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CN112153593B - Unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method - Google Patents

Unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method Download PDF

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CN112153593B
CN112153593B CN202010997275.2A CN202010997275A CN112153593B CN 112153593 B CN112153593 B CN 112153593B CN 202010997275 A CN202010997275 A CN 202010997275A CN 112153593 B CN112153593 B CN 112153593B
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肖振宇
刘岩铭
刘凯
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Beihang University
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Abstract

The invention discloses an unmanned aerial vehicle-assisted high-energy-efficiency Internet of things data collection method, and belongs to the technical field of air-to-air communication. The method comprises the steps of firstly constructing uplink communication scenes of N unmanned aerial vehicles and M pieces of ground Internet of things equipment, and transmitting data to the N unmanned aerial vehicles by the M pieces of Internet of things equipment. And then, establishing an equipment grouping model and solving to obtain the optimal grouping between the unmanned aerial vehicles and the equipment and the two-dimensional position of each unmanned aerial vehicle. And solving the matching qualification matrix between each equipment group and the channel to obtain the matching matrix and the channel distribution result corresponding to each equipment group. And carrying out signal transmission aiming at the channels distributed by each device, and modeling aiming at the uplink orthogonal frequency division multiple access communication system. The total transmitting power of the equipment and the height of each unmanned aerial vehicle are optimized respectively, and the minimum total transmitting power is obtained through convergence finally, so that the energy conservation of the multi-temporary unmanned aerial vehicle assisted Internet of things data collection is realized. The invention reduces the interference between the co-channel signals and realizes energy-saving green communication.

Description

Unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method
Technical Field
The invention belongs to the technical field of air-to-air communication, and particularly relates to an unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method.
Background
With the continuous development of commercialization of 5G wireless communication, the internet of things technology is applied to more and more fields, such as agricultural production, environmental monitoring and the like, and the internet of things technology is gradually improving the quality of life of people through automation and intellectualization.
The internet of things equipment is usually powered by a battery, has small volume and can be widely deployed and covered. However, due to power limitations, internet of things devices may not be able to communicate with legacy base stations that are located at a large distance, especially in remote areas, forests, or oceans. The existing general solution is to use satellite communication, but the satellite communication has high cost and high time delay, and the signal attenuation is extremely serious due to the overlong distance.
The air-to-air communication can overcome the defects of the traditional ground base station and the satellite, and becomes a hot research hotspot of all countries in the world. On one hand, compared with a satellite, the air traffic aircraft has the characteristics of low cost and low flying height, and can obviously improve the signal transmission rate; on the other hand, compared with a ground base station, the air craft has high mobility and flexibility, and is easier to obtain a channel with dominant visual range, so that wide, flexible and rapid coverage and deployment can be realized. Meanwhile, the air-bound aircraft is widely applied to the fields of military affairs, aerial photography, monitoring, disaster early warning and the like. Multiple aircraft can further cover a wider area than a single aircraft. Therefore, the method for collecting the data of the ground internet of things equipment by using the air vehicle is an effective and low-cost method.
Although the aircraft in flight offers many advantages as a base station, there are still many technical challenges.
Take a plurality of unmanned aerial vehicles as facing empty base station and communicate with a large amount of thing networking devices on ground as an example, firstly, because thing networking device quantity is very huge usually, consequently need reasonable grouping. Since the path loss increases with increasing distance, a K-means grouping strategy can be utilized. However, this strategy does not take into account the service capacity of the drone and the channel limitations. Secondly, wireless communication resources, especially frequency spectrum, are limited, and when accessing a base station by using an orthogonal frequency division multiple access method, signal interference between different unmanned aerial vehicle-assisted cells needs to be considered, and extremely serious signal interference may be generated by using a fixed channel access method or a random channel access method, which may cause communication interruption. Therefore, dynamic channel allocation by using the matching theory is a promising method;
furthermore, the internet of things equipment is small in size, is usually powered by a battery, and needs to reasonably control the transmitting power of the internet of things equipment, so that the internet of things equipment can work for a long time; meanwhile, due to the high flexibility and three-dimensional properties of the unmanned aerial vehicle, the unmanned aerial vehicle base station needs to be deployed reasonably and quickly to obtain better communication performance. The optimal deployment position can be obtained by using an exhaustive search method, but with the increase of the number of unmanned aerial vehicles and the search precision, the calculation complexity is exponential, which is intolerable; finally, the internet of things devices have an "active" property, that is, in a scene with a large number of internet of things devices, the sensor devices usually have a periodic active state, and only the sensor devices active in one period need to transmit data to the base station of the unmanned aerial vehicle. Therefore, in each updating period in the scene of the internet of things, the unmanned aerial vehicle needs to be grouped again, the channel is redistributed, the power is redistributed and the unmanned aerial vehicle base station is redeployed, and at the moment, the track of twice deployments of each unmanned aerial vehicle before and after updating needs to be determined, so that the energy consumption of the unmanned aerial vehicle movement is saved.
