CN116170776A - Unmanned aerial vehicle wireless energy supply air calculation auxiliary Internet of things data acquisition method - Google Patents

Unmanned aerial vehicle wireless energy supply air calculation auxiliary Internet of things data acquisition method Download PDF

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CN116170776A
CN116170776A CN202310173207.8A CN202310173207A CN116170776A CN 116170776 A CN116170776 A CN 116170776A CN 202310173207 A CN202310173207 A CN 202310173207A CN 116170776 A CN116170776 A CN 116170776A
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李亦卿
姜淼
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Guangdong University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/20—Circuit arrangements or systems for wireless supply or distribution of electric power using microwaves or radio frequency waves
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04W4/30—Services specially adapted for particular environments, situations or purposes
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses an unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method, which comprises the following steps: s1: establishing a three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station, and establishing an optimization target of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station by jointly optimizing the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power; s2: establishing a resource allocation optimization model P1; s3: establishing an unmanned aerial vehicle track optimization model P2; s4: alternately solving the resource allocation optimization model P1, the unmanned aerial vehicle track optimization model P2 and the optimization targets of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station to obtain the flight track, wireless energy supply time slot allocation and sensor emission power of the unmanned aerial vehicle when the uplink data acquisition rate of the three-dimensional coordinate internet of things transmission system is maximized; according to the invention, the cruising ability of the Internet of things equipment is improved, so that data collection is simple, and the transmission rate is improved.

Description

Unmanned aerial vehicle wireless energy supply air calculation auxiliary Internet of things data acquisition method
Technical Field
The invention relates to the field of wireless energy supply internet of things communication transmission, in particular to an unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method.
Background
In recent years, under the large background of interconnection and interworking of everything, the development of physical network technology has been widely focused by researchers at home and abroad, and the number of various types of sensor devices is also greatly increased, which puts higher demands on the data acquisition work of the internet of things devices. In consideration of the fact that certain Internet of things equipment is deployed in remote areas or dangerous areas with severe environments, the data acquisition of the Internet of things equipment is performed manually, so that the efficiency is low, and the detection is not facilitated. In order to overcome the bottleneck of communication data transmission, the appearance of unmanned aerial vehicle auxiliary communication technology brings new transfer machine for the flexible deployment of large-scale internet of things equipment. Compared with the traditional fixed-position base station deployment, the high-efficiency transmission of the Internet of things equipment can be realized in various complex scenes such as deserts by dynamically optimizing and adjusting the flight track of the unmanned aerial vehicle.
In addition, most of internet of things devices on the market are small in volume and poor in cruising ability, so that long-distance uninterrupted communication cannot be achieved. The unmanned aerial vehicle with high maneuverability and reliability is used as an energy supply party to supply energy to the ground sensor, so that the endurance time of the ground sensor node can be effectively improved, and the large-area deployment of the sensor equipment is further promoted.
Because there is often a double near-far effect (double near-far effect) in the wireless downlink energy supply communication scenario, that is, the device farther from the base station causes the decrease of its uplink transmission rate due to the too little received energy, so that a serious unfair phenomenon occurs in the system. The unmanned aerial vehicle technology is adopted to provide a line-of-sight (line-of-sight) channel between the base station and the receiving Internet of things equipment, and the transmission distance between the base station and the receiving equipment can be reduced by adjusting the flight track of the unmanned aerial vehicle in real time, so that the energy and data transmission efficiency can be improved in a more flexible mode.
The core idea of over-the-air computing is to exploit the waveform superposition characteristics of wireless multiple access channels to achieve efficient data aggregation for concurrent transmissions by multiple devices. Compared with the traditional scheme of transmission before calculation, the air calculation scheme has higher frequency spectrum utilization rate, and the access time delay of the air calculation scheme cannot be greatly increased along with the increase of the scale of the sensing network. Considering that the number of future ground sensor devices will increase significantly, the idea of air calculation is adopted to collect data from all ground sensors.
The prior art proposes a wireless communication method for transmitting data in downlink and uplink, and minimizes data aggregation errors by jointly optimizing three variables, namely energy beam forming, data aggregation beam forming and power control. However, the scheme does not consider that the unmanned plane technology is used for dynamically adjusting the transmission path of the receiving and transmitting end, so that the data transmission rate of the edge equipment is low; the utility model also provides an unmanned aerial vehicle auxiliary wireless energy transmission communication mode, the maximization of uplink data transmission rate is realized through jointly optimizing unmanned aerial vehicle flight orbit and time slot distribution. However, this scheme does not contemplate the use of an over-the-air scheme that can effectively reduce potential transmission delays.
