate of the unmanned aerial vehicle is output.
In one embodiment, the apparatus further comprises:
and the computing module is used for respectively taking each node in the clustering result as a cluster head, computing the total time delay from each node in the cluster to the unmanned aerial vehicle, and taking the node corresponding to the minimum total time delay as the 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 a point cluster center 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 the second calculation module is used for calculating time delay between the coordinates of the four-direction unmanned aerial vehicle and each optimal cluster head respectively, and taking the corresponding coordinates of the unmanned aerial vehicle when the total time delay is minimum as the coordinates of the unmanned aerial vehicle 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 which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the unmanned aerial vehicle assisted mobile edge network clustering method.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the above unmanned aerial vehicle-assisted mobile edge network clustering method.
The embodiment of the invention provides an unmanned aerial vehicle assisted mobile edge network clustering method and device, and the method comprises the following steps: acquiring distribution density of ground nodes in a preset range, and initializing the ground nodes into K clusters according to the distribution density to obtain 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 cluster centers of the cluster head polygons, and taking the point cluster centers as coordinates of the unmanned aerial vehicle; and step 3: acquiring coordinates of the unmanned aerial vehicle, reclustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the cluster to obtain an optimal cluster head in each cluster; and 4, step 4: acquiring cluster head coordinates corresponding to the optimal cluster head, and combining a preset four-direction variable step greedy algorithm to obtain corresponding coordinates of the unmanned aerial vehicle according to the cluster head coordinates; and 5: and (4) repeating the step (3) and the step (4) until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate difference, and outputting the coordinate of the unmanned aerial vehicle when the coordinate difference is smaller than a preset difference. 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 collect information from the ground nodes in all directions, and meanwhile, the total time delay of the system is minimized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for clustering an edge network assisted by an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a structural diagram of an unmanned aerial vehicle-assisted mobile edge network clustering device in the embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow diagram of a method for clustering an edge network assisted by an unmanned aerial vehicle according to an embodiment of the present invention, and as shown in fig. 1, an embodiment of the present invention provides a method for clustering an edge network assisted by an unmanned aerial vehicle, including:
s101, step 1: the method comprises the steps of obtaining distribution density of ground nodes in a preset range, and initializing the ground nodes into K clusters according to the distribution density to obtain K cluster heads.
Specifically, a preset range in which base station nodes need to be deployed is determined, a plurality of ground nodes required by a task are randomly generated in the preset range, distribution density of the ground nodes in the preset range is obtained, the ground nodes are initialized into K clusters according to the distribution density, and K cluster heads are obtained, wherein the specific steps can include:
step 4.1, acquiring nodes with the highest density in the ground nodes as initial nodes according to the distribution density, dividing the initial clusters according to the influence range of the initial nodes, and taking the initial nodes as cluster heads;
step 4.2, removing all nodes in the initial cluster from the ground nodes, obtaining the nodes with the highest density in the ground nodes again as second initial nodes, dividing the second initial cluster according to the influence range of the second initial nodes, and using the second initial nodes as cluster heads;
and 4.3, repeating the step 4.2 until the ground nodes are initialized into K clusters to obtain K cluster heads.
S102, step 2: determining the initial position of an initial cluster head according to the K cluster heads, obtaining a corresponding cluster head polygon according to the initial position, obtaining the point cluster center of the cluster head polygon, and taking the point cluster center as the coordinate of the unmanned aerial vehicle.
Specifically, the initial positions of initial cluster heads are determined according to the K cluster heads, the initial positions are connected to obtain corresponding cluster head polygons, the point cluster centers of the cluster head polygons are obtained, the point cluster centers are the points closest to all vertexes of the cluster head polygons, and the point cluster centers are used as coordinates of the unmanned aerial vehicle.
S103, step 3: and acquiring the coordinates of the unmanned aerial vehicle, reclustering ground nodes according to a preset algorithm to obtain a clustering result, traversing each cluster in the clustering result and each node in the cluster to obtain an optimal cluster head in each cluster.
Specifically, coordinates of the unmanned aerial vehicle are obtained, the ground nodes are clustered again according to a K-means clustering algorithm to obtain clustering results, each cluster in the clustering results and each node in the cluster are facilitated to obtain an optimal cluster head in each cluster, wherein each node in the clustering results is used as a cluster head, total time delay from each node in the cluster to the unmanned aerial vehicle is calculated, the node corresponding to the minimum total time delay is used as the optimal cluster head, in addition, other basic attribute data such as working carrier frequency of the unmanned aerial vehicle and the like need to be obtained when the total time delay is calculated, and further description is omitted.