In summary, when an unmanned aerial vehicle serves as a base station-assisted communication network, a reasonable grouping strategy, a channel allocation strategy, a power control strategy and an unmanned aerial vehicle base station three-dimensional deployment strategy are required, and when a network node changes, resource reconfiguration is performed and a trajectory of an unmanned aerial vehicle base station is determined, so that energy-saving transmission data of internet of things equipment and energy-saving scheduling of the unmanned aerial vehicle base station are realized.
Disclosure of Invention
Aiming at the problems, the invention provides an unmanned aerial vehicle-assisted energy-efficient internet of things data collection method, in a scene that multiple unmanned aerial vehicles collect ground internet of things data, energy-saving communication is realized through resource allocation and redistribution and temporary air base station three-dimensional deployment and track optimization, an improved K-means strategy is adopted in temporary air communication, a matching theory, a convex optimization technology and a one-dimensional search method are adopted, and the transmitting power of internet of things equipment is minimized under the condition of meeting the signal-to-interference-and-noise ratio threshold constraint through optimization design grouping, channel allocation, unmanned aerial vehicle three-dimensional deployment and power allocation. When the equipment with the active Internet of things changes, resource reconfiguration and unmanned aerial vehicle deployment are carried out timely and quickly, and the track design of unmanned aerial vehicle deployment before and after the equipment changes is optimized.
The unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method comprises the following specific steps:
the method comprises the following steps of firstly, constructing a communication scene between multiple unmanned aerial vehicles and multiple ground Internet of things devices;
applicable communication scenarios include: 1. uplink communication between the multiple unmanned aerial vehicles and the large-scale ground Internet of things equipment; 2. downlink communication between the multiple unmanned aerial vehicles and the large-scale ground Internet of things equipment; 3. the uplink communication between the plurality of ground mobile base stations and the large-scale unmanned aerial vehicle is realized; 4. and the plurality of ground mobile base stations and the large-scale unmanned aerial vehicle perform downlink communication.
Step two, aiming at the uplink communication scenes of N unmanned aerial vehicles and M pieces of ground Internet of things equipment, transmitting data to the N unmanned aerial vehicles by the mum pieces of active Internet of things equipment; establishing an equipment grouping model according to the Euclidean distance;
where μ is the percentage of the number of active devices to the total number of devices, and μ M > N; suppose there is
Figure BDA0002693033630000021
An orthogonal channel. The device group model is represented as:
Figure BDA0002693033630000022
Figure BDA0002693033630000023
Figure BDA0002693033630000024
Figure BDA0002693033630000025
the grouping relationship between the mth device and the nth drone is denoted as am,n∈{0,1}；rm,nRepresenting a two-dimensional distance between the mth device and the nth drone; due to the limitation of a channel, namely service capacity, the number of devices served by each unmanned aerial vehicle is at most omega;
and step three, solving an equipment grouping model based on an improved K-means algorithm to obtain the optimal grouping between the unmanned aerial vehicles and the equipment and the two-dimensional position of each unmanned aerial vehicle.
The specific mode is as follows:
step 301, initializing positions of unmanned aerial vehicles by using a K-means + + algorithm, wherein each unmanned aerial vehicle corresponds to one initialized position;
step 302, aiming at each device, allocating the device to the nearest unmanned aerial vehicles which can serve the device and are less than omega in number to form an initialization packet meeting the capacity limit;
the method specifically comprises the following steps: and selecting the devices one by one, and aiming at the current device A, allocating the nearest unmanned aerial vehicle capable of serving the device A, wherein when the number of the devices served by the nearest unmanned aerial vehicle reaches omega, the device A is changed to allocate the next nearest unmanned aerial vehicle with the number of the service devices smaller than omega.
303, obtaining a grouping relation matrix of each unmanned aerial vehicle and each device according to the initialized grouping of the unmanned aerial vehicles, recalculating an objective function of a device grouping model by using the matrix, and updating the two-dimensional position of the unmanned aerial vehicle;
the element in the grouping relation matrix is am,nThe value is 0 or 1, and a corresponding to each device of each unmanned aerial vehicle servicem,nThe value is 1.
The position updating formula of the unmanned aerial vehicle is as follows:
Figure BDA0002693033630000031
xmindicating the location of the mth device;
and step 304, finding out the devices which are not used as the latest contact devices according to the updated two-dimensional position of each unmanned aerial vehicle.
And 305, performing grouping optimization adjustment. For unmanned plane n1In not with drone n1As a device i which is recently contacted, firstly, a nearest unmanned aerial vehicle n corresponding to the device i is judged2Whether the number of devices served is equal to Ω; if so, the unmanned plane n is switched to the optimal switching method1The nearest drone n in which a device i that is not the nearest contact switches to a device i2(ii) a Otherwise, executing the operation, and enabling the unmanned aerial vehicle n1To drone n by device i not most recently contacted2；
The specific process is as follows:
first, unmanned plane n is selected2Each device j of the service calculates the gain r of the device j after exchanging with the device ij；
The calculation formula is as follows:
Figure BDA0002693033630000032
to unmanned plane n in sequence2All the devices in service respectively calculate the gain corresponding to each device after the device is exchanged with the device i, and when the objective function is reduced, the gain r of the device j is increasedjWhen the switching time is more than 0, the optimal switching equipment is selected
Figure BDA0002693033630000033
To unmanned plane n1。
Step 306, after exchanging or performing operation, updating the equipment in each unmanned aerial vehicle set, and updating the two-dimensional position of the unmanned aerial vehicle; and repeating the steps 303-305 until the objective function value of the grouping model is not reduced any more, and obtaining the updated two-dimensional position of the optimal equipment and the unmanned aerial vehicle in each unmanned aerial vehicle group.