Disclosure of Invention
The invention provides an unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method, which aims to solve the problems of poor cruising ability, difficult data collection and low transmission rate of internet of things equipment in the prior art, improves the cruising ability of the internet of things equipment, simplifies data collection and improves the transmission rate;
in order to solve the technical problems, the invention adopts the following technical scheme:
an unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method comprises the following steps:
s1: establishing a three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station, and establishing an optimization target of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station by jointly optimizing the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power;
s2: establishing a resource allocation optimization model P1 for carrying out joint optimization on wireless energy supply time slot allocation and sensor transmission power;
s3: establishing an unmanned aerial vehicle track optimization model P2 for optimizing the unmanned aerial vehicle flight track of the three-dimensional coordinate Internet of things system;
s4: and (3) alternately solving the resource allocation optimization model P1, the unmanned aerial vehicle track optimization model P2 and the optimization targets of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station to obtain the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power when the uplink data acquisition rate of the three-dimensional coordinate internet of things transmission system is maximized.
The working principle of the invention is as follows:
according to the method, the constructed resource allocation optimization model P1, the unmanned aerial vehicle track optimization model P2 and the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station are solved alternately, so that the flight track, wireless energy supply time slot allocation and sensor transmitting power of the unmanned aerial vehicle are obtained when the uplink data acquisition rate of the three-dimensional coordinate internet of things transmission system is maximized.
Preferably, the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station comprises an unmanned aerial vehicle base station and K ground sensor nodes; wherein K is an integer; the unmanned aerial vehicle supplies power to K sensors on the ground in a wireless energy transmission mode, and data are collected from all the sensors on the ground in a data aggregation mode.
Further, when a three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station is established, initializing and setting parameters;
the initialization setting method is as follows: the position coordinate of the kth sensor is x k ＝[x k ,y k ,0] T Where K e k= {1, …, K }; the transmitting power of the unmanned aerial vehicle is P U The method comprises the steps of carrying out a first treatment on the surface of the The flying height of the unmanned plane is H; the unmanned time for completing one flight task is T; dividing T into N time slots, each time slot having a length of
Figure BDA0004099926390000031
The position coordinates of the unmanned plane in each time slot are x [ n ]]＝[x[n],y[n],H] T N∈n= = {1, …, N }; each flight slot of the drone is divided into two sub-slots, including a first sub-slot length τ for energy transmission of the downlink 0 [n]δ t Second sub-slot length τ for data collection of uplink 1 [n]δ t The method comprises the steps of carrying out a first treatment on the surface of the The flying speed of the unmanned plane is not more than V max 。
Further, the method for optimizing the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station comprises the following steps:
when the nth time slot is carried out, a line-of-sight channel fading model is established according to the air-to-ground wireless channel model and the geographic positions of the sensor nodes and the unmanned aerial vehicle;
the line-of-sight channel fading model is as follows:
Figure BDA0004099926390000032
wherein ,β0 Representing the channel gain when the ground sensor is spaced 1m from the drone;
at the τ 0 [n]δ t When the first sub-slot length is the same, the unmanned aerial vehicle transmits energy to the ground sensor of the downlink, and then the energy collected by the kth sensor in the first sub-slot is as follows:
E k [n]＝ητ 0 [n]δ t P U h k [n]
wherein η ε (0, 1) represents the energy receiving efficiency of each ground sensor;
at the τ 1 [n]δ t When the second sub-time slot length is the same, all the ground sensors transmit data to the unmanned aerial vehicle of the uplink, and the data acquisition rate of the unmanned aerial vehicle in the second sub-time slot is as follows:
Figure BDA0004099926390000033
wherein ,[a]+ Represents max (a, 0); p (P) k [n]Representing the transmit power of the kth sensor for uplink data transmission in the nth time slot; sigma (sigma) 2 Representing the noise power received by the unmanned aerial vehicle;
to meet sustainable data acquisition, the power of each ground sensor meets the transmit power constraints as follows:
Figure BDA0004099926390000041
unmanned aerial vehicle mobility constraints are as follows:
||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}
wherein ,δd ＝ max δ t ；
Order the
Figure BDA0004099926390000042
Representing unmanned trajectory->
Figure BDA0004099926390000043
and
Figure BDA0004099926390000044
Indicating wireless power slot allocation, ">
Figure BDA0004099926390000045
Representing the transmit power of the kth sensor; the method comprises the steps that an objective function of an optimization target of a three-dimensional coordinate internet of things transmission system of an unmanned aerial vehicle base station is a first objective function, and the first objective function is expressed as:
Figure BDA0004099926390000046
the constraints of the first objective function include:
A1.
Figure BDA0004099926390000047
indicating that the total energy emitted by the ground sensor is not higher than the total energy received from the drone.
A2.||x[n+1]-x[n]||≤V max δ t ,n∈N - Indicating that the flight speed of the unmanned aerial vehicle is not higher than the maximum flight speed of the unmanned aerial vehicle.
A3.τ 0 [n]+τ 1 [n]And less than or equal to 1 indicates that the sum of sub-slot allocation coefficients in each time slot is not higher than 1.
A4.
