CN110913402A - High-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation - Google Patents

High-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation Download PDF

Info

Publication number
CN110913402A
CN110913402A CN201911178041.9A CN201911178041A CN110913402A CN 110913402 A CN110913402 A CN 110913402A CN 201911178041 A CN201911178041 A CN 201911178041A CN 110913402 A CN110913402 A CN 110913402A
Authority
CN
China
Prior art keywords
unmanned aerial
aerial vehicle
coverage
efficiency
cluster
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201911178041.9A
Other languages
Chinese (zh)
Inventor
吴启晖
游文静
董超
程潇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN201911178041.9A priority Critical patent/CN110913402A/en
Publication of CN110913402A publication Critical patent/CN110913402A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/08—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
    • H04W84/04—Large scale networks; Deep hierarchical networks
    • H04W84/06—Airborne or Satellite Networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention provides an unmanned aerial vehicle ad hoc network clustering method with high coverage efficiency for jointly optimizing communication and formation, which comprises the following steps: obtaining a packet error rate of single-hop transmission according to a free space path loss model of communication between unmanned aerial vehicles, and providing an end-to-end multi-hop delay model based on the packet error rate; defining coverage area efficiency and coverage width efficiency, and providing a coverage efficiency model to evaluate the coverage performance of the unmanned aerial vehicle group; converting the unmanned aerial vehicle coverage efficiency problem under the time delay constraint into a minimized objective function problem with a time delay penalty term by a penalty function method; according to the provided iterative optimization algorithm based on the block coordinate descent method, the optimal cluster head set of the unmanned aerial vehicle cluster, the optimal position and the optimal power of each unmanned aerial vehicle are obtained, so that the maximum coverage of the unmanned aerial vehicle cluster to the area is realized under the constraint condition that the communication time delay requirement and the power requirement among the unmanned aerial vehicles are met.