And fourthly, sequentially solving a matching qualification matrix between the equipment group served by each unmanned aerial vehicle and the channel by using a matching theory, and solving each matching problem by using a Hungarian algorithm or an MOSEK tool to obtain a matching matrix corresponding to each equipment group, so as to further obtain a channel allocation result corresponding to each equipment.
The specific mode is as follows:
step 401, according to the updated two-dimensional position of each unmanned aerial vehicle, selecting two unmanned aerial vehicles with the nearest adjacent distance, and defining the equipment group of each service as pi1And pi2Calculating the device groups which are nearest to all the previous device groups and are not allocated with channels one by one;
the calculation formula is as follows:
Figure BDA0002693033630000041
step 402, initiating a group of devices pi1Each device in the network randomly allocates a respective channel, and each device occupies different channels respectively;
step 403, sequentially selecting each device from the current device group, and respectively calculating an interference value between each device and each channel to obtain a matching optimization problem of the current device group;
the initial value of the current equipment group is pi2For channel k has been allocated to the group of devices pi1Device i, device group pi in (1)2The interference value between device j and channel k in (1) is λj,k；
Figure BDA0002693033630000042
Wherein
Figure BDA0002693033630000043
Representing the distributed equipment occupying the channel k in other equipment groups; when in use
Figure BDA0002693033630000044
Having a value ofi,k＝0；wi,jRepresenting the interference coefficient between devices i and j of two shared channels; the calculation formula is as follows:
Figure BDA0002693033630000045
wherein n is1The unmanned plane which represents the nearest contact of the equipment i corresponds to the equipment group with the value of pi1；n2The unmanned plane which represents the nearest contact of the equipment j is in the corresponding equipment group of pi2(ii) a The denominator represents the sum of the squared distances of the associated links and the numerator represents the sum of the squared distances of the interfering links.
Similarly, the interference coefficients of the device j and each of the remaining devices occupying the channel k are obtained to obtain the interference value λ of the device j and the channel kj,kAnd further, the interference values respectively corresponding to each of the other devices and each of the channels are obtained, so that a matching problem can be considered between each device and a channel of the device group served by the current unmanned aerial vehicle n, as follows:
Figure BDA0002693033630000046
Figure BDA0002693033630000047
Figure BDA0002693033630000048
Figure BDA0002693033630000049
wherein the matching matrix S is a 0-1 matrix to be solved. The problem is an integer programming problem and can be solved by using an MOSEK solving tool or a Hungarian algorithm. In the solved matching matrix S, only one of each row is 1, and the rest are 0, S j,k1 indicates that each device occupies a different channel to transmit signals.
Updating
Figure BDA0002693033630000051
Namely, it is
Figure BDA0002693033630000052
Step 404, update
Figure BDA0002693033630000059
And returning to the step 403, selecting the next equipment group as the current equipment group, and sequentially obtaining the matching optimization problem and the matching matrix corresponding to each equipment group, thereby obtaining the final channel allocation result.
And fifthly, carrying out signal transmission aiming at the channels distributed by each device, considering the signal-to-interference-and-noise ratio of the received signal, and modeling aiming at the uplink orthogonal frequency division multiple access communication system.
The model was constructed as follows:
the objective function is to minimize the total transmit power;
the constraint conditions are as follows: the signal-to-interference-and-noise ratio threshold, the transmission power constraint and the unmanned aerial vehicle height constraint are met;
the model expression is as follows:
Figure BDA0002693033630000053
Figure BDA0002693033630000054
Figure BDA0002693033630000055
Figure BDA0002693033630000056
wherein p ismRepresents the transmit power of device m;
Figure BDA0002693033630000057
representing device m contactsThe altitude of drone n; gamma ray0Representing a signal to interference plus noise ratio threshold; sigma2A variance representing additive white gaussian noise;
Figure BDA0002693033630000058
represents the expected channel power of device m and the contacted drone n; pmaxRepresents a maximum value of the transmission power; h isminA minimum value representing the altitude of the drone; h ismaxRepresenting the maximum value of the altitude of the drone.
And step six, based on the idea of alternative iteration, respectively optimizing the total transmitting power of the equipment and the height of each unmanned aerial vehicle, and finally converging to obtain the minimum total transmitting power, so that the energy-efficient Internet of things data collection assisted by the unmanned aerial vehicles is realized.