Figure BDA0004099926390000048
Indicating that each slot allocation coefficient is not lower than 0.
A5.
Figure BDA0004099926390000049
Indicating that the data transmission power of each sensor for uplink is not lower than 0.
Further, the objective function of the resource allocation optimization model P1 is a second objective function, and the second objective function is expressed as:
Figure BDA00040999263900000410
constraints of the second objective function include:
B1.
Figure BDA0004099926390000051
B2.τ 0 [n]+τ 1 [n]≤1。
B3.
Figure BDA0004099926390000052
B4.
Figure BDA0004099926390000053
still further, for the second objective function,introducing a first relaxation variable gamma [ n ]]And order
Figure BDA0004099926390000054
Converting the second objective function into a convex perspective function while introducing a convex constraint
Figure BDA0004099926390000055
Subsequently let->
Figure BDA0004099926390000056
Figure BDA0004099926390000057
Re-expressed as:
Figure BDA0004099926390000058
Obtaining a third objective function, wherein the third objective function is expressed as:
Figure BDA0004099926390000059
the constraints of the third objective function include:
C1.
Figure BDA00040999263900000510
C2.
Figure BDA00040999263900000511
C3.
Figure BDA00040999263900000512
C4.
Figure BDA00040999263900000513
C5.
Figure BDA00040999263900000514
C6.
Figure BDA00040999263900000515
wherein ,
Figure BDA00040999263900000516
further, the objective function of the unmanned aerial vehicle trajectory optimization model P2 is a fourth objective function, and the fourth objective function is expressed as:
Figure BDA00040999263900000517
the constraint conditions of the fourth objective function include:
D1.
Figure BDA00040999263900000518
D2.||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}。
further, for the fourth objective function, a second relaxation variable is introduced
Figure BDA0004099926390000061
And h is developed by adopting first-order Taylor k [n]Expressed as:
Figure BDA0004099926390000062
wherein ,x(t) [n]Represents the t th iteration x [ n ]]Is the optimum value of (2); obtaining a fifth objective function, wherein the fifth objective function is expressed as:
Figure BDA0004099926390000063
the constraint of the fifth objective function includes:
E1.
Figure BDA0004099926390000064
E2.
Figure BDA0004099926390000065
E3.
Figure BDA0004099926390000066
E4.||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}。
wherein ,
Figure BDA0004099926390000067
further, the step of alternately solving in S4 is as follows:
s41: initializing and setting flight track of unmanned aerial vehicle base station as X (0) Wireless power slot allocation as T 0 (0) and T1 (0) The sensor has a transmitting power P (0) And calculates an initial uplink data acquisition rate R (0) The error threshold η is 10 -3 Alternating iteration number i=0;
s42: flight trajectory X of unmanned aerial vehicle base station is set (i) Substituting the obtained value into a resource allocation optimization model P1, and solving by adopting a CVX solver to obtain an optimal solution T of wireless energy supply time slot allocation and sensor transmitting power in the ith iteration 0 (i+1) 、T 1 (i+1) and Pk (i+1) ；
S43: the optimal solution T obtained in S42 0 (i+1) 、T 1 (i+1) and Pk (i+1) Flying trace X (i) Substituting the optimal solution X into the unmanned aerial vehicle flight trajectory optimization model P2 to obtain the optimal solution X of the unmanned aerial vehicle flight trajectory in the ith iteration (i+1) And calculates an uplink data acquisition rate R (i+1) ；
S44: if |R (i+1) -R (i) The I is less than or equal to eta, and the optimal unmanned plane flight track X is obtained (i+1) Time slot allocation T 0 (i+1) and T1 (i +1) Sensor transmit power P k (i+1) The method comprises the steps of carrying out a first treatment on the surface of the If not, i=i+1 and S42 and S43 are repeated.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the method as described when said computer program is executed.
Compared with the prior art, the invention has the beneficial effects that:
1. the unmanned aerial vehicle is used for wirelessly supplying energy to the sensor with shorter duration, so that the duration of the sensor is improved.
2. The flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power are obtained through alternate solving when the data acquisition rate is maximized, so that the data collection process is simplified, and the data acquisition rate of the Internet of things is effectively improved.
Drawings
Fig. 1 is a flowchart of an unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method.
Fig. 2 is a schematic diagram of energy transmission and data acquisition of a sensor by an unmanned aerial vehicle.
Fig. 3 is a diagram of the positional relationship between the sensor and the unmanned aerial vehicle.
Fig. 4 is a diagram of a three-dimensional flight trajectory of an unmanned aerial vehicle versus wireless energy supply time slot allocation.
Fig. 5 is a graph comparing energy collection and consumption of different sensors under different flight trajectory schemes of the unmanned aerial vehicle.
FIG. 6 is a graph of the relationship between the sum of system data acquisition rates of the unmanned aerial vehicle at different maximum flight rate comparisons as a function of the number of iterations of the algorithm.
