CN111970709A - Unmanned aerial vehicle relay deployment method and system based on particle swarm optimization algorithm - Google Patents

Unmanned aerial vehicle relay deployment method and system based on particle swarm optimization algorithm Download PDF

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CN111970709A
CN111970709A CN202010662808.1A CN202010662808A CN111970709A CN 111970709 A CN111970709 A CN 111970709A CN 202010662808 A CN202010662808 A CN 202010662808A CN 111970709 A CN111970709 A CN 111970709A
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孙红光
高振宇
李书琴
张宏鸣
徐超
南雨航
申环环
马亚军
宋振东
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Abstract

The invention belongs to the technical field of wireless communication, and discloses an unmanned aerial vehicle relay deployment method and system based on a particle swarm optimization algorithm, wherein a channel model of an unmanned aerial vehicle relay is designed and defined, and large-scale fading path loss and small-scale fading of the unmanned aerial vehicle relay are calculated to obtain channel gain and signal-to-interference-and-noise ratio of different devices during connection; designing and defining an energy consumption model of the unmanned aerial vehicle relay, and defining the power consumption composition of the unmanned aerial vehicle in a hovering state; constructing an optimization target, and converting a constrained mixed 01 integer nonlinear programming problem into an unconstrained optimization problem; and optimizing the transmitting power of the terminal equipment, the relay candidate deployment position of the unmanned aerial vehicle and the association relation of the terminal equipment, the relay of the unmanned aerial vehicle and a channel by combining the improved particle swarm algorithm so as to minimize the total energy consumption of the system. The invention improves the standard particle swarm algorithm, is more suitable for solving the optimization problem, and improves the execution efficiency of the algorithm.

Description

Unmanned aerial vehicle relay deployment method and system based on particle swarm optimization algorithm
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to an unmanned aerial vehicle relay deployment method and system based on a particle swarm optimization algorithm.
Background
Currently, most IoT (internet of things) systems, which are mainly suitable for short-distance communication scenarios, include many terminal devices with limited computing power and communication power. Although a mature communication technology supporting long-distance communication exists at present, the technology has a high requirement on the transmission power of the terminal device, and high transmission power inevitably brings high energy consumption, and even shortens the working time of the terminal device. Because many IoT systems at present are composed of many terminal devices that have low power consumption and low computational power and only support short-distance communication, stable communication links cannot be established with a remote base station or a sink node, the quality of long-distance communication cannot be guaranteed, and even long-distance communication links cannot be established. To solve the problems in the above scenarios, many IoT systems are now deployed with consideration of adding relay devices to assist in completing long-distance communication tasks. In such an IoT system, the relay device collects data from the terminal device, and then forwards the data to the remote base station or the sink node, without further processing the data, and assumes the task of collecting and forwarding, and plays a role of a "man-in-the-middle" between the terminal device and the remote base station. Generally, in an IoT system, a relay device tends to occupy more scheduling resources than a terminal device, resulting in more energy consumption and more capital expenditure for deployment. Therefore, a reasonable deployment strategy of the relay equipment is designed, and the utilization rate of communication resources is improved while reliable data transmission is ensured, so that the throughput of the IoT system is maximized, the communication interruption probability of the IoT system is minimized, or the total energy consumption of the system is minimized.
Ahmad Alsharoa proposes An Internet of things Energy-saving deployment relay selection method based on traditional ground relay nodes in a published paper "An Energy-Efficient deployment Scheme for Internet of things Communications", comprehensively considers three optimization targets of terminal equipment transmitting power, relay node deployment position and terminal equipment-relay-channel association relation, constructs a mixed integer nonlinear programming problem (MINLP), introduces substitute variables, converts the problem into a mixed integer linear programming problem (MNLP), and obtains An approximate solution through a genetic algorithm. In order to reduce the computational complexity of large-scale fading, the influence of Line of sight (LoS) links and non-Line of sight (NLoS) links on channel gain is not considered, so that a constructed channel model is not accurate enough, the simulation time bias of an actual application scene is ideal, the simulation value of the large-scale fading is greater than the large-scale fading value in the actual application scene, and the obtained channel gain value is smaller than the actual channel gain value, so that higher terminal equipment transmitting power or more relay nodes are required to be deployed to meet the QoS, and the successful deployment of an internet of things system is realized.
The Omid Esarafillan and Omid Esarafillan consider the problem of terminal equipment Association when multiple unmanned aerial vehicle relays participate in the published paper "Simultaneous User Association and plan in Multi-UAV Enabled Wirelessnetworks": and the terminal equipment transmits the information to the base station through the associated unmanned aerial vehicle relay. The incidence relation between the terminal equipment and the unmanned aerial vehicle relay is determined by the position of the unmanned aerial vehicle, so that the throughput of each communication link can be maximized by finding the optimal position of the unmanned aerial vehicle relay. This document realizes the above-mentioned goal through jointly optimizing the incidence relation of terminal equipment and unmanned aerial vehicle relay and the position of unmanned aerial vehicle relay. The above studies do not take into account terminal device transmit power, candidate locations in the drone, and their corresponding channel relationships. The scheme is suitable for the problem of solving the optimization of the association relation between the terminal equipment and the unmanned aerial vehicle relay in the above problems.
Through the above analysis, the problems and defects of the prior art are as follows: the existing energy-saving deployment relay selection method of the internet of things based on the traditional ground relay node does not consider the influence of a line-of-sight link and a non-line-of-sight link on channel gain, omits partial solution details and reduces the accuracy of an approximate solution obtained by a genetic algorithm.
The difficulty in solving the above problems and defects is: power consumption models and advantages and disadvantages of different types of relay nodes need to be considered; the channel model of the relay node needs to be considered and perfected so as to improve the reliability of the simulation result; the influence of channel models of different types of relay nodes on the actual deployment of the Internet of things system needs to be considered; a low complexity solution algorithm is required to obtain a higher accuracy approximate solution.
The significance of solving the problems and the defects is as follows: in the actual deployment of the Internet of things system, relay nodes with lower power consumption and higher channel quality are selected from relay nodes of different types, so that higher link quality is provided for communication between terminal equipment and a base station or the relay nodes, and the transmission power of the terminal equipment and the actual deployment number of the relay nodes are reduced, so that the energy-saving deployment of the Internet of things system is realized.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an unmanned aerial vehicle relay deployment method and system based on a particle swarm optimization algorithm.