The specific process is as follows:
firstly, fixing the height of an unmanned aerial vehicle, simplifying the modeling model into a linear programming problem, and solving by utilizing a CVX convex optimization toolkit;
then, fixing the obtained transmitting power to obtain the non-convex problem of the minimized power; the height of each unmanned aerial vehicle is solved in sequence to reduce the dimension of the variable; solving by using a one-dimensional search algorithm, and updating the total transmitting power of the equipment and the height of each unmanned aerial vehicle;
and (4) iteratively performing the steps to obtain the final height of the unmanned aerial vehicle and the power of each device, and realizing the minimization of the total transmitting power.
And step seven, when one part of equipment enters the 'dormant' state and the other part of equipment enters the 'active' state in the periodic time, namely the mu changes, the steps two to six are carried out again at the moment to complete the resource reconfiguration and the unmanned aerial vehicle deployment.
In step 301, the Kmeans + + initialization center is not used, but the horizontal position of the drone obtained before is directly used as the initial center, so that the algorithm complexity is reduced, and the track length of the drone is effectively reduced. In step six, the initial altitude of the drone also utilizes the altitude at which it was last deployed.
Order set L1Representing a previous deploymentN unmanned aerial vehicle sequence numbers, set L2Representing the N position sequence numbers determined for the current redeployment, the optimization problem for each drone movement can be written as:
Figure BDA0002693033630000061
Figure BDA0002693033630000062
Figure BDA0002693033630000063
Figure BDA0002693033630000064
wherein C represents an NxN-sized 0-1 matrix, if C ij1 indicates that drone i will move to location j. EijThe energy consumed by the movement of the unmanned plane i to the position j is shown, and is related to the length of the track. Therefore, the optimization problem of unmanned aerial vehicle movement is an integer programming problem, and is also a typical bilateral matching problem in practice, and can be solved by using an MOSEK solving tool or Hungarian algorithm directly to obtain a matching relation matrix C between the unmanned aerial vehicle and a new deployment position, namely the track of the unmanned aerial vehicle, so that the energy consumption of unmanned aerial vehicle movement is minimum.
The invention has the advantages that:
1. an unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method provides a grouping method considering base station service capacity limitation;
2. an unmanned aerial vehicle-assisted high-energy-efficiency Internet of things data collection method provides a dynamic channel allocation scheme based on a matching theory and a Hungarian algorithm, and effectively reduces interference between co-channel signals;
3. an unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method combines design power control and unmanned aerial vehicle height optimization, and has low complexity;
4. an unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method effectively reduces the total transmitting power of Internet of things equipment and realizes energy-saving green communication.
5. An unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method is used for rapidly reconfiguring resources in a changed scene with active Internet of things equipment, optimizing the track of an unmanned aerial vehicle base station and saving the motion energy consumption of an unmanned aerial vehicle.
Drawings
Fig. 1 is a schematic view of a communication scene of multiple unmanned aerial vehicles and internet of things equipment according to the invention;
fig. 2 is a flow chart of an energy-efficient internet of things data collection method assisted by an unmanned aerial vehicle.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
The invention provides an unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method, which comprises the following steps of firstly, establishing a grouping model based on the horizontal distance between Internet of things equipment and an unmanned aerial vehicle and considering the capacity limit of the unmanned aerial vehicle, and solving by using an improved K-means algorithm: firstly, initializing two-dimensional positions of the unmanned aerial vehicles and initializing grouping, and then utilizing an iterative method to minimize the sum of distances between all the devices and the unmanned aerial vehicles which are in contact with the devices, so as to obtain a final grouping relation and two-dimensional positions of the unmanned aerial vehicles. And updating the two-dimensional positions of the unmanned planes in each iteration and adjusting the grouping according to the new two-dimensional distance. And carrying out channel matching on each adjusted equipment group, constructing an adaptive qualification matrix sharing the interference between the channel equipment, obtaining the distribution relation between the equipment and the channel by utilizing an MOSEK solving tool or Hungarian algorithm, and modeling aiming at the uplink orthogonal frequency division multiple access communication system by considering the signal-to-interference-and-noise ratio of the received signals after the connection and the distribution relation between the unmanned aerial vehicle, the equipment and the channel. And finally, solving the non-convex problem by using the idea of alternating iterative optimization, realizing the minimization of the total transmitting power and achieving the aim of saving energy. And finally, when the active state and the number of the internet of things equipment change, rapidly determining the re-deployment position by adopting the measures, solving the problem of integer planning of the motion energy consumption of the unmanned aerial vehicle, and solving the optimal solution.
As shown in fig. 2, the specific steps are as follows:
the method comprises the following steps of firstly, constructing a communication scene between multiple unmanned aerial vehicles and multiple ground Internet of things devices;
applicable communication scenarios include: 1. uplink communication between the multiple unmanned aerial vehicles and the large-scale ground Internet of things equipment; 2. downlink communication between the multiple unmanned aerial vehicles and the large-scale ground Internet of things equipment; 3. uplink communication between the plurality of ground base stations and the large-scale unmanned aerial vehicle; 4. and downlink communication between the plurality of ground base stations and the large-scale unmanned aerial vehicle.