Fig. 7 is a graph of the sum of system data acquisition rates of the unmanned aerial vehicle under different flight trajectory schemes as a function of different transmit powers of the unmanned aerial vehicle.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
Example 1
In this embodiment, as shown in fig. 1, a method for acquiring data of an auxiliary internet of things by wireless energy supply and air calculation of an unmanned aerial vehicle includes the steps of:
s1: establishing a three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station, and establishing an optimization target of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station by jointly optimizing the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power;
s2: establishing a resource allocation optimization model P1 for carrying out joint optimization on wireless energy supply time slot allocation and sensor transmission power;
s3: establishing an unmanned aerial vehicle track optimization model P2 for optimizing the unmanned aerial vehicle flight track of the three-dimensional coordinate Internet of things system;
s4: and (3) alternately solving the resource allocation optimization model P1, the unmanned aerial vehicle track optimization model P2 and the optimization targets of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station to obtain the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power when the uplink data acquisition rate of the three-dimensional coordinate internet of things transmission system is maximized.
Example 2
In this embodiment, a method for acquiring data of an auxiliary internet of things by wireless energy supply of an unmanned aerial vehicle in air calculation includes the steps:
s1: establishing a three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station, and establishing an optimization target of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station by jointly optimizing the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power;
s2: establishing a resource allocation optimization model P1 for carrying out joint optimization on wireless energy supply time slot allocation and sensor transmission power;
s3: establishing an unmanned aerial vehicle track optimization model P2 for optimizing the unmanned aerial vehicle flight track of the three-dimensional coordinate Internet of things system;
s4: and (3) alternately solving the resource allocation optimization model P1, the unmanned aerial vehicle track optimization model P2 and the optimization targets of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station to obtain the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power when the uplink data acquisition rate of the three-dimensional coordinate internet of things transmission system is maximized.
In this embodiment, as shown in fig. 2, the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station includes an unmanned aerial vehicle base station and K ground sensor nodes; wherein K is an integer; the unmanned aerial vehicle supplies power to K sensors on the ground in a wireless energy transmission mode, and data are collected from all the sensors on the ground in a data aggregation mode.
More specifically, when a three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station is established, parameters are initialized.
The initialization setting method is as follows: the position coordinate of the kth sensor is x k ＝[x k ,y k ,0] T Where K e k= {1, …, K }; the transmitting power of the unmanned aerial vehicle is P U The method comprises the steps of carrying out a first treatment on the surface of the The flying height of the unmanned plane is H; the unmanned time for completing one flight task is T; dividing T into N time slots, each time slot having a length of
Figure BDA0004099926390000091
The position coordinates of the unmanned plane in each time slot are x [ n ]]＝[x[n],y[n],H] T N∈n= = {1, …, N }; each flight slot of the drone is divided into two sub-slots, including a first sub-slot length τ for energy transmission of the downlink 0 [n]δ t Second sub-slot length τ for data collection of uplink 1 [n]δ t The method comprises the steps of carrying out a first treatment on the surface of the The flying speed of the unmanned plane is not more than V max 。
More specifically, the method for optimizing the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station comprises the following steps:
and in the nth time slot, establishing a line-of-sight channel fading model according to the air-to-ground wireless channel model and the geographic positions of the sensor nodes and the unmanned aerial vehicle.
The line-of-sight channel fading model is as follows:
Figure BDA0004099926390000092
wherein ,β0 The channel gain is shown when the ground sensor is spaced 1m from the drone.
At the τ 0 [n]δ t When the first sub-slot length is the same, the unmanned aerial vehicle transmits energy to the ground sensor of the downlink, and then the energy collected by the kth sensor in the first sub-slot is as follows:
E k [n]＝ητ 0 [n]δ t P U h k [n]
where η ε (0, 1) represents the energy reception efficiency of each ground sensor.
At the τ 1 [n]δ t When the second sub-time slot length is the same, all the ground sensors transmit data to the unmanned aerial vehicle of the uplink, and the data acquisition rate of the unmanned aerial vehicle in the second sub-time slot is as follows:
Figure BDA0004099926390000093
wherein ,[a]+ Represents max (a, 0); p (P) k [n]Representing the transmit power of the kth sensor for uplink data transmission in the nth time slot; sigma (sigma) 2 Representing the noise power received by the drone.
To meet sustainable data acquisition, the power of each ground sensor meets the transmit power constraints as follows:
Figure BDA0004099926390000094
unmanned aerial vehicle mobility constraints are as follows:
||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}
wherein ,δd ＝ max δ t 。
Order the
Figure BDA0004099926390000101
Representing unmanned trajectory->
Figure BDA0004099926390000102
and
Figure BDA0004099926390000103
Indicating wireless power slot allocation, ">
Figure BDA0004099926390000104
Representing the transmit power of the kth sensor; the method comprises the steps that an objective function of an optimization target of a three-dimensional coordinate internet of things transmission system of an unmanned aerial vehicle base station is a first objective function, and the first objective function is expressed as:
Figure BDA0004099926390000105
the constraints of the first objective function include:
A1.