Description

High-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation
Technical Field
The invention belongs to the field of unmanned aerial vehicle ad hoc network system architecture, and particularly relates to a high-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation.
Background
Because unmanned aerial vehicle has advantages such as small, mobility is strong, low in cost, deployment convenience, unmanned aerial vehicle is more and more extensive in military use and civilian field's application, for example, reconnaissance, accurate agriculture, disaster management and environmental monitoring etc.. In the development process of the unmanned aerial vehicle technology, people gradually realize that the task completion reliability of a single unmanned aerial vehicle is not high enough, and the complex work task is difficult to complete due to the limitation of factors such as energy, capacity and load of the single unmanned aerial vehicle, so that the cooperative operation of multiple unmanned aerial vehicles becomes a trend. With the development of technologies such as electronics and communication, unmanned aerial vehicles tend to be miniaturized, and large-scale unmanned aerial vehicle clusters represented by swarms are receiving wide attention from the industry and academia.
In the face of complex tasks and dynamic uncertain environments, communication interruption, operation failure and other emergency situations sometimes occur in unmanned aerial vehicle clusters, and therefore, in the face of various possible future environments and application requirements, the unmanned aerial vehicle clusters are required to be capable of intelligently judging working environments, behaviors of the unmanned aerial vehicles are automatically adjusted and controlled to guarantee completion of working tasks, and therefore the unmanned aerial vehicles become important development directions of large-scale unmanned aerial vehicle clusters. In order to realize autonomous control, an unmanned aerial vehicle ad hoc network capable of providing efficient and flexible inter-machine communication becomes a key, however, a series of challenges are brought to resource allocation, channel access, network routing and the like of the unmanned aerial vehicle ad hoc network in a large scale, and clustering of an unmanned aerial vehicle cluster can meet the challenges brought by large-scale unmanned aerial vehicles.
Among the numerous applications of drones, area coverage is one of the most common and important applications, such as reconnaissance of a certain area, data acquisition, or providing network services to ground users. In order to meet mission requirements, the drone network needs to perceive the area as efficiently as possible. In drone ad hoc networks, autonomous clustering and data transfer require that drones remain close enough to maintain network connectivity, which may severely reduce the coverage efficiency of the drone swarm. And in order to reduce coverage overlap, dispersing the drones will reduce the communication quality between drones. Most of the known methods for clustering unmanned aerial vehicles do not consider the coverage efficiency and communication problems of the unmanned aerial vehicle cluster. Therefore, it is very important to study maximization of area coverage performance while securing communication requirements.
Disclosure of Invention
The invention aims to provide a high-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation aiming at the defects or problems in the prior art.
The technical scheme of the invention is as follows: a high coverage efficiency unmanned aerial vehicle ad hoc network clustering method jointly optimizing communication and formation considers that N unmanned aerial vehicles reconnaissance a certain area, and the network structure of the unmanned aerial vehicle ad hoc network is G (N, Q, P, H, M), wherein N is a node set, Q is a node position vector set, P is a power set, H is a cluster head set, and M is a cluster member set, and the coverage efficiency of the unmanned aerial vehicle cluster is maximized under the time delay and power constraint through the joint optimization of cluster heads, positions and power of the unmanned aerial vehicle cluster; the high-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation comprises the following steps:
step 1, obtaining a packet error rate of single-hop transmission according to a free space path loss model of communication between unmanned aerial vehicles, and providing an end-to-end multi-hop delay model based on the packet error rate;
step 2, defining coverage area efficiency and coverage width efficiency, and providing a coverage efficiency model to evaluate the coverage performance of the unmanned aerial vehicle group;
step 3, converting the unmanned aerial vehicle coverage efficiency problem under the time delay constraint into a minimized objective function problem with a time delay penalty term by a penalty function method;
step 4, clustering based on a clustering algorithm optimization structure of a greedy algorithm to obtain a better cluster head set;
step 5, obtaining a local optimal position according to the relative position of the optimal structure of the position optimization algorithm based on the steepest descent algorithm;
step 6, obtaining more optimal power according to the transmission power of the power optimization algorithm optimization structure based on the steepest descent algorithm;
step 7, based on the optimization algorithms in the steps 4, 5 and 6, carrying out iterative optimization on the optimization algorithms by adopting a structural optimization algorithm of a block coordinate descent algorithm idea, so that the coverage efficiency of the unmanned aerial vehicle group to the area is maximized under the constraint condition of meeting the communication time delay requirement and the power requirement among the unmanned aerial vehicles;
in step 1, a packet error rate of single-hop transmission is obtained according to a free space path loss model of communication between unmanned aerial vehicles, and an end-to-end multi-hop delay model, a time delay model sigma, is provided based on the packet error rate of single-hop transmissionijIs defined as:
Figure BDA0002290487910000021
wherein
Figure BDA0002290487910000022
The average single-hop time delay from the K-1 hop to the K hop in the shortest path from the unmanned aerial vehicle i to the unmanned aerial vehicle j is represented, K represents the total shortest path hop number from the unmanned aerial vehicle i to the unmanned aerial vehicle j, tau represents the round-trip time of single-hop communication, and an、gnIs a parameter that is independent of the transmission mode,
Figure BDA0002290487910000023
representing the signal to interference plus noise ratio of the channel. Considering the UAV communication link as a line-of-sight link, the information power gain h transmitted from node i to node jijIs defined as
Figure BDA0002290487910000024
Where ρ is0Is the unit distance channel gain, qiIs the location vector of node i, qjIs the position vector of node j, dijIs the distance between node i and node j. The signal-to-interference-and-noise ratio of the channel is expressed as
Figure BDA0002290487910000031
Where No represents noise, by2Is obtained as2As ambient noise, IijFor interference, hijFor the power gain of information transmitted from node I to node j, IiFor a set of interfering nodes, Pi、PzRepresenting the transmission power of node i and node z. Assuming that the MAC protocol used in the network is TDMA, each node transmits information independently and randomly at each time slot with a probability r. Thus, interference IijIs desired to be
Figure BDA0002290487910000032
The SINR at this expected interference may be approximated as
Figure BDA0002290487910000033
Based on the channel SINR formula, the single-hop packet error rate of the node i transmitting information to the node j can be expressed as
Figure BDA0002290487910000034
Average single-hop delay from node i to node j is
Figure BDA0002290487910000035
Therefore, the time delay sigma corresponding to the shortest path from the node i to the node j can be obtainedijIf there is no communication path from node i to node j, then σ is definedij＝∞。
Defining coverage area efficiency and coverage width efficiency in the step 2, and providing a coverage efficiency model to evaluate the coverage performance of the unmanned aerial vehicle group;