The invention is realized in such a way that an unmanned aerial vehicle relay deployment method based on a particle swarm optimization algorithm comprises the following steps: designing and defining a channel model of the unmanned aerial vehicle relay, and calculating large-scale fading path loss and small-scale fading to obtain channel gain and signal-to-noise ratio of different devices during connection; designing and defining an energy consumption model of the unmanned aerial vehicle relay, and defining the power consumption composition of the unmanned aerial vehicle in a hovering state; constructing an optimization target, and converting a constrained mixed 01 integer nonlinear programming problem into an unconstrained optimization problem; and optimizing the transmitting power of the terminal equipment, the relay candidate deployment position of the unmanned aerial vehicle and the association relation of the terminal equipment, the relay of the unmanned aerial vehicle and a channel by combining the improved particle swarm algorithm so as to minimize the total energy consumption of the system.
Further, the unmanned aerial vehicle relay deployment method based on the particle swarm optimization algorithm meets the QoS (quality of service) of all uplink users; performing joint optimization on the uplink transmitting power of the terminal equipment and the incidence relation between the terminal equipment and the unmanned aerial vehicle relay channel, and determining the number of deployed unmanned aerial vehicle relays and the optimal deployment position; and solving the constructed mixed 01 integer nonlinear programming problem by an improved particle swarm algorithm to obtain an approximate minimum value of the overall total energy consumption of the system, thereby realizing energy-saving deployment of the Internet of things system.
Further, the unmanned aerial vehicle relay deployment method based on the particle swarm optimization algorithm further comprises the following steps:
(1) designing a channel model of the unmanned aerial vehicle relay;
(2) designing an energy consumption model of the unmanned aerial vehicle relay;
(3) constructing an optimization target, aiming at minimizing the total energy consumption of the terminal equipment and the unmanned aerial vehicle relay in the IoT system, minimizing the total energy consumption of the unmanned aerial vehicle relay, namely minimizing the deployment number of the unmanned aerial vehicle relay, and selecting as few unmanned aerial vehicle relays as possible from a plurality of candidate deployment positions to deploy on the premise of meeting the service quality; the minimization of the total energy consumption of the terminal equipment can be realized by minimizing the transmitting power of the terminal equipment on the basis of stable communication between the terminal equipment and the unmanned aerial vehicle relay;
(4) introducing a penalty function to reduce the solving complexity, converting the constrained optimization problem into the unconstrained optimization problem by introducing the penalty function, and defining the objective function f as EtotalAnd a penalty function, the formula being:
f＝Etotal+kp*penalty；
Figure BDA0002579245000000041
in the above formula kpDetermining the magnitude of punishment strength by setting punishment coefficients with different magnitudes as punishment coefficients, wherein the punishment is a punishment function and is converted from the punishment coefficients through limiting conditions;
(5) improved particle swarm algorithm solution,
Further, the designing the channel model of the unmanned aerial vehicle relay includes:
1) calculating the large-scale fading path loss pathloss of the unmanned aerial vehicle relay:
Figure BDA0002579245000000042
Figure BDA0002579245000000043
Figure BDA0002579245000000044
wherein f iskIs the frequency of channel k, dn，UIs the distance between the terminal equipment n and the unmanned aerial vehicle U, c is the speed of light in vacuum, ηLoSAnd ηNLoSIs an additional attenuation coefficient when LoS and NLoS are connected, and if the antenna of the unmanned aerial vehicle and the antenna of the terminal equipment are vertically arranged, the additional attenuation coefficient is
Figure BDA0002579245000000045
Is the probability of LoS connection, a and b are constants determined by the environment, θ is the elevation angle from the terminal device to the drone relay, and is expressed as θ ═ arcsin (Ht/Lt), where Ht is the altitude of the drone, Lt is the euclidean distance from the drone to the terminal device, PRNLoSIs the probability of NLoS connection, the probability of NLoS connection is PRNLoS＝1-PRLoS；
2) According to the channel model, the channel gain on channel k associated to terminal device n of the drone relay can be expressed as:
Figure BDA0002579245000000051
in the formula (I), the compound is shown in the specification,
Figure BDA0002579245000000052
subject to the Nakagami-m distribution,
Figure BDA0002579245000000053
obeying the distribution with the parameter m, the channels have mutual difference, and the channel gain from the terminal equipment n to the unmanned aerial vehicle relay
Figure BDA0002579245000000054
Is equal to
Figure BDA0002579245000000055
3) Signal-to-interference-and-noise ratio SINR gamma from terminal equipment n to unmanned aerial vehicle relay m through channel kUAVComprises the following steps:
Figure BDA0002579245000000056
wherein the content of the first and second substances,
Figure BDA0002579245000000057
relaying, for the drone, accumulated interference from other terminal devices received on channel k, the accumulated interference being from users associated to other drones through channel k;
Figure BDA0002579245000000058
2variance of additive white Gaussian noise AWGN as zero mean, defining gammathSINR threshold value required for normal communication between the terminal user and the unmanned aerial vehicle relay, namely the QoS requirement, when gamma isUAV＞γthIn time, the terminal device may establish communication with the drone relay.
Further, the design of the energy consumption model of the unmanned aerial vehicle relay includes:
1) the propulsion power consumption under the hovering state of the multi-rotor unmanned aerial vehicle is expressed as:
Ph＝P0+Pi；
wherein, P0，PiAnd the time is two constants which respectively represent the rotating power and the induced power of the blade in the hovering state. Assuming that each terminal device and the unmanned aerial vehicle relay pass through and establish a communication link only through 1 channel, the communication power of the unmanned aerial vehicle relay is PitemIf n terminal devices are connected with the unmanned aerial vehicle relay, the communication power of the unmanned aerial vehicle relay is n PitemAnd is marked as Pc;
2) the main energy consumption of the unmanned aerial vehicle relay is derived from the propulsion energy consumption, and the total power consumption of the unmanned aerial vehicle relay is represented as:
PUAV＝Ph+Pc；
3) the total energy consumption of the Internet of things system consists of the energy consumption of terminal equipment and the relay energy consumption of an unmanned aerial vehicle:
Etotal＝ωEuser+(1-ω)EUAV；
wherein, omega is a weight coefficient for balancing the energy consumption ratio of the terminal equipment and the unmanned aerial vehicle relay, EuserIndicating the total energy consumption of the terminal equipment within the length of the T time slot, EUAVIndicating total unmanned aerial vehicle relay energy consumption within T slot length, EtotalIs the total energy consumption of the system.