S104, step 4: and acquiring cluster head coordinates corresponding to the optimal cluster head, and combining a preset four-direction variable step greedy algorithm to obtain corresponding coordinates of the unmanned aerial vehicle according to the cluster head coordinates.
Specifically, a corresponding cluster head polygon is obtained according to the cluster head coordinates, a point cluster center of the cluster head polygon is obtained, 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 respectively calculated, the corresponding unmanned aerial vehicle coordinates when the total time delay is minimum serve as the unmanned aerial vehicle coordinates output by the four-direction variable-step greedy algorithm, in addition, when the total time delay is calculated, other basic attribute data such as the working carrier frequency of the unmanned aerial vehicle and the like need to be obtained, and the description is omitted.
In addition, the specific algorithm in step 3 and step 4 may be as follows, and the algorithm is divided into three steps a, b, and c:
step a, an algorithm of total time delay (including formula 1 to formula 13):
the ground node set is defined as
. Of arbitrary ground nodes nThree-dimensional Cartesian coordinates are expressed as
. Let us assume that the altitude of the drone remains unchanged, then the drone
Can be expressed as
。
Ground node
And unmanned aerial vehicle
The path loss of (d) is expressed as:
wherein the parameters
、
、
、
Is a constant determined by the communication environment.
The carrier frequency for the operation of the unmanned aerial vehicle,
is the speed of propagation of the light and,
representing ground node n and unmanned aerial vehicle
The distance between the two or more of the two or more,
representing ground nodes
With unmanned aerial vehicle
The elevation angle therebetween. In particular, the method comprises the steps of,
the following can be used to solve:
ground node
And unmanned aerial vehicle
The channel gain in between can be defined as:
assume that the ground node has a transmit power of
From any ground node
And unmanned aerial vehicle
The signal-to-noise ratio (SNR) of (d) can be expressed as:
wherein
Representing the power of additive white gaussian noise.
In this embodiment, a ground node is defined
And unmanned aerial vehicle
The transmission rates of (a) and (b) are:
wherein
Representing ground nodes
And unmanned aerial vehicle
The channel bandwidth in between.
From arbitrary ground nodes
To another ground node
The signal-to-noise ratio (SNR) of (d) can be expressed as:
wherein
Representing ground nodes
To another ground node
Obeys an exponential distribution with a mean value of 1.
Representing the path loss index.
Representing the distance of the ground node from the ground node.
Ground node
Node to ground
The transmission rate of can be expressed as
In the formula
Representing the channel bandwidth between ground node n and ground node m.
For a ground node n, the available clusters are denoted by k,
this means that ground nodes can only form K clusters at most. Node point
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 total time delay for terrestrial users. The specific optimization problem is as follows:
assuming that the unmanned aerial vehicle only serves one node in each time slot and the size of the file to be transmitted by the ground node is normalized, the ground node
Time delay when uploading data to the drone
Is shown as
In the formula
Representing cluster head nodes
To unmanned aerial vehicle
The rate of transmission of (a) is,
representing ground nodes
To cluster head node
The transmission rate of (c). Cluster
The total time delay of data transmission of all nodes in the network is
Then, summing the data throughputs of all ground nodes to obtain the formula:
by the formula
In a clear view of the above, it is known that,
。
b, mutually interacting unmanned aerial vehicle position deployment and ground node clustering, and determining coordinates of the unmanned aerial vehicle (comprising a formula 14 to a formula 20) through clustering and a cluster head:
for drones, the position of the drone determines the time delay of the air-ground transmission. The invention first optimizes the position deployment of the unmanned aerial vehicle under the condition of assuming the division of the known clusters
Corresponding toThe optimization problem is as follows:
the coverage of unmanned aerial vehicle is limited in practical application. Under the condition of knowing the position of the unmanned aerial vehicle, 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, thereby reducing the total time delay of the system, namely:
for equation (14), in order to minimize the total system delay, the drone needs to be as close to the cluster head node as possible. Suppose that ground nodes are divided into
Individual clusters, i.e. produced
And (4) clustering the head nodes. Cluster head node coordinates are expressed as
Drone position requires the sum of distances to all cluster heads to be minimal. Then the equation (14) is the problem of finding the center of the K-edge point group.