Step two, aiming at an uplink communication scene of N unmanned aerial vehicles and M pieces of ground Internet of things equipment, at present, mu M pieces of active Internet of things equipment transmit data to the N unmanned aerial vehicles, wherein mu is the percentage of the number of the active equipment to the total equipment, and mu M is larger than N; establishing an equipment grouping model according to the Euclidean distance;
as shown in fig. 1, the internet of things device with known μ M location information transmits data to N drone base stations, and considering a general scenario, all drones share a spectrum resource and share a spectrum resource
Figure BDA0002693033630000071
An orthogonal channel. Therefore, to avoid intra-cell interference, the maximum number of serving devices per drone is Ω. As is clear from the fries transmission equation, the path loss increases with increasing distance, and therefore, it is necessary to perform grouping according to the euclidean distance.
Two-dimensional position of unmanned aerial vehicle
Figure BDA0002693033630000072
The grouping relation between the mth device and the nth drone is shown as a by fixing the geometric mean center of each group of devicesm,nE {0,1}, the two-dimensional distance between the mth device and the nth drone being represented as
Figure BDA0002693033630000073
The problem of groupingCan be expressed as:
Figure BDA0002693033630000074
Figure BDA0002693033630000075
Figure BDA0002693033630000076
Figure BDA0002693033630000077
xmindicating the location of the mth device;
and step three, solving an equipment grouping model based on an improved K-means algorithm to obtain the optimal grouping between the unmanned aerial vehicles and the equipment and the two-dimensional position of each unmanned aerial vehicle.
The specific mode is as follows:
step 301, initializing positions of unmanned aerial vehicles by using a K-means + + algorithm, wherein each unmanned aerial vehicle corresponds to one initialized position;
step 302, aiming at each device, allocating the device to the nearest unmanned aerial vehicles which can serve the device and are less than omega in number to form an initialization packet meeting the capacity limit;
the method specifically comprises the following steps: and selecting the devices one by one, and aiming at the current device A, allocating the nearest unmanned aerial vehicle capable of serving the device A, wherein when the number of the devices served by the nearest unmanned aerial vehicle reaches omega, the device A is changed to allocate the next nearest unmanned aerial vehicle with the number of the service devices smaller than omega.
303, obtaining a grouping relation matrix of each unmanned aerial vehicle and each device according to the initialized grouping of the unmanned aerial vehicles, recalculating an objective function of a device grouping model by using the matrix, and updating the two-dimensional position of the unmanned aerial vehicle;
the element in the grouping relation matrix is am,nValue of 0 or 1And each unmanned aerial vehicle satisfies a corresponding a of each equipment of servicem,nThe value is 1.
The update formula is:
Figure BDA0002693033630000081
and step 304, finding out the devices which are not used as the latest contact devices according to the updated two-dimensional position of each unmanned aerial vehicle.
And 305, performing grouping optimization adjustment. For unmanned plane n1In not with drone n1As a device i which is recently contacted, firstly, a nearest unmanned aerial vehicle n corresponding to the device i is judged2Whether the number of devices served is equal to Ω; if so, the unmanned plane n is switched to the optimal switching method1The nearest drone n in which a device i that is not the nearest contact switches to a device i2(ii) a Otherwise, executing the operation, and enabling the unmanned aerial vehicle n1To drone n by device i not most recently contacted2；
The specific process is as follows:
first, unmanned plane n is selected2Each device j of the service calculates the gain r of the device j after exchanging with the device ij；
The calculation formula is as follows:
Figure BDA0002693033630000082
to unmanned plane n in sequence2All the devices in service respectively calculate the gain corresponding to each device after the device is exchanged with the device i, and when the objective function is reduced, the gain r of the device j is increasedjWhen the switching time is more than 0, the optimal switching equipment is selected
Figure BDA0002693033630000083
To unmanned plane n1。
Step 306, after the exchange or the giving operation, the equipment in each unmanned aerial vehicle set is updated; and repeating the steps 303-305 until the objective function value of the grouping model is not reduced any more, and obtaining the updated two-dimensional position of the optimal equipment and the unmanned aerial vehicle in each unmanned aerial vehicle group.
And fourthly, sequentially solving a matching qualification matrix between the equipment group served by each unmanned aerial vehicle and the channel by using a matching theory, and solving each matching problem by using a Hungarian algorithm or an MOSEK tool to obtain a matching matrix corresponding to each equipment group, so as to further obtain a channel allocation result corresponding to each equipment.
The specific mode is as follows:
step 401, according to the updated two-dimensional position of each unmanned aerial vehicle, selecting two unmanned aerial vehicles with the nearest adjacent distance, and defining the equipment group of each service as pi1And pi2Calculating the device groups which are nearest to all the previous device groups and are not allocated with channels one by one;
the calculation formula is as follows:
Figure BDA0002693033630000091
step 402, initiating a group of devices pi1Each device in the network randomly allocates a respective channel, and each device occupies different channels respectively;
step 403, sequentially selecting each device from the current device group, and respectively calculating an interference value between each device and each channel to obtain a matching optimization problem of the current device group;
the initial value of the current equipment group is pi2For channel k has been allocated to the group of devices pi1Device i, device group pi in (1)2The interference value between device j and channel k in (1) is λj,k；
Figure BDA0002693033630000092
Wherein
Figure BDA0002693033630000093
Representing the distributed equipment occupying the channel k in other equipment groups; when in use
Figure BDA0002693033630000094
Having a value ofi,k＝0；wi,jRepresenting two shared channelsThe interference coefficient between devices i and j; the calculation formula is as follows:
Figure BDA0002693033630000095
wherein n is1The unmanned plane which represents the nearest contact of the equipment i corresponds to the equipment group with the value of pi1；n2The unmanned plane which represents the nearest contact of the equipment j is in the corresponding equipment group of pi2(ii) a The denominator represents the sum of the squared distances of the associated links and the numerator represents the sum of the squared distances of the interfering links.