the coverage area efficiency is defined as:
Figure BDA0002290487910000036
wherein N represents the total number of drones,r represents the coverage radius of each unmanned aerial vehicle, S (G) represents the coverage area of the unmanned aerial vehicle cluster under the structure G, and S (G) is defined as
Figure BDA0002290487910000037
Wherein s isijRepresents the overlapping coverage area of UAV i and UAV j, and is divided into sijIs defined as
Figure BDA0002290487910000038
dijRepresenting the distance between drone i and drone j.
The coverage width efficiency is defined as:
Figure BDA0002290487910000039
wherein We(G) The coverage width of the unmanned aerial vehicle perpendicular to the speed direction under the structure G is represented, and W is usede(G) Is defined as
Figure BDA00022904879100000310
Wherein u isκ(i)Is the value of the i-th unmanned aerial vehicle position vector projection to the velocity vertical direction, k is the sequence in the node set and satisfies
Figure BDA00022904879100000311
Based on the coverage area efficiency and the coverage width efficiency, the coverage area efficiency can be defined as:
Figure BDA0002290487910000041
wherein α ∈ [0,1 ]]α can be selected according to the specific application.
In step 3, considering the time delay constraint between the leader node and the cluster head and the time delay constraint between the cluster members, modeling the problem of maximized coverage efficiency as follows:
Figure BDA0002290487910000042
s.t.σij≤σM,σji≤σM,
Figure BDA0002290487910000043
σ1i≤σH,σi1≤σH,
Figure BDA0002290487910000044
σ1j＜∞,σj1＜∞,
Figure BDA0002290487910000045
0≤Pi≤Pmax,
Figure BDA0002290487910000046
wherein σHUpper bound value, sigma, for the time delay of leader node and cluster head nodeMIs the upper limit constraint value, P, of the time delay of the cluster member node and the cluster head nodemaxIs an upper bound for transmission power. Adopting a penalty function method to convert the unmanned aerial vehicle coverage efficiency problem under the delay constraint into a minimized objective function problem with a delay penalty term, wherein the problem can be modeled as a minimized model as follows:
Figure BDA0002290487910000047
s.t.Pi≤Pmax
wherein λ is1,λ2,λ3> 0 is the corresponding penalty term coefficient, Δ1,Δ2,Δ3Is a penalty term, δ, corresponding to the delay constraintijAnd deltaiIs a delay constraint penalty function, which is defined as follows:
Figure BDA0002290487910000048
Figure BDA0002290487910000049
if the time delay sigmaijSatisfy corresponding timeThe delay upper bound is then δ ij0, otherwise δij>0 has a penalty on latency. If the node is not communicated with the leader node, the time delay is infinite, and the distance between the node i and the leader node is used as a penalty item.
In step 4, according to clustering of the clustering algorithm optimization structure based on the greedy algorithm, the current unmanned aerial vehicle position Q is givenlTransmission power PlAnd cluster head HlWhere l refers to the number of iterations, the goal is optimized by adding or deleting cluster heads, i.e., to find a cluster head set H satisfying J (Q)l,Pl,H)＜J(Ql,Pl,Hl) This cluster head set H is the found better cluster head set, and the details are as follows:
4.1, deleting cluster heads: from HlFinding an optimal cluster head i and changing the optimal cluster head i into a cluster member to reduce the target function J most, if the optimal cluster head i cannot be found to reduce the target function J, keeping the optimal cluster head i unchanged;
4.2, adding cluster heads: finding a non-cluster-head node J to reduce the target function J to the maximum after the non-cluster-head node J is changed into a cluster head, and if the non-cluster-head node J cannot be found, keeping the target function J unchanged;
in step 5, according to the relative position of the position optimization algorithm optimization structure based on the steepest descent algorithm, a local optimal position is obtained, and the gradient of the objective function J on Q is firstly calculated
Figure BDA0002290487910000051
Wherein l refers to the number of iterations, then the position Q is updated by using the steepest descent algorithm,
Figure BDA0002290487910000052
wherein the content of the first and second substances,
Figure BDA0002290487910000053
in step 6, optimizing the transmission power of the structure according to the power optimization algorithm based on the steepest descent algorithm to obtain better power, determining the reduction or increase of the node power according to the gradient of J to P, and firstly calculating the gradient
Figure BDA0002290487910000054
Wherein l is iteration number, updating power P by using a steepest descent method,
Figure BDA0002290487910000055
wherein λ>0 and
Figure BDA0002290487910000056
to satisfy the constraint Pi≤Pmax,
Figure BDA0002290487910000057
Can order Pl+1←min{Pmax,Pl+1}；
In step 7, the structural optimization algorithm is proposed according to the idea based on the block coordinate descent algorithm, iterative optimization is carried out on the algorithms in step 4, step 5 and step 6, the optimal cluster head set of the unmanned aerial vehicle cluster, the optimal position and the optimal power of each unmanned aerial vehicle are obtained, and the unmanned aerial vehicle cluster can cover the area to the maximum extent under the constraint condition that the communication time delay requirement and the power requirement among the unmanned aerial vehicles are met; in order to optimize the structure G ═ of the unmanned aerial vehicle ad hoc network (N, Q, P, H, M), the selection, relative position and transmission power of the cluster heads will be optimized simultaneously, and the cluster member set is determined by Q, P, H; in the structure optimization algorithm proposed based on the block coordinate descent algorithm idea, the current structure G is given at each stepl＝(Ql,Pl,Hl) Wherein l refers to the number of times of the first iteration, and the optimized structure G is obtained by the following stepsl+1The method comprises the following steps:
5.1 optimizing the Structure (Q) by clustering Algorithml,Pl,Hl) To obtain a better cluster head set Hl+1；
5.2 optimizing the Structure (Q) by means of a position optimization Algorithml,Pl,Hl+1) To obtain a locally preferred position Ql+1；
5.3 optimizing the Structure (Q) by means of a Power optimization Algorithml+1,Pl,Hl+1) To a more optimal power Pl+1；
5.4, passing aboveStep(s) to obtain a more optimal structure Gl+1＝(Ql+1,Pl+1,Hl+1) Repeating the above step pair Gl+1Optimizing until | | JL+1-JLThe value of | < epsilon, wherein epsilon is used for judging whether the algorithm is converged or not, and the value is e-6。
The technical scheme provided by the invention has the following beneficial effects:
the high-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method based on joint optimization communication and formation takes the maximized coverage efficiency as a target under the constraint of considering communication delay and power, and converts the maximized coverage efficiency problem under the constraint of delay into a minimized target function problem with a delay punishment item by adopting a punishment function method. In order to meet the communication time delay requirement and the power requirement between unmanned aerial vehicles and simultaneously maximize the coverage efficiency, an iterative optimization algorithm based on a block coordinate descent method is developed to simultaneously optimize the selection, the relative position and the transmission power of a cluster head.
Drawings
Fig. 1 is a schematic diagram of an unmanned aerial vehicle ad hoc network reconnaissance system according to the present invention;
FIG. 2 is a schematic diagram of the coverage width of the unmanned aerial vehicle cluster according to the present invention;
FIG. 3(a) is a schematic diagram of an initial networking architecture of the unmanned aerial vehicle cluster according to the present invention;
FIG. 3(b) is a schematic diagram of the networking architecture after the optimization of the unmanned aerial vehicle cluster of the present invention;
FIG. 4 is a graph of experimental simulation results of the relationship between coverage efficiency and the number of unmanned aerial vehicles under different coverage radii;