Find the maximum and minimum of the horizontal and vertical coordinates in all cluster head coordinates, i.e.
、
、
、
. Defining a step size
Is defined as
Wherein
Is the step change factor.
Selecting the average center of the point group as the initial coordinate position of the unmanned aerial vehicle:
because the height of the drone is unchanged, the drone is considered to move in a two-dimensional plane. By taking the coordinates of the unmanned aerial vehicle as the center,
for moving the step-size to a Cartesian coordinate system
Moving in four directions to obtain the position matrix of the unmanned aerial vehicle
，
Where each row represents a drone position coordinate. Is calculated in the matrix
And the sum of the distances of the coordinates of the five unmanned aerial vehicles and all cluster head nodes. If so
Is the step sizeUpdating the coordinates of the unmanned aerial vehicle if the moved coordinate distance and the moved coordinate distance are the shortest, and repeating the steps; if the initial coordinates
Distance and shortest then update step length
C, determining clustering and cluster heads (including a formula 21 to a formula 24) through coordinates of the unmanned aerial vehicle;
according to the formula (9), calculating the ground node
Node to ground
Transmission rate of
. Let ground node
Transmission rate to itself
Obtaining a transmission rate matrix
Ground node
The average transmission rate to the remaining ground nodes is defined as:
in the formula
Representing nodes other than ground
All ground nodes except.
Sorting the average transmission rates of all ground nodes from large to small, selecting the node with the maximum average transmission rate in the sequence as a cluster head node, and deleting the node with the radius of the center as the radius
In which
Representing the coverage radius of the ground node. And repeating the steps on the rest nodes until all the nodes are searched. Finally, the cluster number of the ground node cluster is obtained
And
individual cluster head node coordinates.
For ground node
Available cluster selection strategy
I.e. by
. When in use
Then, the ground node at that time can be obtained according to the formula (11)
Is expressed as a transmission delay of
. Traversing ground nodes
All cluster selection strategies of (1), ground nodes
Selecting the cluster selection strategy with the least time delay, wherein the formula is as follows:
and updating cluster heads in the clusters after clustering is completed. Cluster
All ground nodes in
. Traversing each cluster node, and obtaining the cluster head of each cluster node according to the formula (12)
. Then cluster
The cluster head selection is as follows:
s105, step 5: and repeating the step 3 and the step 4 until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate difference, and outputting the coordinate of the unmanned aerial vehicle when the coordinate difference is smaller than a preset difference.
Specifically, repeating the step 3 and the step 4, and iterating until the coordinate difference between the coordinate of the unmanned aerial vehicle in the iteration process and the previous unmanned aerial vehicle coordinate in the previous repeated step is smaller than a preset difference, which indicates that when the position change of the unmanned aerial vehicle reaches the required precision, the optimal clustering, cluster head and unmanned aerial vehicle coordinate are obtained.
Substituting the ground node clustering result into a formula (14), and updating the unmanned aerial vehicle position deployment; and substituting the updated unmanned aerial vehicle position 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 when the total time delay of the system is minimum and a ground clustering result.
The embodiment of the invention provides an unmanned aerial vehicle assisted mobile edge network clustering method, which comprises the following steps: acquiring distribution density of ground nodes in a preset range, and initializing the ground nodes into K clusters according to the distribution density to obtain 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 cluster centers of the cluster head polygons, and taking the point cluster centers as coordinates of the unmanned aerial vehicle; and step 3: acquiring coordinates of the unmanned aerial vehicle, reclustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the cluster to obtain an optimal cluster head in each cluster; and 4, step 4: acquiring cluster head coordinates corresponding to the optimal cluster head, and combining a preset four-direction variable step greedy algorithm to obtain corresponding coordinates of the unmanned aerial vehicle according to the cluster head coordinates; and 5: and (4) repeating the step (3) and the step (4) until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate difference, and outputting the coordinate of the unmanned aerial vehicle when the coordinate difference is smaller than a preset difference. 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 collect information from the ground nodes in all directions, and meanwhile, the total time delay of the system is minimized. .
Fig. 2 is a diagram of an unmanned aerial vehicle-assisted mobile edge network clustering device provided in an embodiment of the present invention, including: a first obtaining module S201, a second obtaining module S202, a clustering module S203, a coordinate calculating module S204, and a repeating module S205, wherein:
the first obtaining module S201 is configured to obtain distribution density of the ground nodes in a preset range, and initialize the ground nodes into K clusters according to the distribution density to obtain K cluster heads.