Similarly, the interference coefficients of the device j and each of the remaining devices occupying the channel k are obtained to obtain the interference value λ of the device j and the channel kj,kAnd further, the interference values respectively corresponding to each of the other devices and each of the channels are obtained, so that a matching problem can be considered between each device and a channel of the device group served by the current unmanned aerial vehicle n, as follows:
Figure BDA0002693033630000096
Figure BDA0002693033630000097
Figure BDA0002693033630000098
Figure BDA0002693033630000099
wherein the matching matrix S is a 0-1 matrix to be solved. The problem is an integer programming problem and can be solved by using an MOSEK solving tool or a Hungarian algorithm. In the solved matching matrix S, only one of each row is 1, and the rest are 0, S j,k1 indicates that each device occupies a different channel to transmit signals.
Updating
Figure BDA0002693033630000101
Namely, it is
Figure BDA0002693033630000102
Step 404, update
Figure BDA0002693033630000103
And returning to the step 403, selecting the next equipment group as the current equipment group, and sequentially obtaining the matching optimization problem and the matching matrix corresponding to each equipment group, thereby obtaining the final channel allocation result.
And fifthly, carrying out signal transmission aiming at the channels distributed by each device, considering the signal-to-interference-and-noise ratio of the received signal, and modeling aiming at the uplink orthogonal frequency division multiple access communication system.
The model was constructed as follows:
the objective function is to minimize the total transmit power;
the constraint conditions are as follows: the signal-to-interference-and-noise ratio threshold, the transmission power constraint and the unmanned aerial vehicle height constraint are met;
the model expression is as follows:
Figure BDA0002693033630000104
Figure BDA0002693033630000105
Figure BDA0002693033630000106
Figure BDA0002693033630000107
wherein p ismRepresenting the transmit power of device m；
Figure BDA0002693033630000108
Representing the altitude of the drone n with which the device m is in contact; gamma ray0Representing a signal to interference plus noise ratio threshold; n is(m)Unmanned plane, σ, representing device m contact2A variance representing additive white gaussian noise;
Figure BDA0002693033630000109
represents the expected channel power of device m and the contacted drone n; pmaxRepresents a maximum value of the transmission power; h isminA minimum value representing the altitude of the drone; h ismaxRepresenting the maximum value of the altitude of the drone.
Figure BDA00026930336300001010
Representing the expected channel power of device m and the associated drone n, calculated as:
Figure BDA00026930336300001011
dm,nrepresenting the three-dimensional Euclidean distance, alpha representing the path loss exponent, etaLoSAnd ηNLoSRepresents an excess path loss coefficient, fcIs the carrier frequency, c is the speed of light.
Considering the general case of a probabilistic line-of-sight channel,
Figure BDA00026930336300001012
the probabilistic model representing the line-of-sight link is:
Figure BDA00026930336300001013
a and b are modeling parameters, thetam,nRepresenting the elevation angle between device m and drone n.
The non-line-of-sight probability is expressed as
Figure BDA00026930336300001014
The channel energy for line-of-sight and non-line-of-sight links may be expressed as:
Figure BDA0002693033630000111
Figure BDA0002693033630000112
Figure BDA0002693033630000113
representing the channel energy of the line-of-sight link;
Figure BDA0002693033630000114
representing the channel energy of the non-line-of-sight link;
the signal to interference plus noise ratio of a device can be expressed as:
Figure BDA0002693033630000115
and step six, based on the idea of alternating iteration, the total transmitting power of the equipment and the height of each unmanned aerial vehicle are optimized by using a convex optimization technology and a one-dimensional search algorithm, and finally, the minimum total transmitting power is obtained through convergence, so that the energy conservation of multi-temporary unmanned-machine-assisted Internet of things data collection is realized.