FIG. 5 is a diagram of experimental simulation results of the relationship between coverage efficiency and the number of UAVs according to different time delay constraints;
FIG. 6 is a diagram of a simulation result of a relationship between the number of nodes and the coverage efficiency under different powers according to the present invention;
FIG. 7(a) is a graph of experimental simulation results of relationship between the number of nodes and the number of cluster heads under different time delay constraints;
fig. 7(b) is a graph of the experimental simulation result of the relationship between the number of nodes and the number of cluster heads under different powers.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the high-coverage unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation, provided by the invention, N unmanned aerial vehicles are used for reconnaissance of a certain area, and the network structure of the unmanned aerial vehicle ad hoc network is recorded as G (N, Q, P, H, M), wherein N is a node set, Q is a node position vector set, P is a power set, H is a cluster head set, and M is a cluster member set.
In the problem of large-scale unmanned aerial vehicle ad hoc network clustering applied to reconnaissance coverage, the optimization problem of communication performance and coverage performance exists, and the following challenges specifically exist: 1) in an unmanned aerial vehicle ad hoc network, autonomous cluster and data transmission require that an unmanned aerial vehicle maintain a close enough distance to maintain network connection, which may seriously reduce the coverage efficiency of an unmanned aerial vehicle cluster; 2) in order to reduce coverage overlap, dispersing the drones reduces the communication quality between the drones; 3) the larger transmission power of the unmanned aerial vehicle consumes more energy and causes a communication interference problem to other nodes, and if the transmission power is too small, the signal strength is small, so that the transmission power needs to be optimized on the premise of not exceeding the maximum power limit.
Specifically, the high-coverage-rate unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation comprises the following steps:
in step 1, a packet error rate of single-hop transmission is obtained according to a free space path loss model of communication between unmanned aerial vehicles, and an end-to-end multi-hop delay model, a time delay model sigma, is provided based on the packet error rate of single-hop transmissionijIs defined as:
Figure BDA0002290487910000071
wherein
Figure BDA0002290487910000072
The average single-hop time delay from the k-1 hop to the k hop in the shortest path from the unmanned aerial vehicle i to the unmanned aerial vehicle j is represented, k represents the total shortest path hop number from the unmanned aerial vehicle i to the unmanned aerial vehicle j, tau represents the round-trip time of single-hop communication, and an、gnIs a parameter that is independent of the transmission mode,
Figure BDA0002290487910000073
representing the signal to interference plus noise ratio of the channel. Considering the UAV communication link as a line-of-sight link, the information power gain h transmitted from node i to node jijIs defined as
Figure BDA0002290487910000074
Where ρ is0Is the unit distance channel gain, qiIs the location vector of node i, qjIs the position vector of node j, dijIs the distance between node i and node j. The signal-to-interference-and-noise ratio of the channel is expressed as
Figure BDA0002290487910000075
Where No represents noise, by2Is obtained as2As ambient noise, IijFor interference, hijFor the power gain of information transmitted from node I to node j, IiFor a set of interfering nodes, Pi、PzRepresenting the transmission power of node i and node z. Assuming that the MAC protocol used in the network is TDMA, each node transmits information independently and randomly at each time slot with a probability r. Thus, interference IijIs desired to be
Figure BDA0002290487910000076
The SINR at this expected interference may be approximated as
Figure BDA0002290487910000077
Single-hop error of node i transmitting information to node j based on channel signal-to-interference-and-noise ratio formulaThe packet rate can be expressed as
Figure BDA0002290487910000081
Average single-hop delay from node i to node j is
Figure BDA0002290487910000082
Therefore, the time delay sigma corresponding to the shortest path from the node i to the node j can be obtainedijIf there is no communication path from node i to node j, then σ is definedij＝∞。
Defining coverage area efficiency and coverage width efficiency in the step 2, and providing a coverage efficiency model to evaluate the coverage performance of the unmanned aerial vehicle group;
the coverage area efficiency is defined as:
Figure BDA0002290487910000083
wherein N represents the total number of the unmanned aerial vehicles, R represents the coverage radius of each unmanned aerial vehicle, S (G) represents the coverage area of the unmanned aerial vehicle cluster under the structure G, and S (G) is defined as
Figure BDA0002290487910000084
Wherein s isijRepresents the overlapping coverage area of UAV i and UAV j, and is divided into sijIs defined as
Figure BDA0002290487910000085
dijRepresenting the distance between drone i and drone j.
The coverage width efficiency is defined as:
Figure BDA0002290487910000086
wherein We(G) The coverage width of the unmanned aerial vehicle perpendicular to the speed direction under the structure G is represented, and W is usede(G) Is defined as
Figure BDA0002290487910000087
Wherein u isκ(i)Is the value of the i-th unmanned aerial vehicle position vector projection to the velocity vertical direction, k is the sequence in the node set and satisfies
Figure BDA0002290487910000088
Based on the coverage area efficiency and the coverage width efficiency, the coverage area efficiency can be defined as:
Figure BDA0002290487910000089
wherein α ∈ [0,1 ]]α can be selected according to the specific application.
In step 3, considering the time delay constraint between the leader node and the cluster head and the time delay constraint between the cluster members, modeling the problem of maximized coverage efficiency as follows:
Figure BDA0002290487910000091
s.t.σij≤σM,σji≤σM,
Figure BDA0002290487910000092
σ1i≤σH,σi1≤σH,
Figure BDA0002290487910000093
σ1j＜∞,σj1＜∞,
Figure BDA0002290487910000094
0≤Pi≤Pmax,
Figure BDA0002290487910000095
wherein σHUpper bound value, sigma, for the time delay of leader node and cluster head nodeMIs the upper limit constraint value, P, of the time delay of the cluster member node and the cluster head nodemaxIs an upper bound for transmission power. Adopting a penalty function method to convert the unmanned aerial vehicle coverage efficiency problem under the delay constraint into a minimized objective function problem with a delay penalty term, wherein the problem can be modeled as a minimized model as follows:
Figure BDA0002290487910000096
s.t.Pi≤Pmax
wherein λ is1,λ2,λ3> 0 is the corresponding penalty term coefficient, Δ1,Δ2,Δ3Is a penalty term, δ, corresponding to the delay constraintijAnd deltaiIs a delay constraint penalty function, which is defined as follows:
Figure BDA0002290487910000097
Figure BDA0002290487910000098
if the time delay sigmaijSatisfying the corresponding upper time delay constraint is δ ij0, otherwise δij>0 has a penalty on latency. If the node is not communicated with the leader node, the time delay is infinite, and the distance between the node i and the leader node is used as a penalty item.
In step 4, according to clustering of the clustering algorithm optimization structure based on the greedy algorithm, the current unmanned aerial vehicle position Q is givenlTransmission power PlAnd cluster head HlWhere l refers to the number of iterations, the goal is optimized by adding or deleting cluster heads, i.e., to find a cluster head set H satisfying J (Q)l,Pl,H)＜J(Ql,Pl,Hl) This cluster head set H is the found better cluster head set, and the details are as follows:
4.1, deleting cluster heads: from HlFinding an optimal cluster head i and changing the optimal cluster head i into a cluster member to reduce the target function J most, if the optimal cluster head i cannot be found to reduce the target function J, keeping the optimal cluster head i unchanged;