Figure BDA0004099926390000106
indicating that the total energy emitted by the ground sensor is not higher than the total energy received from the drone.
A2.||x[n+1]-x[n]||≤V max δ t ,n∈N - Indicating that the flight speed of the unmanned aerial vehicle is not higher than the maximum flight speed of the unmanned aerial vehicle.
A3.τ 0 [n]+τ 1 [n]And less than or equal to 1 indicates that the sum of sub-slot allocation coefficients in each time slot is not higher than 1.
A4.
Figure BDA0004099926390000107
Indicating that each slot allocation coefficient is not lower than 0.
A5.
Figure BDA0004099926390000108
Indicating that each sensor is not transmitting power for uplink dataBelow 0.
More specifically, the objective function of the resource allocation optimization model P1 is a second objective function, and the second objective function is expressed as:
Figure BDA0004099926390000109
constraints of the second objective function include:
B1.
Figure BDA00040999263900001010
B2.τ 0 [n]+τ 1 [n]≤1。
B3.
Figure BDA00040999263900001011
B4.
Figure BDA00040999263900001012
more specifically, for the second objective function, a first relaxation variable γn is introduced]And order
Figure BDA00040999263900001013
Converting the second objective function into a convex perspective function while introducing a convex constraint
Figure BDA00040999263900001014
Subsequently let->
Figure BDA00040999263900001015
Figure BDA0004099926390000111
Re-expressed as:
Figure BDA00040999263900001113
Obtaining a third objective function, wherein the third objective function is expressed as:
Figure BDA0004099926390000112
the constraints of the third objective function include:
C1.
Figure BDA0004099926390000113
C2.
Figure BDA00040999263900001114
C3.
Figure BDA0004099926390000114
C4.
Figure BDA0004099926390000115
C5.
Figure BDA0004099926390000116
C6.
Figure BDA0004099926390000117
wherein ,
Figure BDA0004099926390000118
more specifically, the objective function of the unmanned aerial vehicle trajectory optimization model P2 is a fourth objective function, where the fourth objective function is expressed as:
Figure BDA0004099926390000119
the constraint conditions of the fourth objective function include:
D1.
Figure BDA00040999263900001110
D2.||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}。
more specifically, for the fourth objective function, a second relaxation variable is introduced
Figure BDA00040999263900001111
And h is developed by adopting first-order Taylor k [n]Expressed as:
Figure BDA00040999263900001112
wherein ,x(t) [n]Represents the t th iteration x [ n ]]Is the optimum value of (2); obtaining a fifth objective function, wherein the fifth objective function is expressed as:
Figure BDA0004099926390000121
the constraint of the fifth objective function includes:
E1.
Figure BDA0004099926390000122
E2.
Figure BDA0004099926390000123
E3.
Figure BDA0004099926390000124
E4.||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}。
wherein ,
Figure BDA0004099926390000125
more specifically, the steps of the alternate solution in S4 are as follows:
s41: initializing and setting flight track of unmanned aerial vehicle base station as X (0) No thing is provided withLine powered time slot allocation as T 0 (0) and T1 (0) The sensor has a transmitting power P (0) And calculates an initial uplink data acquisition rate R (0) The error threshold η is 10 -3 Alternating iteration number i=0;
s42: flight trajectory X of unmanned aerial vehicle base station is set (i) Substituting the obtained value into a resource allocation optimization model P1, and solving by adopting a CVX solver to obtain an optimal solution T of wireless energy supply time slot allocation and sensor transmitting power in the ith iteration 0 (i+1) 、T 1 (i+1) and Pk (i+1) ；
S43: the optimal solution T obtained in S42 0 (i+1) 、T 1 (i+1) and Pk (i+1) Flying trace X (i) Substituting the optimal solution X into the unmanned aerial vehicle flight trajectory optimization model P2 to obtain the optimal solution X of the unmanned aerial vehicle flight trajectory in the ith iteration (i+1) And calculates an uplink data acquisition rate R (i+1) ；
S44: if |R (i+1) -R (i) The I is less than or equal to eta, and the optimal unmanned plane flight track X is obtained (i+1) Time slot allocation T 0 (i+1) and T1 (i +1) Sensor transmit power P k (i+1) The method comprises the steps of carrying out a first treatment on the surface of the If not, i=i+1 and S42 and S43 are repeated.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the method as described when said computer program is executed.