Further, the constructing of the optimization objective includes:
1) using e to represent a binary three-dimensional matrix of size M x K x N, each element is as follows:
Figure BDA0002579245000000061
2) whether the unmanned aerial vehicle is deployed at the candidate position is represented by a binary vector pi with the size of 1 × M, wherein each element is as follows:
Figure BDA0002579245000000062
3) the total energy consumption of the equipment is obtained as follows:
Figure BDA0002579245000000063
4) the total energy consumption of the UAV relay node is as follows:
Figure BDA0002579245000000064
t is the data transmission period, PUAV、PuserPower of the drone and the user, respectively;
5) converting the energy-saving relay strategy problem into the minimum total energy consumption problem of the IoT system when the QoS is satisfied, wherein:
minimize Etotal＝ωEuser+(1-ω)EUAV；
s.t.：
Figure BDA0002579245000000065
Figure BDA0002579245000000066
Figure BDA0002579245000000067
Figure BDA0002579245000000068
Figure BDA0002579245000000071
Figure BDA0002579245000000072
here, ω is a weight constant for balancing the energy consumption ratio of the terminal device and the drone relay, (1) indicates that the transmission power of each terminal device may not exceed the maximum transmission power; (2) indicating that the terminal device to drone relay communication must meet its own QoS requirements; (3) indicating that the terminal equipment of each unmanned aerial vehicle relay service cannot exceed the total channel number K; (4) each terminal device is in relay connection with the unmanned aerial vehicle through 1 channel and only 1 channel; (5) indicating that the drone relay cannot be connected if not activated; (6) indicating that the number of active drone relays must be able to serve all terminal devices.
Further, the improved particle swarm algorithm solving comprises:
1) initializing all particles, and dividing 1 particle into 3 sub-particles according to different variable types corresponding to different dimensions to obtain the position x1 of each sub-particle; x 2; x3 and velocity v 1; v 2; v 3;
2) calculating a fitness value F (i) for each particle;
3) for each particle, comparing the fitness value F (i) with the individual extreme value Pbest (i), and if F (i) < Pbest (i), making Pbest (i) equal to F (i); for each particle, comparing the fitness value F (i) with a global extreme value Gtest, and if F (i) < Gtest, making Gtest equal to F (i);
4) updating the position and the speed of each sub-particle according to an updating formula;
5) exit if maximum number of cycles is reached, otherwise return to 2).
Another object of the present invention is to provide an unmanned aerial vehicle relay deployment system implementing the unmanned aerial vehicle relay deployment method based on particle swarm optimization, the unmanned aerial vehicle relay deployment system comprising:
the service quality setting module is used for meeting the service quality of all uplink users;
the unmanned aerial vehicle relay number and deployment position determining module is used for carrying out combined optimization on the uplink transmitting power of the terminal equipment and the incidence relation between the terminal equipment and the unmanned aerial vehicle relay channel, and determining the number and the optimal deployment position of the unmanned aerial vehicle relays;
and the energy-saving deployment module of the Internet of things system is used for solving the constructed mixed 01 nonlinear programming problem through an improved particle swarm algorithm to obtain an approximate minimum value of the overall total energy consumption of the system, so that the energy-saving deployment of the Internet of things system is realized.
The invention also aims to provide the communication terminal of the internet of things, and the communication terminal of the internet of things carries the unmanned aerial vehicle relay deployment system.
Another object of the present invention is to provide an unmanned aerial vehicle, wherein the unmanned aerial vehicle is equipped with the unmanned aerial vehicle relay deployment system.
By combining all the technical schemes, the invention has the advantages and positive effects that: under an uplink Internet of things transmission scene, an unmanned aerial vehicle relay selection method based on a particle swarm algorithm is provided. The invention realizes the minimization of the total energy consumption of the Internet of things system by optimizing the transmitting power of the terminal equipment, the deployment number of the relays of the unmanned aerial vehicle and the incidence relation between the terminal equipment and the relays and channels. The invention improves the solving efficiency of the energy-saving deployment strategy of the Internet of things. According to the invention, a suboptimal solution of energy-saving deployment can be obtained, the time complexity of violently solving the relay selection problem of the unmanned aerial vehicle is reduced, the optimization efficiency is improved, and a more efficient solving algorithm is provided for the energy-saving deployment of the Internet of things.
According to the invention, the unmanned aerial vehicle is selected as the relay equipment, so that the advantages of low cost, flexible movement and rapid deployment of the unmanned aerial vehicle compared with the traditional ground relay node are fully utilized, and as the unmanned aerial vehicle relays in low altitude flight, a line-of-sight link is easier to construct compared with the traditional ground relay, the probability of constructing a non-line-of-sight link due to the shielding of ground obstacles is reduced, and the channel quality is improved; unmanned aerial vehicle relay deploys in a flexible way, retrieves rapidly, and the change of response demand that can be faster simultaneously.
The invention improves the standard particle swarm algorithm, is more suitable for the current mixed 01 integer nonlinear programming problem, and improves the execution efficiency of the algorithm. Compared with a greedy heuristic algorithm, the improved particle swarm algorithm has higher solving precision, and greatly reduces the calculation complexity of the algorithm at the cost of certain performance loss.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of an unmanned aerial vehicle relay deployment method provided in an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an unmanned aerial vehicle relay deployment system provided in an embodiment of the present invention;
in fig. 2: 1. a quality of service setting module; 2. the unmanned aerial vehicle relay number and deployment position determining module; 3. and the system of the Internet of things is an energy-saving deployment module.
Fig. 3 is a schematic diagram of an application scenario provided in the embodiment of the present invention.
Fig. 4 is a schematic diagram of a system scenario provided in the embodiment of the present invention.
Fig. 5 is a flowchart of an improved particle swarm algorithm provided by an embodiment of the present invention.
Fig. 6 is a schematic diagram of a simulation scenario provided in an embodiment of the present invention.
Fig. 7 is a diagram of simulation results provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following 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.