4.2, adding cluster heads: finding a non-cluster-head node J to reduce the target function J to the maximum after the non-cluster-head node J is changed into a cluster head, and if the non-cluster-head node J cannot be found, keeping the target function J unchanged;
optimizing the junction according to a steepest descent algorithm-based position optimization algorithm in step 5Obtaining the relative position of the structure, obtaining the local optimal position, firstly calculating the gradient of the target function J on Q
Figure BDA0002290487910000101
Wherein l refers to the number of iterations, then the position Q is updated by using the steepest descent algorithm,
Figure BDA0002290487910000102
wherein the content of the first and second substances,
Figure BDA0002290487910000103
in step 6, optimizing the transmission power of the structure according to the power optimization algorithm based on the steepest descent algorithm to obtain better power, determining the reduction or increase of the node power according to the gradient of J to P, and firstly calculating the gradient
Figure BDA0002290487910000104
Wherein l is iteration number, updating power P by using a steepest descent method,
Figure BDA0002290487910000105
wherein λ>0 and
Figure BDA0002290487910000106
to satisfy the constraint Pi≤Pmax,
Figure BDA0002290487910000107
Can order Pl+1←min{Pmax,Pl+1}；
In step 7, the structural optimization algorithm is proposed according to the idea based on the block coordinate descent algorithm, iterative optimization is carried out on the algorithms in step 4, step 5 and step 6, the optimal cluster head set of the unmanned aerial vehicle cluster, the optimal position and the optimal power of each unmanned aerial vehicle are obtained, and the unmanned aerial vehicle cluster can cover the area to the maximum extent under the constraint condition that the communication time delay requirement and the power requirement among the unmanned aerial vehicles are met; in order to optimize the structure G ═ (N, Q, P, H, M) of the drone ad hoc network, the selection of cluster heads, relative positions and transmission powers, cluster members, will be optimized simultaneouslyThe set is determined by Q, P, H; in the structure optimization algorithm proposed based on the block coordinate descent algorithm idea, the current structure G is given at each stepl＝(Ql,Pl,Hl) Wherein l refers to the number of times of the first iteration, and the optimized structure G is obtained by the following stepsl+1The method comprises the following steps:
5.1 optimizing the Structure (Q) by clustering Algorithml,Pl,Hl) To obtain a better cluster head set Hl+1；
5.2 optimizing the Structure (Q) by means of a position optimization Algorithml,Pl,Hl+1) To obtain a locally preferred position Ql+1；
5.3 optimizing the Structure (Q) by means of a Power optimization Algorithml+1,Pl,Hl+1) To a more optimal power Pl+1；
5.4 obtaining a better structure G by the above stepsl+1＝(Ql+1,Pl+1,Hl+1) Repeating the above step pair Gl+1Optimizing until | | JL+1-JLThe value of | < epsilon, wherein epsilon is used for judging whether the algorithm is converged or not, and the value is e-6。
The invention discloses an unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation and having high coverage efficiency, and figure 1 shows a scene of unmanned aerial vehicle ad hoc network clustering for maximizing coverage efficiency under time delay constraint.
Fig. 3(a) (b) are the structure diagrams of the unmanned aerial vehicles before and after optimization, respectively, considering that 15 unmanned aerial vehicles reconnaissance the area. In fig. 3(b), a node a is a leader node, a square node is a cluster head node, the remaining circle nodes are cluster member nodes, an arrow on the node a indicates a moving direction of the unmanned aerial vehicle cluster, a dotted circle indicates a coverage area, a dotted line between the nodes indicates a link between two adjacent nodes, numbers on the node a and the node E indicate a time delay from the leader node to the cluster head, other numbers in the figure indicate a time delay from the cluster head to the cluster member, and a diagonal dotted line indicates a coverage width. As can be seen from fig. 3(a), initially most nodes are isolated nodes, and the calculated coverage area efficiency and coverage width efficiency are 0.76 and 0.34, respectively. After optimization, as shown in fig. 3(b), 3 clusters are obtained, the cluster heads are respectively a node a, a node K and a node E, and the coverage area efficiency and the coverage width efficiency are respectively 0.84 and 0.37. It can be seen that due to communication delay constraint, nodes are covered by a certain overlap, and after the scheme designed by the invention is adopted to optimize the unmanned aerial vehicle ad hoc network structure, the coverage performance is improved.
Fig. 4 is a graph of a relationship between the number of nodes and the coverage performance under different coverage radii, where the coverage radii are sequentially increased by 20, 50, 80, and 110, and the coverage efficiencies when the number of nodes under different radii is 10, 20, 30, 40, and 50 are respectively recorded, and it can be seen that the coverage efficiency of the system decreases with the increase of the coverage radius of the nodes, and also decreases with the increase of the number of nodes. This is because the delay constraints limit the distance and number of communication hops between drones, while increasing the coverage radius results in more overlapping coverage.
FIG. 5 is a graph of the relationship between the number of nodes and the coverage efficiency under different delay constraints, where σ is (σ)H，σM) To represent a delay constraint, where σHUpper bound value, sigma, for the time delay of leader node and cluster head nodeMFour kinds of delay constraints are considered for the upper limit constraint values of the time delay of the cluster member node and the cluster head node, wherein σ is (2,3), σ is (4,6), σ is (6,9) and σ is (8,12), the coverage efficiency when the number of nodes is 10, 20, 30, 40 and 50 under different time delay combinations is recorded, and it can be seen that the coverage efficiency is increased along with the increase of the time delay threshold value, because the larger the time delay threshold value is, the larger the inter-unmanned aerial vehicle distance can meet the time delay constraint.
Fig. 6 is a graph of a relationship between the number of nodes and coverage efficiency under different powers, and considering static power and optimized power, in a scenario of setting static power, the power of the nodes is independently and randomly generated by normal distribution, and the standard deviation is 10, and it is expected that the cases of 30 mW, 60 mW, and 90mW are considered respectively. For an optimized power scene, the node power is randomly and independently taken from 20-100 mW in a uniformly distributed manner, and it can be seen that in some cases, the coverage efficiency is increased along with the increase of the power, because the larger the power is, the larger the distance between the nodes can be under the same time delay constraint, and after the power of the nodes is optimized by adopting the scheme designed by the invention, the nodes can have better coverage efficiency under the lower power.
Fig. 7(a) is a relationship diagram of the number of nodes and the number of cluster heads under four different delay constraints, and it can be seen that the smaller the delay constraint threshold, the greater the number of cluster heads, and more clustering results, because the larger the cluster size, the greater the delay between cluster heads and cluster members will also increase, and the delay constraint threshold limits the cluster size. Fig. 7(b) shows that, when the delay constraint is σ ═ 2,3, the influence of power optimization on the number of cluster heads is considered, and it can be seen that the optimized power reduces the number of cluster heads, because the connection and interference between nodes can be better considered when the scheme designed by the present invention is used to optimize the node power, under the same delay constraint, higher connectivity and less interference between nodes can be achieved, so that more nodes can be accommodated in the cluster, and the number of clusters is reduced.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.