Example 3
In the embodiment, matlab is adopted to simulate the unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method; the simulation parameters were set as follows:
all unmanned aerial vehicles and ground sensor nodes are provided with only one antenna; the number of nodes of the ground sensor is 5; all sensors are uniformly and randomly distributed in a rectangular area of 200m by 200 m; the total flight time of the unmanned aerial vehicle for completing one task is T=20sAnd equally dividing the total time of flight into n=40 slots; the flying height of the unmanned plane is H=50m; the maximum flight rate of the unmanned aerial vehicle is V max =40 m/s; the power gain of a link reference channel between the unmanned aerial vehicle and the ground sensor node is beta 0 -30dBm; the variance of the noise received at the unmanned aerial vehicle is sigma 2 -104dBm; the transmitting power of the unmanned aerial vehicle is P U =40 dBm; the algorithm convergence accuracy is 10 -3 。
In the embodiment, the scheme of hovering, equal-time distribution hovering and geometric center hovering is a position strategy scheme adopted by the existing unmanned aerial vehicle, and cruising is the unmanned aerial vehicle moving flight track calculated by the method.
In this embodiment, as shown in fig. 3 and 4, the position of the sensor is indicated by "Σ", and the number in "Σ" indicates the serial number of the sensor; "hover" means the optimal position for the unmanned aerial vehicle using a static hover strategy; "cruise" means the unmanned aerial vehicle movement flight path obtained by the invention; the initial trajectory represents the optimal position obtained by adopting a static hovering strategy by the unmanned aerial vehicle as the center of a circle,
Figure BDA0004099926390000131
a circle with radius is used as an initial point of alternating optimization of a cruising scheme; when the unmanned aerial vehicle can only hover statically, the optimal hover position is the circle center of the minimum circumcircle of the convex hulls of all the sensor node positions; when the unmanned aerial vehicle is in a flight state, hovering can be carried out at the point B close to the No. 1 and No. 2 sensors and the point A close to the No. 3, no. 4 and No. 5 sensors. With reference to fig. 4, when approaching points a and B, more time is allocated to wireless energy transmission of the downlink, and when being intermediate to points a and B, more time is allocated to air calculation of the uplink, so as to realize balance between energy collection and energy consumption among the sensors; furthermore, the optimal flight trajectory is always located within the convex hull of all sensor positions.
In this embodiment, as shown in FIG. 5, the "isochronous distribution hover" scheme represents a drone hovering in an optimal position and bringing the total time of flight toT is uniformly allocated to the energy transmission of the downlink and the air calculation of the uplink; the "geometric center hover" scheme means that the drone always hovers at the geometric center of all sensor locations: that is to say,
Figure BDA0004099926390000132
because serious long-distance path attenuation can be generated when the unmanned aerial vehicle hovers statically, the three schemes of hovering, equal-time distribution hovering and geometric center hovering have obvious double near-far effects; for example, in the "hover" and "equal time allocation hover" schemes, sensor No. 2 and sensor No. 3, which are furthest from the optimal hover position, collect the least energy, but consume the most energy. After the energy of the sensors No. 2 and No. 3 is exhausted, the system still cannot perform air calculation even if the energy of other sensors remains, so that the energy efficiency is low; in the cruising scheme provided by the invention, the double near-far problem is effectively improved, not only can all sensors consume energy at the same time, but also all sensors can collect more energy to support the air calculation of an uplink; therefore, the optimal design scheme provided by the invention can better realize the efficient data aggregation of the ground large-scale Internet of things equipment.
In the present embodiment, as shown in FIG. 6, the sum of the data acquisition rates is defined as
Figure BDA0004099926390000141
Maximum flight rate V of unmanned aerial vehicle max 10, 20 and 40m/s, respectively. As can be seen from the figure, as the unmanned aerial vehicle flight rate increases, the sum of the system data acquisition rates increases; in addition, the alternating optimization method provided by the invention can converge without more than 5 iterations under different flight rates of the unmanned aerial vehicle, and the iteration times required by algorithm convergence are not obviously increased along with the rate improvement of the unmanned aerial vehicle.
In this embodiment, as shown in fig. 7, as the transmitting power of the unmanned aerial vehicle increases, the sum of data acquisition rates increases, and the performance of the "cruising" scheme provided by the invention is better than that of the other three schemes; when the flight rate of the unmanned aerial vehicle is increased, the performance of the cruising scheme is improved to a certain extent; when the flight rate of the unmanned aerial vehicle is low, the performance of the cruise scheme is similar to that of the hover scheme; when the transmitting power of the unmanned aerial vehicle is low, the calculation rate of the scheme of 'equal-time distribution hovering' is zero, which reflects the importance of resource distribution in a wireless energy supply air calculation system of the unmanned aerial vehicle; and when the transmitting power of the unmanned aerial vehicle is large enough, the performance of the scheme of 'geometric center hover' is better than that of the scheme of 'equal-time distribution hover', and the necessity of unmanned aerial vehicle hover position optimization is verified.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.