The invention researches uplink transmission, so that the unmanned aerial vehicle to which the equipment is transmitted needs to be known, namely the grouping problem; which device allocates which channel, i.e. a channel allocation problem; because the internet of things is required to be energy-saving, the height of the unmanned aerial vehicle and the transmitting power of each device are required to be adjusted as much as possible, so that the total transmitting power is the lowest under the condition of meeting the signal-to-interference-and-noise ratio, and the purpose of energy saving is achieved. The method comprises the following specific steps:
firstly, fixing the height of an unmanned aerial vehicle, simplifying the modeling model into a linear programming problem, and solving by utilizing a CVX convex optimization toolkit;
Figure BDA0002693033630000116
Figure BDA0002693033630000117
Figure BDA0002693033630000118
then, fixing the obtained transmitting power
Figure BDA00026930336300001112
The non-convex problem of minimized power is obtained;
Figure BDA0002693033630000119
Figure BDA00026930336300001110
Figure BDA00026930336300001111
for this non-convex problem, the height of each drone is solved in turn to reduce the variable dimension, as follows:
Figure BDA0002693033630000121
Figure BDA0002693033630000122
hmin≤hn≤hmax
for this one-dimensional optimization problem, one-dimensional search algorithms are used, such as yellowSolving the problem by using a golden partition method or a dichotomy method, and updating { p }mH andn；
and (4) iteratively performing the steps to obtain the final height of the unmanned aerial vehicle and the power of each device, and realizing the minimization of the total transmitting power.
And step seven, when one part of equipment enters the 'dormant' state and the other part of equipment enters the 'active' state in the periodic time, namely the mu changes, the steps two to six are carried out again at the moment to complete the resource reconfiguration and the unmanned aerial vehicle deployment.
In step 301, the Kmeans + + initialization center is not used, but the horizontal position of the drone obtained before is directly used as the initial center, so that the algorithm complexity is reduced, and the track length of the drone is effectively reduced. In step six, the initial altitude of the drone also utilizes the altitude at which it was last deployed.
Order set L1Representing N previously deployed drone sequence numbers, set L2Representing the N position sequence numbers determined for the current redeployment, the optimization problem for each drone movement can be written as:
Figure BDA0002693033630000123
Figure BDA0002693033630000124
Figure BDA0002693033630000125
Figure BDA0002693033630000126
wherein C represents an NxN-sized 0-1 matrix, if C ij1 indicates that drone i will move to location j. EijRepresenting the energy consumed by the movement of drone i to position j, and the trajectoryIs related to the length of (c). EijThe calculation is expressed as:
Figure BDA0002693033630000127
wherein DijRepresents the distance from the previous deployment location of drone i to the now updated deployment location j, v represents the velocity of the drone, is a constant value,
Figure BDA0002693033630000128
the horizontal movement energy consumption of the unmanned aerial vehicle is shown, the horizontal movement energy consumption is related to the horizontal effective speed of the unmanned aerial vehicle on the corresponding track,
Figure BDA0002693033630000129
the vertical movement energy consumption of the unmanned aerial vehicle is represented, and the vertical movement energy consumption is related to the vertical effective speed of the unmanned aerial vehicle on the corresponding track. Since the locations of the two deployments are known, so
Figure BDA00026930336300001210
Figure BDA00026930336300001211
And EijCan be easily obtained.
Therefore, the optimization problem of unmanned aerial vehicle movement is an integer programming problem, and is also a typical bilateral matching problem in practice, and can be solved by using an MOSEK solving tool or Hungarian algorithm directly to obtain a matching relation matrix C between the unmanned aerial vehicle and a new deployment position, namely the track of the unmanned aerial vehicle, so that the energy consumption of unmanned aerial vehicle movement is minimum.

Claims (2)

1. An unmanned aerial vehicle-assisted energy-efficient Internet of things data collection method is characterized by comprising the following specific steps:
the method comprises the following steps of firstly, constructing a communication scene between multiple unmanned aerial vehicles and multiple ground Internet of things devices;
step two, aiming at the uplink communication scene of N unmanned aerial vehicles and M pieces of ground Internet of things equipment, transmitting data to the N unmanned aerial vehicles by the mum pieces of active Internet of things equipment, and establishing an equipment grouping model according to the Euclidean distance;
where μ is the percentage of the number of active devices to the total number of devices, and μ M > N; suppose there is
Figure FDA0003101477860000011
A plurality of orthogonal channels; the device group model is represented as:
Figure FDA0003101477860000012
Figure FDA0003101477860000013
Figure FDA0003101477860000014
Figure FDA0003101477860000015
the grouping relationship between the mth device and the nth drone is denoted as am,n∈{0,1}；rm,nRepresenting a two-dimensional distance between the mth device and the nth drone; due to the limitation of a channel, namely service capacity, the number of devices served by each unmanned aerial vehicle is at most omega;
solving an equipment grouping model based on an improved K-means algorithm to obtain an optimal grouping between the unmanned aerial vehicles and the equipment and a two-dimensional position of each unmanned aerial vehicle;
step 301, initializing positions of unmanned aerial vehicles by using a K-means + + algorithm, wherein each unmanned aerial vehicle corresponds to one initialized position;
step 302, aiming at each device, allocating the device to the nearest unmanned aerial vehicles which can serve the device and are less than omega in number to form an initialization packet meeting the capacity limit;