A second obtaining module S202, configured to determine an initial position of an initial cluster head according to the K cluster heads, obtain a corresponding cluster head polygon according to the initial position, obtain a point group center of the cluster head polygon, and use the point group center as a coordinate of the unmanned aerial vehicle.
And the clustering module S203 is used for acquiring the coordinates of the unmanned aerial vehicle, clustering ground nodes again according to a preset algorithm to obtain a clustering result, traversing each cluster in the clustering result and each node in the cluster to obtain an optimal cluster head in each cluster.
And the coordinate calculation module S204 is used for acquiring cluster head coordinates corresponding to the optimal cluster head, and obtaining corresponding coordinates of the unmanned aerial vehicle by combining a preset four-direction variable step length greedy algorithm according to the cluster head coordinates.
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 previous unmanned aerial vehicle coordinate in the previous repeating step to obtain a coordinate difference, and when the coordinate difference is smaller than a preset difference, outputting the coordinate of the unmanned aerial vehicle.
In one embodiment, the apparatus may further comprise:
and the computing module is used for respectively taking each node in the clustering result as a cluster head, computing the total time delay from each node in the cluster to the unmanned aerial vehicle, and taking the node corresponding to the minimum total time delay as the optimal cluster head.
In one embodiment, the apparatus may further comprise:
and the algorithm module is used for obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining the point cluster center 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 the second calculation module is used for calculating time delay between the coordinates of the four-direction unmanned aerial vehicle and each optimal cluster head respectively, and taking the corresponding coordinates of the unmanned aerial vehicle when the total time delay is minimum as the coordinates of the unmanned aerial vehicle output by the four-direction variable-step greedy algorithm.
For specific definition of the apparatus for clustering a mobile edge network assisted by a drone, reference may be made to the above definition of the method for clustering a mobile edge network assisted by a drone, which is not described herein again. Each module in the unmanned aerial vehicle assisted mobile edge network clustering device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 3 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 3: 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 complete 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 distribution density of ground nodes in a preset range, and initializing the ground nodes into K clusters according to the distribution density to obtain 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 cluster centers of the cluster head polygons, and taking the point cluster centers as coordinates of the unmanned aerial vehicle; and step 3: acquiring coordinates of the unmanned aerial vehicle, reclustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the cluster to obtain an optimal cluster head in each cluster; and 4, step 4: acquiring cluster head coordinates corresponding to the optimal cluster head, and combining a preset four-direction variable step greedy algorithm to obtain corresponding coordinates of the unmanned aerial vehicle according to the cluster head coordinates; and 5: and (4) repeating the step (3) and the step (4) until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate difference, and outputting the coordinate of the unmanned aerial vehicle when the coordinate difference is smaller than a preset difference.
Furthermore, the logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute 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), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: step 1: acquiring distribution density of ground nodes in a preset range, and initializing the ground nodes into K clusters according to the distribution density to obtain 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 cluster centers of the cluster head polygons, and taking the point cluster centers as coordinates of the unmanned aerial vehicle; and step 3: acquiring coordinates of the unmanned aerial vehicle, reclustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the cluster to obtain an optimal cluster head in each cluster; and 4, step 4: acquiring cluster head coordinates corresponding to the optimal cluster head, and combining a preset four-direction variable step greedy algorithm to obtain corresponding coordinates of the unmanned aerial vehicle according to the cluster head coordinates; and 5: and (4) repeating the step (3) and the step (4) until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate difference, and outputting the coordinate of the unmanned aerial vehicle when the coordinate difference is smaller than a preset difference.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An unmanned aerial vehicle-assisted mobile edge network clustering method is characterized by comprising the following steps:
step 1: acquiring distribution density of ground nodes in a preset range, and initializing the ground nodes into K clusters according to the distribution density to obtain 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 cluster centers of the cluster head polygons, and taking the point cluster centers as coordinates of the unmanned aerial vehicle;
and step 3: acquiring coordinates of the unmanned aerial vehicle, reclustering ground nodes according to a preset algorithm to obtain clustering results, traversing each cluster in the clustering results and each node in the cluster to obtain an optimal cluster head in each cluster;
and 4, step 4: acquiring cluster head coordinates corresponding to the optimal cluster head, and combining a preset four-direction variable-step greedy algorithm to obtain corresponding coordinates of the unmanned aerial vehicle according to the cluster head coordinates;
and 5: and repeating the step 3 and the step 4 until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeated step to obtain a coordinate difference, and outputting the coordinate of the unmanned aerial vehicle when the coordinate difference is smaller than a preset difference.