Aiming at the problems in the prior art, the invention provides an unmanned aerial vehicle relay deployment method and system based on a particle swarm optimization algorithm, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the unmanned aerial vehicle relay deployment method provided by the present invention includes the following steps:
s101: the Quality of Service (QoS) of all uplink terminal equipment is satisfied;
s102: performing joint optimization on the uplink transmitting power of the terminal equipment and the incidence relation between the terminal equipment and the unmanned aerial vehicle relay channel, and determining the number of deployed unmanned aerial vehicle relays and the optimal deployment position;
s103: and solving the constructed mixed 01 integer nonlinear programming problem by an improved particle swarm algorithm to obtain an approximate minimum value of the overall total energy consumption of the system, thereby realizing energy-saving deployment of the Internet of things system.
Those skilled in the art can also implement the relay deployment method of the unmanned aerial vehicle by using other steps, and the relay deployment method of the unmanned aerial vehicle provided by the invention in fig. 1 is only one specific embodiment.
The method comprises the following specific steps:
(1) designing a channel model of the unmanned aerial vehicle relay:
(1a) calculating the large-scale fading path loss pathloss of the unmanned aerial vehicle relay according to the following formula:
Figure BDA0002579245000000101
Figure BDA0002579245000000102
Figure BDA0002579245000000103
wherein f iskIs the frequency of channel k, dn，UIs the distance between the terminal equipment n and the unmanned aerial vehicle U, c is the speed of light in vacuum, ηLoSAnd ηNLoSWhen LoS and NLoS are connectedAdding attenuation coefficient, and assuming that the antenna of the unmanned aerial vehicle and the antenna of the terminal equipment are both vertically arranged
Figure BDA0002579245000000104
Is the probability of LoS connection, a and b are constants determined by the environment, θ is the elevation angle from the terminal device to the drone relay, and is expressed as θ ═ arcsin (Ht/Lt), where Ht is the altitude of the drone, Lt is the euclidean distance from the drone to the terminal device, PRNLoSIs the probability of NLoS connection, the probability of NLoS connection is PRNLoS＝1-PRLoS；
(1b) According to the above channel model, the channel gain on channel k associated to terminal device n of the drone may be expressed as:
Figure BDA0002579245000000105
in the above formula, the first and second light sources are,
Figure BDA0002579245000000106
subject to the Nakagami-m distribution,
Figure BDA0002579245000000107
a distribution with a parameter m is obeyed. Assuming that the channels have mutual difference, the channel gain from the terminal device n to the unmanned aerial vehicle relay
Figure BDA0002579245000000108
Is equal to
Figure BDA0002579245000000109
(1c) Therefore, the signal to interference and noise ratio SINR gamma of the terminal equipment n to the unmanned aerial vehicle relay m through the channel k can be obtainedUAVComprises the following steps:
Figure BDA00025792450000001010
wherein the content of the first and second substances,
Figure BDA00025792450000001011
relaying the accumulated interference received on channel k for this drone from other terminal devices, which, by assumption, comes from users associated to other drones through channel k.
Figure BDA0002579245000000111
2Variance of Additive White Gaussian Noise (AWGN) with zero mean. Definition of gammathSINR threshold value, i.e. quality of service QoS requirement, required for normal communication between end user and drone relay, so that when γ is reachedUAV＞γthIn time, the terminal device may establish communication with the drone relay.
(2) Designing an energy consumption model of the unmanned aerial vehicle relay:
the unmanned aerial vehicle energy consumption mainly comprises the relevant energy consumption of communication and propulsion energy consumption, and the relevant energy consumption of communication mainly includes the energy of consumption such as communication circuit, signal processing, signal radiation/receipt. The propulsion energy consumption is mainly used for providing power for the air flight and hovering of the unmanned aerial vehicle. Generally speaking, the propulsion power of a drone depends on the flight speed of the drone and on its acceleration. In the present invention, the energy consumption caused by acceleration/deceleration of the drone is ignored in order to simplify the analysis, which is reasonable for the case where the duration of the maneuver of the drone is only a fraction of the total operating time.
(2a) The propulsion power consumption in the hovering state of the multi-rotor unmanned aerial vehicle can be expressed as:
Ph＝P0+Pi；
wherein, P0，PiAnd the time is two constants which respectively represent the rotating power and the induced power of the blade in the hovering state. Assuming that each terminal device and the unmanned aerial vehicle relay pass through and establish a communication link only through 1 channel, the communication power of the unmanned aerial vehicle relay is PitemIf n terminal devices are connected with the unmanned aerial vehicle relay, the communication power of the unmanned aerial vehicle relay is n PitemAnd is denoted as Pc.
(2b) It is assumed that the main energy consumption of the drone relay is derived from the propulsion energy consumption. The total power consumption of the drone relay may be expressed as:
PUAV＝Ph+Pc；
(2c) assuming that the total energy consumption of the Internet of things system consists of the energy consumption of terminal equipment and the relay energy consumption of the unmanned aerial vehicle, namely:
Etotal＝ωEuser+(1-ω)EUAV；
wherein, omega is a weight coefficient for balancing the energy consumption ratio of the terminal equipment and the unmanned aerial vehicle relay, EuserIndicating the total energy consumption of the terminal equipment within the length of the T time slot, EUAVIndicating total unmanned aerial vehicle relay energy consumption within T slot length, EtotalIs the total energy consumption of the system.
(3) Constructing an optimization target:
the invention aims to minimize the total energy consumption of the terminal equipment and the unmanned aerial vehicle relay in the IoT system. The total energy consumption of the unmanned aerial vehicle relays is minimized, namely the deployment number of the unmanned aerial vehicle relays is minimized, and on the premise of meeting the service quality, the unmanned aerial vehicle relays are selected from a plurality of candidate deployment positions as few as possible to be deployed; the minimization of the energy consumption of the terminal equipment can minimize the transmitting power of the terminal equipment on the basis that the terminal equipment is stably communicated with the unmanned aerial vehicle relay, so that the minimization of the energy consumption is realized. Meanwhile, the invention also considers that on the basis of meeting the conditions, higher communication quality is provided as far as possible, so that the association relation of the terminal equipment, the unmanned aerial vehicle relay and the channel is also brought into an optimization range.
(3a) The invention uses epsilon to represent a binary three-dimensional matrix with the size of M x K x N, and each element is as follows:
Figure BDA0002579245000000121
(3b) the invention represents whether the unmanned aerial vehicle is deployed at a candidate position or not through a binary vector pi with the size of 1 x M, and each element is as follows:
Figure BDA0002579245000000122
(3c) the total energy consumption of the equipment is obtained as follows:
Figure BDA0002579245000000123
(3d) at this time, the total energy consumption of the UAV relay node is:
Figure BDA0002579245000000124
here, T is the data transmission period, PUAV、PuserRespectively, the power of the drone and the user.