Claims (10)

1. The unmanned aerial vehicle wireless energy supply air calculation auxiliary Internet of things data acquisition method is characterized by comprising the following steps:
s1: establishing a three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station, and establishing an optimization target of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station by jointly optimizing the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power;
s2: establishing a resource allocation optimization model P1 for carrying out joint optimization on wireless energy supply time slot allocation and sensor transmission power;
s3: establishing an unmanned aerial vehicle track optimization model P2 for optimizing the unmanned aerial vehicle flight track of the three-dimensional coordinate Internet of things system;
s4: and (3) alternately solving the resource allocation optimization model P1, the unmanned aerial vehicle track optimization model P2 and the optimization targets of the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station to obtain the flight track of the unmanned aerial vehicle, wireless energy supply time slot allocation and sensor transmitting power when the uplink data acquisition rate of the three-dimensional coordinate internet of things transmission system is maximized.
2. The unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method according to claim 1, wherein the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station comprises an unmanned aerial vehicle base station and K ground sensor nodes; wherein K is an integer; the unmanned aerial vehicle supplies power to K sensors on the ground in a wireless energy transmission mode, and data are collected from all the sensors on the ground in a data aggregation mode.
3. The unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method according to claim 2, wherein parameters are initialized when a three-dimensional coordinate internet of things transmission system of an unmanned aerial vehicle base station is established;
the initialization setting method is as follows: the position coordinate of the kth sensor is x k ＝[x k ,y k ,0] T Where K e k= {1, …, K }; the transmitting power of the unmanned aerial vehicle is P U The method comprises the steps of carrying out a first treatment on the surface of the The flying height of the unmanned plane is H; the unmanned time for completing one flight task is T; dividing T into N time slots, each time slot having a length of
Figure FDA0004099926380000011
The position coordinates of the unmanned plane in each time slot are x [ n ]]＝[x[n],y[n],H] T N∈n= = {1, …, N }; each flight slot of the drone is divided into two sub-slots, including a first sub-slot length τ for energy transmission of the downlink 0 [n]δ t Second sub-slot length τ for data collection of uplink 1 [n]δ t The method comprises the steps of carrying out a first treatment on the surface of the The flying speed of the unmanned plane is not more than V max 。
4. The unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method according to claim 3, wherein the method for optimizing the three-dimensional coordinate internet of things transmission system of the unmanned aerial vehicle base station is as follows:
when the nth time slot is carried out, a line-of-sight channel fading model is established according to the air-to-ground wireless channel model and the geographic positions of the sensor nodes and the unmanned aerial vehicle;
the line-of-sight channel fading model is as follows:
Figure FDA0004099926380000021
wherein ,β0 Representing the channel gain when the ground sensor is spaced 1m from the drone;
at the τ 0 [n]δ t When the first sub-slot length is the same, the unmanned aerial vehicle transmits energy to the ground sensor of the downlink, and then the energy collected by the kth sensor in the first sub-slot is as follows:
E k [n]＝ητ 0 [n]δ t P U h k [n]
wherein η ε (0, 1) represents the energy receiving efficiency of each ground sensor;
at the τ 1 [n]δ t When the second sub-time slot length is the same, all the ground sensors transmit data to the unmanned aerial vehicle of the uplink, and the data acquisition rate of the unmanned aerial vehicle in the second sub-time slot is as follows:
Figure FDA0004099926380000022
wherein ,[a]+ Represents max (a, 0); p (P) k [n]Representing the transmit power of the kth sensor for uplink data transmission in the nth time slot; sigma (sigma) 2 Representing the noise power received by the unmanned aerial vehicle;
to meet sustainable data acquisition, the power of each ground sensor meets the transmit power constraints as follows:
Figure FDA0004099926380000023
unmanned aerial vehicle mobility constraints are as follows:
||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}
wherein ,δd ＝V max δ t ；
Order the
Figure FDA0004099926380000024
Representing unmanned trajectory->
Figure FDA0004099926380000025
and
Figure FDA0004099926380000026
Indicating wireless power slot allocation, ">
Figure FDA0004099926380000027
Representing the transmit power of the kth sensor; the method comprises the steps that an objective function of an optimization target of a three-dimensional coordinate internet of things transmission system of an unmanned aerial vehicle base station is a first objective function, and the first objective function is expressed as:
Figure FDA0004099926380000031
the constraints of the first objective function include:
A1.
Figure FDA0004099926380000032
indicating that the total energy emitted by the ground sensor is not higher than the total energy received from the drone;
A2.||x[n+1]-x[n]||≤V max δ t ,n∈N - indicating that the flying speed of the unmanned aerial vehicle is not higher than the maximum flying speed of the unmanned aerial vehicle;
A3.τ 0 [n]+τ 1 [n]less than or equal to 1 indicates that the sum of sub-slot allocation coefficients in each time slot is not higher than 1;
A4.
Figure FDA0004099926380000033
indicating that each time slot allocation coefficient is not lower than 0;
A5.
Figure FDA0004099926380000034
indicating that the data transmission power of each sensor for uplink is not lower than 0.