2. The unmanned-aerial-vehicle-assisted mobile edge network clustering method according to claim 1, wherein traversing each cluster and each node in the cluster in the clustering result to obtain an optimal cluster head in each cluster 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 total time delay as the optimal cluster head.
3. The unmanned aerial vehicle-assisted mobile edge network clustering method according to claim 1, wherein the obtaining of corresponding coordinates of the unmanned aerial vehicle by combining a preset four-direction variable step greedy algorithm according to cluster head coordinates comprises:
obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining a point cluster center of the cluster head polygon, and obtaining corresponding four-direction unmanned aerial vehicle coordinates by combining a preset four-direction variable step length greedy algorithm;
and respectively calculating time delay between the coordinates of the four-direction unmanned aerial vehicle and each optimal cluster head, and taking the coordinates of the unmanned aerial vehicle corresponding to the minimum total time delay as the coordinates of the unmanned aerial vehicle output by the four-direction variable-step greedy algorithm.
4. The unmanned-aerial-vehicle-assisted mobile edge network clustering method according to claim 1, wherein initializing ground nodes into K clusters according to the distribution density to obtain K cluster heads comprises:
step 4.1, acquiring a node with the highest density in the ground nodes as an initial node according to the distribution density, 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, obtaining the nodes with the highest density in the ground nodes again as second initial nodes, dividing the second initial cluster according to the influence range of the second initial nodes, and taking the second initial nodes as cluster heads;
and 4.3, repeating the step 4.2 until the ground nodes are initialized into K clusters to obtain K cluster heads.
5. The unmanned aerial vehicle-assisted mobile edge network clustering method according to claim 1, wherein the obtaining of coordinates of the unmanned aerial vehicle and the re-clustering of the ground nodes according to a preset algorithm to obtain a clustering result comprises:
and re-clustering the ground nodes by a K-means clustering algorithm according to the coordinates of the unmanned aerial vehicle to obtain a clustering result.
6. An unmanned aerial vehicle assists and removes edge network clustering device which characterized in that, the device includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the distribution density of ground nodes in a preset range and initializing the ground nodes into K clusters according to the distribution density to obtain K cluster heads;
a second obtaining module, configured to determine an initial position of an initial cluster head according to the K cluster heads, obtain a corresponding cluster head polygon according to the initial position, obtain a point group center of the cluster head polygon, and use the point group center as a coordinate of the unmanned aerial vehicle;
the clustering module is used for acquiring the coordinates of the unmanned aerial vehicle, clustering ground nodes again according to a preset algorithm to obtain a clustering result, 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 is used for acquiring cluster head coordinates corresponding to the optimal cluster head and obtaining corresponding unmanned aerial vehicle coordinates by combining a preset four-direction variable step length greedy algorithm according to the cluster head coordinates;
and the repeating module is used for repeating the steps of the clustering module and the coordinate calculation module until the coordinate of the unmanned aerial vehicle is compared with the previous unmanned aerial vehicle coordinate in the previous repeating step to obtain a coordinate difference, and when the coordinate difference is smaller than a preset difference, the coordinate of the unmanned aerial vehicle is output.
7. The drone assisted mobile edge network clustering device of claim 6, wherein the device further comprises:
and the computing module is used for respectively taking each node in the clustering result as a cluster head, computing the total time delay from each node in the cluster to the unmanned aerial vehicle, and taking the node corresponding to the minimum total time delay as the optimal cluster head.
8. The drone assisted mobile edge network clustering device of claim 6, wherein the device further comprises:
the algorithm module is used for obtaining a corresponding cluster head polygon according to the cluster head coordinates, obtaining a point cluster center 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 the second calculation module is used for calculating time delay between the coordinates of the four-direction unmanned aerial vehicle and each optimal cluster head respectively, and taking the corresponding coordinates of the unmanned aerial vehicle when the total time delay is minimum as the coordinates of the unmanned aerial vehicle output by the four-direction variable-step greedy algorithm.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the drone assisted mobile edge network clustering method according to any one of claims 1 to 5.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the drone assisted mobile edge network clustering method according to any one of claims 1 to 5.
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