(3e) The energy-saving relay policy problem can thus be converted to solve the IoT system minimum total energy consumption problem when QoS is satisfied, as follows:
minimize Etotal＝ωEuser+(1-ω)EUAV；
s.t.：
Figure BDA0002579245000000125
Figure BDA0002579245000000126
Figure BDA0002579245000000131
Figure BDA0002579245000000132
Figure BDA0002579245000000133
Figure BDA0002579245000000134
here, ω is a weight constant for balancing the energy consumption ratio of the terminal device and the UAV relay, (1) indicates that the transmit power of each terminal device may not exceed the maximum transmit power; (2) indicating that the terminal device to drone relay communication must meet its own QoS requirements; (3) indicating that the terminal equipment of each unmanned aerial vehicle relay service cannot exceed the total channel number K; (4) each terminal device is in relay connection with the unmanned aerial vehicle through 1 channel and only 1 channel; (5) indicating that the drone relay cannot be connected if not activated; (6) indicating that the number of active drone relays must be able to serve all terminal devices.
(4) Introducing a penalty function reduces the solution complexity:
the problem is obtained by analysis, is a mixed 01 integer nonlinear programming problem, has more limiting conditions, and improves the solving difficulty. In order to solve the problem, the invention converts the constrained optimization problem into the unconstrained optimization problem by introducing a penalty function method, thereby reducing the solving difficulty. The invention defines an objective function f as EtotalAnd a penalty function, the formula is as follows:
f＝Etotal+kp*penalty；
Figure BDA0002579245000000135
in the above formula kpAnd determining the magnitude of penalty degree by setting penalty coefficients with different magnitudes for the penalty coefficients, wherein penalty is a penalty function and is converted by a limiting condition. Due to the variable pimAnd e the presence of two binary vectors, making the optimization problem an NP-hard problem. When the scale is large, the algorithm complexity is high, and in order to obtain a better solving effect, the method is improved to a certain extent based on the standard particle swarm algorithm.
(5) Solving by an improved particle swarm algorithm:
(5a) all particles are initialized. Dividing 1 particle into 3 sub-particles according to different variable types corresponding to different dimensions to obtain the position x1 of each sub-particle; x 2; x3 and velocity v 1; v 2; v 3.
(5b) The fitness value F (i) of each particle is calculated.
(5c) For each particle, comparing the fitness value F (i) with the individual extreme value Pbest (i), and if F (i) < Pbest (i), making Pbest (i) equal to F (i); for each particle, its fitness value F (i) is compared with the global extremum Gbest, and if F (i) < Gbest, let Gbest be F (i).
(5d) And updating the position and the speed of each sub-particle according to the updating formula.
(5e) And exiting if the maximum number of cycles is reached, and returning to the step (5b) if the maximum number of cycles is not reached.
As shown in fig. 2, the unmanned aerial vehicle relay deployment system provided by the present invention includes:
the service quality setting module 1 is used for meeting the service quality of all uplink users;
the unmanned aerial vehicle relay number and deployment position determining module 2 is used for carrying out combined optimization on the uplink transmitting power of the terminal equipment and the incidence relation between the terminal equipment and the unmanned aerial vehicle relay channel, and determining the number and the optimal deployment position of unmanned aerial vehicle relays;
and the energy-saving deployment module 3 of the internet of things system is used for solving the constructed mixed 01 nonlinear programming problem through an improved particle swarm algorithm to obtain an approximate minimum value of the overall total energy consumption of the system, so that the energy-saving deployment of the internet of things system is realized.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
TABLE 1 meanings of the respective characters
Figure BDA0002579245000000151
The unmanned aerial vehicle relay deployment method designs and defines a channel model of the unmanned aerial vehicle relay, calculates large-scale fading path loss and small-scale fading of the unmanned aerial vehicle relay, and obtains channel gain and signal-to-noise ratio of different devices during connection; designing and defining an energy consumption model of the unmanned aerial vehicle relay, and defining the power consumption composition of the unmanned aerial vehicle in a hovering state; constructing an optimization target, and converting a constrained mixed 01 integer nonlinear programming problem into an unconstrained optimization problem; and optimizing the transmitting power of the terminal equipment, the relay candidate deployment position of the unmanned aerial vehicle and the association relation of the terminal equipment, the relay of the unmanned aerial vehicle and a channel by combining the improved particle swarm algorithm so as to minimize the total energy consumption of the system.
As shown in fig. 3, the implementation scenario of the present invention is further described in detail. The invention is mainly applied to non-time-delay sensitive scenes, as shown in fig. 3, in a farmland scene with a large number of sensors for temperature, humidity and the like, an unmanned aerial vehicle relay can undertake the data collection task, and then all data are gathered to upper-layer equipment for data processing and analysis.
As shown in fig. 4, a specific network system scenario of the present invention is described in further detail. The IoT network system is composed of three layers of nodes, namely a terminal device, an unmanned aerial vehicle relay and a base station. The invention mainly researches the uplink data transmission process in the Internet of things system. The scene comprises N terminal devices, M unmanned aerial vehicle relay candidate positions and 1 base station. The drone relay selects several optimal deployment positions from several candidate deployment positions to achieve coverage for all terminal devices, each terminal device having to establish a communication link with a remote base station through the drone relay. Assuming that the drone relay can establish reliable communication links with the base station at all candidate deployment locations, only the uplink communication process of the terminal device and the drone relay is considered. The communication capability, propulsion power consumption and hovering power consumption of each drone relay are all the same. The total number of channels depends on the radio frequency working frequency band carried by the unmanned aerial vehicle relay, and the channels are orthogonal in frequency.
The invention considers the following association strategies of the user and the unmanned aerial vehicle relay: assume that there are K mutually orthogonal channels in the system, and the attenuation characteristics of each channel are independent of each other. Consider a full frequency reuse pattern, i.e., each drone may use the K subchannels to communicate with users. The drone relay allocates 1 channel to each user, and multiple users associated with the same drone relay cannot use the same channel, that is, each drone relay serves K users at most.
As shown in fig. 5, the improved particle swarm optimization algorithm is further described in detail in the present invention.