5. The unmanned aerial vehicle wireless energy supply air computing auxiliary internet of things data acquisition method according to claim 4, wherein an objective function of the resource allocation optimization model P1 is a second objective function, and the second objective function is expressed as:
Figure FDA0004099926380000035
constraints of the second objective function include:
B1.
Figure FDA0004099926380000036
B2.τ 0 [n]+τ 1 [n]≤1；
B3.
Figure FDA0004099926380000037
B4.
Figure FDA0004099926380000038
6. the unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method according to claim 5, wherein a first relaxation variable gamma [ n ] is introduced for a second objective function]And order
Figure FDA0004099926380000039
Converting the second objective function into a convex perspective function, asIntroduces a convex constraint->
Figure FDA00040999263800000310
Then order
Figure FDA00040999263800000311
Figure FDA0004099926380000041
Re-expressed as:
Figure FDA0004099926380000042
Obtaining a third objective function, wherein the third objective function is expressed as:
Figure FDA0004099926380000043
the constraints of the third objective function include:
C1.
Figure FDA0004099926380000044
C2.
Figure FDA0004099926380000045
C3.
Figure FDA0004099926380000046
C4.
Figure FDA0004099926380000047
C5.
Figure FDA0004099926380000048
C6.
Figure FDA0004099926380000049
wherein ,
Figure FDA00040999263800000410
7. the unmanned aerial vehicle wireless energy supply air computing auxiliary internet of things data acquisition method according to claim 6, wherein an objective function of the unmanned aerial vehicle track optimization model P2 is a fourth objective function, and the fourth objective function is expressed as:
Figure FDA00040999263800000411
the constraint conditions of the fourth objective function include:
D1.
Figure FDA00040999263800000412
D2.||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}。
8. the unmanned aerial vehicle wireless energy supply air calculation auxiliary internet of things data acquisition method according to claim 7, wherein a second relaxation variable is introduced for a fourth objective function
Figure FDA00040999263800000413
And h is developed by adopting first-order Taylor k [n]Expressed as:
Figure FDA00040999263800000414
wherein ,x(t) [n]Represents the t th iteration x [ n ]]Is the optimum value of (2); obtaining a fifth objective function, wherein the fifth objective function is expressed as:
Figure FDA0004099926380000051
the constraint of the fifth objective function includes:
E1.
Figure FDA0004099926380000052
E2.
Figure FDA0004099926380000053
E3.
Figure FDA0004099926380000054
E4.||x[n+1]-x[n]||≤δ d ,n∈N - ＝{1,…,N-1}；
wherein ,
Figure FDA0004099926380000055
9. the unmanned aerial vehicle wireless energy supply air computing auxiliary internet of things data acquisition method according to claim 8, wherein the step of alternately solving in the step S4 is as follows:
s41: initializing and setting flight track of unmanned aerial vehicle base station as X (0) Wireless power slot allocation as T 0 (0) and T1 (0) The sensor has a transmitting power P (0) And calculates an initial uplink data acquisition rate R (0) The error threshold η is 10 -3 Alternating iteration number i=0;
s42: flight trajectory X of unmanned aerial vehicle base station is set (i) Substituting the obtained value into a resource allocation optimization model P1, and solving by adopting a CVX solver to obtain an optimal solution T of wireless energy supply time slot allocation and sensor transmitting power in the ith iteration 0 (i+1) 、T 1 (i+1) and Pk (i+1) ；
S43: the optimal solution T obtained in S42 0 (i+1) 、T 1 (i+1) and Pk (i+1) Flying trace X (i) Substituting the optimal solution X into the unmanned aerial vehicle flight trajectory optimization model P2 to obtain the optimal solution X of the unmanned aerial vehicle flight trajectory in the ith iteration (i+1) And calculates an uplink data acquisition rate R (i+1) ；
S44: if |R (i+1) -R (i) The I is less than or equal to eta, and the optimal unmanned plane flight track X is obtained (i+1) Time slot allocation T 0 (i+1) and T1 (i+1) Sensor transmit power P k (i+1) The method comprises the steps of carrying out a first treatment on the surface of the If not, i=i+1 and S42 and S43 are repeated.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the computer program, performs the steps of the method according to any one of claims 1 to 9.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117440386A (en) * 2023-10-25 2024-01-23 南京邮电大学 Resource optimization method and system for unmanned aerial vehicle auxiliary data transmission network
CN118226888A (en) * 2024-05-22 2024-06-21 南京邮电大学 RSMA-based multi-unmanned aerial vehicle auxiliary data acquisition system optimization method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117440386A (en) * 2023-10-25 2024-01-23 南京邮电大学 Resource optimization method and system for unmanned aerial vehicle auxiliary data transmission network
CN118226888A (en) * 2024-05-22 2024-06-21 南京邮电大学 RSMA-based multi-unmanned aerial vehicle auxiliary data acquisition system optimization method

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