Claims (8)

1. An unmanned aerial vehicle ad hoc network clustering method with high coverage efficiency for jointly optimizing communication and formation is characterized in that: considering that N unmanned aerial vehicles reconnaissance a certain area, and recording the network structure of the unmanned aerial vehicle ad hoc network as G ═ N, Q, P, H and M, wherein N is a node set, Q is a node position vector set, P is a power set, H is a cluster head set, and M is a cluster member set;
the high-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation comprises the following steps:
step 1, obtaining a packet error rate of single-hop transmission according to a free space path loss model of communication between unmanned aerial vehicles, and providing an end-to-end multi-hop delay model based on the packet error rate;
step 2, defining coverage area efficiency and coverage width efficiency, and providing a coverage efficiency model to evaluate the coverage performance of the unmanned aerial vehicle group;
step 3, converting the unmanned aerial vehicle coverage efficiency problem under the time delay constraint into a minimized objective function problem with a time delay penalty term by a penalty function method;
step 4, clustering based on a clustering algorithm optimization structure of a greedy algorithm to obtain a better cluster head set;
step 5, obtaining a local optimal position according to the relative position of the optimal structure of the position optimization algorithm based on the steepest descent algorithm;
step 6, obtaining more optimal power according to the transmission power of the power optimization algorithm optimization structure based on the steepest descent algorithm;
and 7, based on the optimization algorithms in the steps 4, 5 and 6, carrying out iterative optimization on the optimization algorithms by adopting a structural optimization algorithm of a block coordinate descent algorithm idea, so that the coverage efficiency of the unmanned aerial vehicle group to the area is maximized under the constraint condition of meeting the communication time delay requirement and the power requirement among the unmanned aerial vehicles.
2. The high coverage efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation according to claim 1, wherein in step 1, a packet error rate of single-hop transmission is obtained according to a free space path loss model of communication between unmanned aerial vehicles, and an end-to-end multi-hop delay model, a time delay model sigma, is provided based on the packet error rate of single-hop transmissionijIs defined as:
Figure FDA0002290487900000011
wherein
Figure FDA0002290487900000012
The average single-hop time delay from the K-1 hop to the K hop in the shortest path from the unmanned aerial vehicle i to the unmanned aerial vehicle j is represented, K represents the total shortest path hop number from the unmanned aerial vehicle i to the unmanned aerial vehicle j, tau represents the round-trip time of single-hop communication, and an、gnIs a parameter that is independent of the transmission mode,
Figure FDA0002290487900000013
representing the signal to interference plus noise ratio of the channel.
3. The unmanned aerial vehicle ad hoc network clustering method for high coverage efficiency combined optimization of communication and formation according to claim 1, wherein coverage area efficiency and coverage width efficiency are defined in step 2, and a coverage efficiency model is proposed to evaluate the coverage performance of the unmanned aerial vehicle cluster;
the coverage area efficiency is defined as:
Figure FDA0002290487900000021
wherein N represents the total number of the unmanned aerial vehicles, R represents the coverage radius of each unmanned aerial vehicle, S (G) represents the coverage area of the unmanned aerial vehicle cluster under the structure G, and S (G) is defined as
Figure FDA0002290487900000022
Wherein s isijRepresenting the overlapping coverage area of drone i and drone j;
the coverage width efficiency is defined as:
Figure FDA0002290487900000023
wherein We(G) Representing the coverage width of the unmanned aerial vehicle under the structure G in the direction vertical to the speed direction;
based on the coverage area efficiency and the coverage width efficiency, the coverage area efficiency is defined as:
Figure FDA0002290487900000024
wherein α ∈ [0,1 ]]α, the values are chosen according to the specific application requirements.
4. The method for clustering unmanned aerial vehicle ad hoc networks with high coverage efficiency by jointly optimizing communication and formation according to claim 1, wherein the unmanned aerial vehicle coverage efficiency problem under the delay constraint is converted into a minimization objective function problem with a delay penalty term by a penalty function method in step 3, and the problem is modeled as a minimization model as follows:
Figure FDA0002290487900000025
s.t.Pi≤Pmax
wherein λ is1,λ2,λ3> 0 is the corresponding penalty term coefficient, Δ1,Δ2,Δ3Is a penalty term, δ, corresponding to the delay constraintijAnd deltaiIs a delay constraint penalty function, which is defined as follows:
Figure FDA0002290487900000026
Figure FDA0002290487900000027
5. the unmanned aerial vehicle ad hoc network clustering method capable of jointly optimizing communication and formation and having high coverage efficiency according to claim 1, wherein in step 4, a current unmanned aerial vehicle position Q is given according to clustering of a clustering algorithm optimization structure based on a greedy algorithmlTransmission power PlAnd cluster head HlWhere l refers to the number of iterations, the goal is optimized by adding or deleting cluster heads, i.e.Find satisfying J (Q)l,Pl,H)＜J(Ql,Pl,Hl) The cluster head set H is a more optimal cluster head set, and specifically, the following is performed:
4.1, deleting cluster heads: from HlFinding an optimal cluster head i and changing the optimal cluster head i into a cluster member to reduce the target function J most, if the optimal cluster head i cannot be found to reduce the target function J, keeping the optimal cluster head i unchanged;
4.2, adding cluster heads: finding a non-cluster head node J makes the objective function J reduce most after it becomes a cluster head, and if not, it remains unchanged.
6. The unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation and having high coverage efficiency according to claim 1, wherein in step 5, a local optimal position is obtained according to a relative position of an optimization structure of a position optimization algorithm based on a steepest descent algorithm, and a gradient of an objective function J on Q is first calculated
Figure FDA0002290487900000031
Wherein l refers to the number of iterations, then the position Q is updated by using the steepest descent algorithm,
Figure FDA0002290487900000032
wherein the content of the first and second substances,
Figure FDA0002290487900000033
7. the method according to claim 1, wherein the optimal power is obtained in step 6 by optimizing the transmission power of the structure according to a power optimization algorithm based on a steepest descent algorithm, the decrease or increase of the node power is determined according to the gradient of J to P, and the gradient is first calculated
Figure FDA0002290487900000037
The power P is updated using the steepest descent method,
Figure FDA0002290487900000034
wherein λ>0 and
Figure FDA0002290487900000035
to satisfy the constraint
Figure FDA0002290487900000036
Can order Pl+1←min{Pmax,Pl+1}。
8. The unmanned aerial vehicle ad hoc network clustering method with high coverage efficiency for jointly optimizing communication and formation according to claim 1, wherein in step 7, the algorithms in step 4, step 5 and step 6 are iteratively optimized according to a structural optimization algorithm proposed based on the idea of a block coordinate descent algorithm to obtain an optimal cluster head set of an unmanned aerial vehicle cluster, an optimal position and optimal power of each unmanned aerial vehicle, so that under the constraint condition of meeting the communication delay requirement and power requirement among the unmanned aerial vehicles, the maximum coverage of the unmanned aerial vehicle cluster on an area is realized; in order to optimize the structure G ═ of the unmanned aerial vehicle ad hoc network (N, Q, P, H, M), the selection, relative position and transmission power of the cluster heads will be optimized simultaneously, and the cluster member set is determined by Q, P, H; in the structure optimization algorithm proposed based on the block coordinate descent algorithm idea, the current structure G is given at each stepl＝(Ql,Pl,Hl) Wherein l is the number of iterations, the optimized structure G is obtained by the following stepsl+1The method comprises the following steps:
5.1 optimizing the Structure (Q) by clustering Algorithml,Pl,Hl) To obtain a better cluster head set Hl+1；
5.2 optimizing the Structure (Q) by means of a position optimization Algorithml,Pl,Hl+1) To obtain a locally preferred position Ql+1；
5.3 optimizing the Structure (Q) by means of a Power optimization Algorithml+1,Pl,Hl+1) To a more optimal power Pl+1；
5.4 obtaining a better structure G by the above stepsl+1＝(Ql+1,Pl+1,Hl+1) Repeating the above step pair Gl+1Optimizing until | | JL+1-JLAnd | | is less than epsilon, wherein epsilon is used as a threshold for judging whether the algorithm converges or not.
CN201911178041.9A 2019-11-27 2019-11-27 High-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation Pending CN110913402A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911178041.9A CN110913402A (en) 2019-11-27 2019-11-27 High-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911178041.9A CN110913402A (en) 2019-11-27 2019-11-27 High-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation

Publications (1)

Publication Number Publication Date
CN110913402A true CN110913402A (en) 2020-03-24

Family

ID=69819869

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911178041.9A Pending CN110913402A (en) 2019-11-27 2019-11-27 High-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation

Country Status (1)

Country Link
CN (1) CN110913402A (en)

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111555798A (en) * 2020-05-09 2020-08-18 中国航空无线电电子研究所 Multi-platform aviation network clustering method
CN111683375A (en) * 2020-05-08 2020-09-18 北京科技大学 Unmanned aerial vehicle deployment optimization method for unmanned aerial vehicle-assisted wireless cellular network
CN111787548A (en) * 2020-06-30 2020-10-16 北京邮电大学 Method and device for deploying near-earth wireless network
CN112068592A (en) * 2020-08-31 2020-12-11 南京航空航天大学 Dispatching method for realizing fence coverage based on rechargeable unmanned aerial vehicle
CN112578811A (en) * 2020-12-02 2021-03-30 中国联合网络通信集团有限公司 Unmanned aerial vehicle cluster performance method and device
CN113050687A (en) * 2021-03-19 2021-06-29 四川大学 Multi-unmanned aerial vehicle formation recombination track planning method
CN113115399A (en) * 2021-03-31 2021-07-13 南京航空航天大学 Route optimization method for self-organizing network of heterogeneous unmanned aerial vehicle
CN113271643A (en) * 2021-03-06 2021-08-17 南京航空航天大学 Multi-node cooperative unmanned aerial vehicle ad hoc network clustering topology reconstruction method
CN113395676A (en) * 2021-08-17 2021-09-14 南京航空航天大学 Unmanned aerial vehicle task cooperation method for forming game based on overlapping alliance
CN115474216A (en) * 2022-11-02 2022-12-13 中国人民解放军国防科技大学 Flight ad hoc network topology optimization method and device based on adaptive hummingbird algorithm
CN117241274A (en) * 2023-08-22 2023-12-15 国网冀北电力有限公司张家口供电公司 Communication method of self-adaptive networking

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109525958A (en) * 2018-12-22 2019-03-26 北京工业大学 A kind of unmanned plane cluster network controller design method of software definition
CN110149588A (en) * 2019-05-17 2019-08-20 电信科学技术研究院有限公司 Determine the method, apparatus, equipment and storage medium of the position of unmanned plane base station

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109525958A (en) * 2018-12-22 2019-03-26 北京工业大学 A kind of unmanned plane cluster network controller design method of software definition
CN110149588A (en) * 2019-05-17 2019-08-20 电信科学技术研究院有限公司 Determine the method, apparatus, equipment and storage medium of the position of unmanned plane base station