Step one, initializing all particles. Dividing 1 particle into 3 sub-particles according to different variable types corresponding to different dimensions to obtain the position x1 of each sub-particle; x 2; x3 and velocity v 1; v 2; v3 for
Figure BDA0002579245000000161
The position and speed of the vehicle. Wherein x1 is an integer variable, x2, x3 is a 01 variable; v1 is a floating point type variable, and v2 and v3 are floating point type variables in the range of (0, 1).
And step two, combining three sub-particles into one particle, substituting the particle into an objective function f, and calculating the fitness value f (i) of each particle.
Step three, for each particle, using the fitness value f (i) of each particle to compare with the individual extreme value Pbest (i), and if f (i) < Pbest (i), making Pbest (i) f (i); for each particle, its fitness value f (i) is compared with the global extremum Gbest, and if f (i) < Gbest, let Gbest be f (i). Respectively setting Pbest corresponding to the description in the step one1，Pbest2，Pbest3And Gbest1，Gbest2，Gbest3The method is used for storing the corresponding individual optimal solution and the population optimal solution so as to update the positions and the speeds of the sub-particles.
And step four, updating the position and the speed of each sub-particle according to the updating formula. x is the number of{t+1}＝round(xt+Vt) And a new position x is obtained.
And step five, if the maximum cycle number is reached, exiting, otherwise, returning to the step two.
The technical effects of the present invention will be described in detail with reference to simulation experiments.
1. Simulation conditions are as follows:
the simulation experiment of the invention is on a Windows platform, and is mainly configured as follows: the CPU is Intel (R) i5-6200U, 2.30 GHz; the memory is 12G; the operating system is Windows 10; the simulation software is Matlab.
Fig. 6 is a schematic view of a simulation scene used in a simulation experiment of the present invention, in which dots represent terminal devices such as temperature and humidity, and asterisks represent relay devices of an unmanned aerial vehicle hovering at a fixed position in the air.
2. Simulation content and result analysis:
the simulation experiment is to compare the method with two greedy heuristic algorithms, use the simulation scene shown in figure 4, and simulate the unmanned aerial vehicle from two angles of the number of channels and the number of the relay candidate deployment positions by adopting the improved particle swarm algorithm according to the simulation conditions. In simulation experiments, the area is 2000 x 2000m2In-range simulation of (1), unmanned aerial vehicle relays at [100m, 120m]The specific parameter table is shown in fig. 6, and the simulation curve obtained by drawing is shown in fig. 7.
The greedy-based heuristic algorithm follows the following principles:
1. and sequencing the candidate positions of the unmanned aerial vehicle relay in an ascending order according to the path loss to obtain a UAV (M), and deploying according to the sequence of the UAV (M), namely preferentially selecting the unmanned aerial vehicle with the minimum path loss to deploy.
2. And traversing all channels of the UAV (1), and accessing the user with the maximum channel gain in the current channel every time.
3. Traversing all channels of the UAV (2: M), trying to access a user with the maximum channel gain in the current channel, and if the conditions are met, accessing the user on the premise of not influencing other users on the channel; if the condition is not met, trying to access the user with the second largest channel gain value, and so on until all the remaining users of the current channel are traversed.
The algorithm is marked as a heuristic algorithm a, and in order to judge main influence factors in the heuristic algorithm a optimization principle, the heuristic algorithm a optimization principle is simply replaced, the principle 1 is replaced by randomly selecting the relay of the unmanned aerial vehicle, and a heuristic algorithm b is obtained and used as a comparison algorithm.
Fig. 7(a) is a comparison graph of total energy consumption curves of the system obtained by respectively setting different particle scale iterations for 50 times when the number N of terminal devices is 25, the number M of candidate deployment positions of the unmanned aerial vehicle is 5, and the total number K of channels is 13 under the above simulation conditions.
As can be seen from fig. 7(a), as the number of particle swarm iterations increases, the total energy consumption of the system decreases, and finally gradually converges to a sub-optimal solution. When the number of the optimized particles is 30000, the convergence speed is fastest; when the number of the optimized particles is 20000, the convergence rate is the second order; when the number of the optimization particles is 10000, the convergence rate is slowest. Therefore, the convergence rate can be increased by increasing the size of the optimum particle. The convergence speed is increased at the cost of increasing the calculation time of each generation of particles. .
Fig. 7(b) and 7(c) are curves of total system energy consumption, total unmanned aerial vehicle energy consumption, and total terminal equipment energy consumption changing with the number of channels when the number N of terminal equipment is 25 and the number M of candidate deployment positions of unmanned aerial vehicles is 10 under the above simulation conditions.
As can be seen from fig. 7(b), as the total number of channels increases, the total system energy consumption decreases, and the total terminal equipment energy consumption also decreases. The total energy consumption of the system is reduced because the number of terminal devices which can be served by a single unmanned aerial vehicle is increased along with the increase of the number of channels, so that the relay number of the activated unmanned aerial vehicle is reduced, and the total energy consumption of the system is reduced; the energy consumption curve of the terminal equipment tends to be reduced because the relay number of the activated unmanned aerial vehicles is not changed any more, the number of the terminal equipment served by the relay of a single unmanned aerial vehicle is increased due to the increase of the number of channels, the number of the multiplexed channels is reduced, and the extra power used by the terminal equipment for resisting the interference generated by other equipment is reduced, so that the total energy consumption of the terminal equipment is reduced.
As can be seen from fig. 7(c), as the number of channels increases, the total energy consumption of the drone and the total energy consumption of the system decrease, and finally, the total energy consumption of the drone does not change any more, and the total energy consumption of the system gradually converges. The number of the activated unmanned aerial vehicle relays is reduced along with the increase of the number of the channels, so that the total energy consumption of the unmanned aerial vehicle is reduced.
Fig. 7(d) is a variation curve of total energy consumption of the terminal device and total energy consumption of the system according to the number of candidate positions for relaying the drone, when the number of terminal devices N is 25 and the number of channels K is 10 under the above simulation conditions.
As can be seen from fig. 7(d), the total energy consumption curve of the system has little change, and the total energy consumption of the terminal equipment tends to decrease. The reason is that although the number of candidate deployment positions of the drone relay increases, the number of actual deployments of the drone relay does not change. When N25, K10, 25 terminal equipments, 10 channels, minimum only 3 unmanned aerial vehicle relays can be provided with service for all terminal equipments. The total energy consumption of the unmanned aerial vehicle relay does not change, and as the deployment position of the unmanned aerial vehicle relay candidate increases, the terminal equipment can select to establish connection with the unmanned aerial vehicle relay closer to the terminal equipment. Therefore, the transmission power of the terminal equipment is reduced, and the total energy consumption is reduced, thereby causing a small reduction in the energy consumption of the system.
Fig. 7(e) and 7(f) are comparison curves of the improved particle swarm optimization algorithm with the heuristic algorithm a and the heuristic algorithm b, which are obtained under the condition that the number of the control channels and the number of the relay candidate positions are respectively unchanged when the number N of the terminal devices is 25 under the simulation condition.
As can be seen from fig. 7(e), the relationship between the total energy consumption of the system and the number of relay candidate positions is compared with the solution of different algorithms. With the increase of the number of relay candidate positions, the fluctuation range of the total energy consumption curve of the system is small and is in a gentle trend. Under the unchangeable condition of the number of users and channel number, increase the number of unmanned aerial vehicle relay candidate position, it is little to the actual deployment number of unmanned aerial vehicle influence, can not make and deploy unmanned aerial vehicle figure and reduce. It can be known through comparison that the improved particle swarm optimization is still superior to the heuristic optimization in the solving result, and the solving result of the heuristic optimization a is superior to the solving result of the heuristic optimization b.
As can be seen from fig. 7(f), the total energy consumption of the system is related to the number of channels, and the solution of different algorithms is compared. As the number of channels increases, the total energy consumption of the system tends to decrease. Under the condition that the number of users and the number of relay candidate positions are not changed, the number of channels is increased, the number of users of each unmanned aerial vehicle relay service can be increased, and therefore fewer unmanned aerial vehicles are deployed to serve all users; meanwhile, the increase of the number of channels can reduce the number of users multiplexing the same channel, reduce the extra power generated by resisting interference of the users and further reduce the total energy consumption of the system. The total energy consumption of the improved particle swarm algorithm for solving the system is smaller than that of a heuristic algorithm result, and is closer to a suboptimal solution; the result of the heuristic algorithm a for obtaining the total energy consumption of the system is smaller than that of the heuristic algorithm b, and therefore the unmanned aerial vehicle with the minimum path loss is preferentially selected, so that the overall energy saving of the IoT system is facilitated.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. The unmanned aerial vehicle relay deployment method based on the particle swarm optimization algorithm is characterized by comprising the following steps of: designing and defining a channel model of the unmanned aerial vehicle relay, and calculating large-scale fading path loss and small-scale fading to obtain channel gain and signal-to-noise ratio of different devices during connection; designing and defining an energy consumption model of the unmanned aerial vehicle relay, and defining the power consumption composition of the unmanned aerial vehicle in a hovering state; constructing an optimization target, and converting a constrained mixed 01 integer nonlinear programming problem into an unconstrained optimization problem; and optimizing the transmitting power of the terminal equipment, the relay candidate deployment position of the unmanned aerial vehicle and the association relation of the terminal equipment, the relay of the unmanned aerial vehicle and a channel by combining the improved particle swarm algorithm so as to minimize the total energy consumption of the system.
2. The particle swarm optimization algorithm-based unmanned aerial vehicle relay deployment method according to claim 1, wherein the particle swarm optimization algorithm-based unmanned aerial vehicle relay deployment method meets the QoS of all uplink terminal devices; performing joint optimization on the uplink transmitting power of the terminal equipment and the incidence relation between the terminal equipment and the unmanned aerial vehicle relay channel, and determining the number of deployed unmanned aerial vehicle relays and the optimal deployment position; and solving the constructed mixed 01 integer nonlinear programming problem by an improved particle swarm algorithm to obtain an approximate minimum value of the overall total energy consumption of the system, thereby realizing energy-saving deployment of the Internet of things system.
3. The particle swarm optimization algorithm-based unmanned aerial vehicle relay deployment method of claim 1, wherein the particle swarm optimization algorithm-based unmanned aerial vehicle relay deployment method further comprises:
(1) designing a channel model of the unmanned aerial vehicle relay;
(2) designing an energy consumption model of the unmanned aerial vehicle relay;
(3) constructing an optimization target, aiming at minimizing the total energy consumption of the terminal equipment and the unmanned aerial vehicle relay in the IoT system, minimizing the total energy consumption of the unmanned aerial vehicle relay, namely minimizing the deployment number of the unmanned aerial vehicle relay, and selecting the unmanned aerial vehicle relay deployment as few as possible from a plurality of candidate deployment positions on the premise of meeting the service quality; the minimization of the total energy consumption of the terminal equipment can be realized by minimizing the transmitting power of the terminal equipment on the basis of stable communication between the terminal equipment and the unmanned aerial vehicle relay;
(4) introducing a penalty function to reduce the solving complexity, converting the constrained optimization problem into the unconstrained optimization problem by introducing the penalty function, and defining the objective function f as EtotalAnd a penalty function, the formula being:
f＝Etotal+kp*penafty；
Figure FDA0002579244990000021
in the above formula kpDetermining the magnitude of punishment strength by setting punishment coefficients with different magnitudes as punishment coefficients, wherein the punishment is a punishment function and is converted from the punishment coefficients through limiting conditions;
(5) and improving the particle swarm algorithm for solving.
4. The particle swarm optimization algorithm-based unmanned aerial vehicle relay deployment method of claim 3, wherein the designing the channel model of the unmanned aerial vehicle relay comprises:
1) calculating the large-scale fading path loss pathloss of the unmanned aerial vehicle relay:
Figure FDA0002579244990000022
Figure FDA0002579244990000023
Figure FDA0002579244990000024
wherein f iskIs the frequency of channel k, dn，UIs the distance between the terminal equipment n and the unmanned aerial vehicle U, c is the speed of light in vacuum, ηLoSAnd ηNLoSIs an additional attenuation coefficient when LoS and NLoS are connected, and if the antenna of the unmanned aerial vehicle and the antenna of the terminal equipment are vertically arranged, the additional attenuation coefficient is
Figure FDA0002579244990000025
Is the probability of LoS connection, a and b are constants determined by the environment, θ is the elevation angle from the terminal device to the drone relay, and is expressed as θ ═ arcsin (Ht/Lt), where Ht is the altitude of the drone, Lt is the euclidean distance from the drone to the terminal device, PRNLoSIs the probability of NLoS connection, the probability of NLoS connection is PRNLoS＝1-PRLoS；
2) According to the channel model, the channel gain on channel k of terminal device n associated to drone relay U can be expressed as:
Figure FDA0002579244990000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002579244990000032
subject to the Nakagami-m distribution,
Figure FDA0002579244990000033
obeying the distribution with the parameter m, the channels have mutual difference, and the channel gain from the terminal equipment n to the unmanned aerial vehicle relay
Figure FDA0002579244990000034
Is equal to
Figure FDA0002579244990000035
3) Signal-to-interference-and-noise ratio SINR gamma from terminal equipment n to unmanned aerial vehicle relay m through channel kUAVComprises the following steps:
Figure FDA0002579244990000036
wherein the content of the first and second substances,
Figure FDA0002579244990000037
relaying, for the drone, accumulated interference received on channel k from other terminal devices, the accumulated interference originating from users associated to other drones through channel k;
Figure FDA0002579244990000038
2variance of additive white Gaussian noise AWGN as zero mean, defining gammathSINR threshold value required for normal communication between the terminal user and the unmanned aerial vehicle relay, namely the QoS requirement, when gamma isUAV＞γthIn time, the terminal device may establish communication with the drone relay.