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
程潇: "无人机编队组网技术研究", 《中国优秀硕士学位论文数据库》 *

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111683375A (en) * 2020-05-08 2020-09-18 北京科技大学 Unmanned aerial vehicle deployment optimization method for unmanned aerial vehicle-assisted wireless cellular network
CN111683375B (en) * 2020-05-08 2021-07-16 北京科技大学 Unmanned aerial vehicle deployment optimization method for unmanned aerial vehicle-assisted wireless cellular network
CN111555798B (en) * 2020-05-09 2022-02-11 中国航空无线电电子研究所 Multi-platform aviation network clustering method
CN111555798A (en) * 2020-05-09 2020-08-18 中国航空无线电电子研究所 Multi-platform aviation network clustering method
CN111787548A (en) * 2020-06-30 2020-10-16 北京邮电大学 Method and device for deploying near-earth wireless network
CN111787548B (en) * 2020-06-30 2021-07-06 北京邮电大学 Method and device for deploying near-earth wireless network
CN112068592A (en) * 2020-08-31 2020-12-11 南京航空航天大学 Dispatching method for realizing fence coverage based on rechargeable unmanned aerial vehicle
CN112068592B (en) * 2020-08-31 2021-10-26 南京航空航天大学 Dispatching method for realizing fence coverage based on rechargeable unmanned aerial vehicle
CN112578811A (en) * 2020-12-02 2021-03-30 中国联合网络通信集团有限公司 Unmanned aerial vehicle cluster performance method and device
CN112578811B (en) * 2020-12-02 2022-11-22 中国联合网络通信集团有限公司 Unmanned aerial vehicle cluster performance method and device
CN113271643A (en) * 2021-03-06 2021-08-17 南京航空航天大学 Multi-node cooperative unmanned aerial vehicle ad hoc network clustering topology reconstruction method
CN113050687A (en) * 2021-03-19 2021-06-29 四川大学 Multi-unmanned aerial vehicle formation recombination track planning method
CN113115399B (en) * 2021-03-31 2022-11-29 南京航空航天大学 Route optimization method for self-organizing network of heterogeneous unmanned aerial vehicle
CN113115399A (en) * 2021-03-31 2021-07-13 南京航空航天大学 Route optimization method for self-organizing network of heterogeneous unmanned aerial vehicle
CN113395676B (en) * 2021-08-17 2021-11-09 南京航空航天大学 Unmanned aerial vehicle task cooperation method for forming game based on overlapping alliance
CN113395676A (en) * 2021-08-17 2021-09-14 南京航空航天大学 Unmanned aerial vehicle task cooperation method for forming game based on overlapping alliance
CN115474216A (en) * 2022-11-02 2022-12-13 中国人民解放军国防科技大学 Flight ad hoc network topology optimization method and device based on adaptive hummingbird algorithm
CN115474216B (en) * 2022-11-02 2023-04-07 中国人民解放军国防科技大学 Flight ad hoc network topology optimization method and device based on adaptive hummingbird algorithm
CN117241274A (en) * 2023-08-22 2023-12-15 国网冀北电力有限公司张家口供电公司 Communication method of self-adaptive networking
CN117241274B (en) * 2023-08-22 2024-03-19 国网冀北电力有限公司张家口供电公司 Communication method of self-adaptive networking

Similar Documents

Publication Publication Date Title
CN110913402A (en) High-coverage-efficiency unmanned aerial vehicle ad hoc network clustering method for jointly optimizing communication and formation
Wang et al. Deployment algorithms of flying base stations: 5G and beyond with UAVs
Zhao et al. Efficiency maximization for UAV-enabled mobile relaying systems with laser charging
Shamsoshoara et al. An autonomous spectrum management scheme for unmanned aerial vehicle networks in disaster relief operations
You et al. Joint optimization of area coverage and mobile-edge computing with clustering for FANETs
Lee et al. Multiagent Q-learning-based multi-UAV wireless networks for maximizing energy efficiency: Deployment and power control strategy design
CN110730031B (en) Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication
CN111031513A (en) Multi-unmanned-aerial-vehicle-assisted Internet-of-things communication method and system
CN110730495A (en) Unmanned aerial vehicle data distribution optimization method under energy constraint
CN111768654B (en) Multi-unmanned aerial vehicle cooperative relay assisted vehicle-mounted ad hoc network data transmission method
Wang et al. Energy-efficient UAV deployment and task scheduling in multi-UAV edge computing
Zhang et al. Power control and trajectory planning based interference management for UAV-assisted wireless sensor networks
WO2023010712A1 (en) Optimization method and device for communication network of aerial swarm
CN111970709A (en) Unmanned aerial vehicle relay deployment method and system based on particle swarm optimization algorithm
Cheng et al. MOOC: a mobility control based clustering scheme for area coverage in FANETs
Liu et al. Joint resource optimization for UAV-enabled multichannel Internet of Things based on intelligent fog computing
CN114070379A (en) Unmanned aerial vehicle flight path optimization and resource allocation method based on safety energy efficiency fairness
Lu et al. Relay in the sky: A UAV-aided cooperative data dissemination scheduling strategy in VANETs
Hussain et al. Co-DLSA: Cooperative delay and link stability aware with relay strategy routing protocol for flying Ad-hoc network
Huang et al. Task offloading in uav swarm-based edge computing: Grouping and role division
Xiao et al. Energy-efficient resource allocation in multiple UAVs-assisted energy harvesting-powered two-hop cognitive radio network
Xing et al. Nash network formation among unmanned aerial vehicles
CN114945182B (en) Multi-unmanned aerial vehicle relay optimization deployment method in urban environment
Zhang et al. An energy-efficient UAV deployment scheme for emergency communications in air-ground networks with joint trajectory and power optimization
Cai et al. Trajectory design and resource allocation for UAV-enabled data collection in wireless sensor networks with 3D blockages

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20200324

WD01 Invention patent application deemed withdrawn after publication