5. The unmanned aerial vehicle relay deployment method based on particle swarm optimization algorithm of claim 3, wherein the designing of the energy consumption model of the unmanned aerial vehicle relay comprises:
1) the propulsion power consumption under the hovering state of the multi-rotor unmanned aerial vehicle is expressed as:
Ph＝P0+Pi；
wherein, P0，PiAnd the time is two constants which respectively represent the rotating power and the induced power of the blade in the hovering state. Assuming that each terminal device and the unmanned aerial vehicle relay pass through and establish a communication link only through 1 channel, the communication power of the unmanned aerial vehicle relay is PitemIf n terminal devices are connected with the unmanned aerial vehicle relay, the communication power of the unmanned aerial vehicle relay is n PitemAnd is marked as Pc;
2) the main energy consumption of the unmanned aerial vehicle relay is derived from the propulsion energy consumption, and the total power consumption of the unmanned aerial vehicle relay is represented as:
PUAV＝Ph+Pc；
3) the total energy consumption of the Internet of things system consists of the energy consumption of terminal equipment and the relay energy consumption of an unmanned aerial vehicle:
Etotal＝ωEuser+(1-ω)EUAV；
wherein, omega is a weight coefficient for balancing the energy consumption ratio of the terminal equipment and the unmanned aerial vehicle relay, EuserIndicating the total energy consumption of the terminal equipment within the length of the T time slot, EUAVIndicating total unmanned aerial vehicle relay energy consumption within T slot length, EtotalIs the total energy consumption of the system.
6. The particle swarm optimization algorithm-based unmanned aerial vehicle relay deployment method of claim 3, wherein the constructing an optimization objective comprises:
1) using e to represent a binary three-dimensional matrix of size M x K x N, each element is as follows:
Figure FDA0002579244990000041
2) whether the unmanned aerial vehicle relay is deployed at the candidate position is represented by a binary vector pi with the size of 1 × M, wherein each element is as follows:
Figure FDA0002579244990000042
3) the total energy consumption of the equipment is obtained as follows:
Figure FDA0002579244990000043
4) the total energy consumption of the UAV relay node is as follows:
Figure FDA0002579244990000044
t is the data transmission period, PUAV、PuserPower of the drone and the user, respectively;
5) converting the energy-saving relay strategy problem into a solution of the IoT system minimum total energy consumption problem when the QoS is satisfied:
minimize Etotal＝ωEuser+(1-ω)EUAV；
s.t.：
Figure FDA0002579244990000045
Figure FDA0002579244990000046
Figure FDA0002579244990000047
Figure FDA0002579244990000048
Figure FDA0002579244990000051
Figure FDA0002579244990000052
here, ω is a weight constant for balancing the energy consumption ratio of the terminal device and the drone relay, (1) indicates that the transmission power of each terminal device may not exceed the maximum transmission power; (2) indicating that the terminal device to drone relay communication must meet its own QoS requirements; (3) indicating that the terminal equipment of each unmanned aerial vehicle relay service cannot exceed the total channel number K; (4) each terminal device is in relay connection with the unmanned aerial vehicle through 1 channel and only 1 channel; (5) indicating that the drone relay cannot be connected if not activated; (6) indicating that the number of active drone relays must be able to serve all terminal devices.
7. The particle swarm optimization algorithm-based unmanned aerial vehicle relay deployment method of claim 3, wherein the improved particle swarm optimization algorithm solution comprises:
1) initializing all particles, and dividing 1 particle into 3 sub-particles according to different variable types corresponding to different dimensions to obtain the position x1 of each sub-particle; x 2; x3 and velocity v 1; v 2; v 3;
2) calculating a fitness value F (i) for each particle;
3) for each particle, comparing the fitness value F (i) with the individual extreme value Pbest (i), and if F (i) < Pbest (i), making Pbest (i) equal to F (i); for each particle, comparing the fitness value F (i) with a global extreme value Gtest, and if F (i) < Gtest, making Gtest equal to F (i);
4) updating the position and the speed of each sub-particle according to an updating formula;
5) exit if maximum number of cycles is reached, otherwise return to 2).
8. An unmanned aerial vehicle relay deployment system implementing the unmanned aerial vehicle relay deployment method based on the particle swarm optimization algorithm according to any one of claims 1 to 7, wherein the unmanned aerial vehicle relay deployment system comprises:
the service quality setting module is used for meeting the service quality of all uplink terminal equipment;
the unmanned aerial vehicle relay number and deployment position determining module is used for carrying out combined optimization on the uplink transmitting power of the terminal equipment and the incidence relation between the terminal equipment and the unmanned aerial vehicle relay channel, and determining the number and the optimal deployment position of the unmanned aerial vehicle relays;
and the energy-saving deployment module of the Internet of things system is used for solving the constructed mixed 01 nonlinear programming problem through an improved particle swarm algorithm to obtain an approximate minimum value of the overall total energy consumption of the system, so that the energy-saving deployment of the Internet of things system is realized.
9. An internet of things communication terminal, characterized in that, the internet of things communication terminal carries on the unmanned aerial vehicle relay deployment system of claim 8.
10. A drone characterized in that it is equipped with the drone relay deployment system of claim 8.
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CN114374981B (en) * 2022-01-12 2024-02-20 深圳泓越信息科技有限公司 Energy-saving on-demand pre-deployment method of communication unmanned aerial vehicle

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