the method specifically comprises the following steps: selecting equipment one by one, aiming at the current equipment A, allocating the equipment A to the nearest unmanned aerial vehicle capable of serving the equipment A, and when the number of the equipment served by the nearest unmanned aerial vehicle reaches omega, allocating the equipment A to the next nearest unmanned aerial vehicle with the number of the service equipment smaller than omega;
303, obtaining a grouping relation matrix of each unmanned aerial vehicle and each device according to the initialized grouping of the unmanned aerial vehicles, recalculating an objective function of a device grouping model by using the matrix, and updating the two-dimensional position of the unmanned aerial vehicle;
the element in the grouping relation matrix is am,nThe value is 0 or 1, and a corresponding to each device of each unmanned aerial vehicle servicem,nA value of 1;
the position updating formula of the unmanned aerial vehicle is as follows:
Figure FDA0003101477860000016
xmindicating the location of the mth device;
step 304, according to the updated two-dimensional position of each unmanned aerial vehicle, finding out whether each unmanned aerial vehicle is taken as a device which is recently contacted;
step 305, performing grouping optimization adjustment; for unmanned plane n1In not with drone n1As a device i which is recently contacted, firstly, a nearest unmanned aerial vehicle n corresponding to the device i is judged2Whether the number of devices served is equal to Ω; if so, the unmanned plane n is switched to the optimal switching method1The nearest drone n in which a device i that is not the nearest contact switches to a device i2(ii) a Otherwise, executing the operation, and enabling the unmanned aerial vehicle n1To drone n by device i not most recently contacted2；
The specific process is as follows:
first, unmanned plane n is selected2Each device j of the service calculates the gain r of the device j after exchanging with the device ij；
The calculation formula is as follows:
Figure FDA0003101477860000021
to unmanned plane n in sequence2All the devices in service respectively calculate the gain corresponding to each device after the device is exchanged with the device i, and when the objective function is reduced, the gain r of the device j is increasedjWhen the switching time is more than 0, the optimal switching equipment is selected
Figure FDA0003101477860000022
To unmanned plane n1；
Step 306, after exchanging or performing operation, updating the equipment in each unmanned aerial vehicle set, and updating the two-dimensional position of the unmanned aerial vehicle; repeating the steps 303-305 until the objective function value of the grouping model is not reduced any more, and obtaining the updated two-dimensional positions of the optimal equipment and the unmanned aerial vehicle in each unmanned aerial vehicle set;
step four, sequentially solving a matching qualification matrix between the equipment group and the channel served by each unmanned aerial vehicle by using a matching theory, and solving each matching problem by using a Hungarian algorithm or an MOSEK tool to obtain a matching matrix corresponding to each equipment group so as to further obtain a channel allocation result corresponding to each equipment;
the specific mode is as follows:
step 401, according to the updated two-dimensional position of each unmanned aerial vehicle, selecting two unmanned aerial vehicles with the nearest adjacent distance, and defining the equipment group of each service as pi1And pi2Calculating the device groups which are nearest to all the previous device groups and are not allocated with channels one by one;
the calculation formula is as follows:
Figure FDA0003101477860000023
step 402, initiating a group of devices pi1Each device in the network randomly allocates a respective channel, and each device occupies different channels respectively;
step 403, sequentially selecting each device from the current device group, and respectively calculating an interference value between each device and each channel to obtain a matching optimization problem of the current device group;
the initial value of the current equipment group is pi2Is divided intoAllocating equipment group pi1Device i, device group pi in (1)2The interference value between device j and channel k in (1) is λj,k；
Figure FDA0003101477860000024
Wherein
Figure FDA0003101477860000025
Representing the distributed equipment occupying the channel k in other equipment groups; when in use
Figure FDA0003101477860000026
Having a value ofi,k＝0；wi,jRepresenting the interference coefficient between devices i and j of two shared channels; the calculation formula is as follows:
Figure FDA0003101477860000027
wherein n is1The unmanned plane which represents the nearest contact of the equipment i corresponds to the equipment group with the value of pi1；n2The unmanned plane which represents the nearest contact of the equipment j is in the corresponding equipment group of pi2(ii) a The denominator represents the distance square sum of the link to be connected, and the numerator represents the distance square sum of the link to be interfered;
similarly, the interference coefficients of the device j and each of the remaining devices occupying the channel k are obtained to obtain the interference value λ of the device j and the channel kj,kAnd further, obtaining interference values respectively corresponding to each of the other devices and each of the channels, so that each device and channel of the device group served by the current unmanned aerial vehicle n is regarded as a matching problem, as follows:
Figure FDA0003101477860000031
Figure FDA0003101477860000032
Figure FDA0003101477860000033
Figure FDA0003101477860000034
wherein, the matching matrix S is a 0-1 matrix to be solved; solving is carried out by utilizing an MOSEK solving tool or Hungarian algorithm, and only one of each row is 1, and the rest are 0, S in the obtained matching matrix Sj,k1 indicates that each device occupies a different channel to transmit signals;
updating
Figure FDA0003101477860000035
Namely, it is
Figure FDA0003101477860000036
Step 404, update
Figure FDA0003101477860000037
Returning to step 403, selecting the next equipment group as the current equipment group, and sequentially obtaining the matching optimization problem and the matching matrix corresponding to each equipment group, thereby obtaining a final channel allocation result;
fifthly, carrying out signal transmission aiming at the channels distributed by each device, and modeling aiming at an uplink orthogonal frequency division multiple access communication system by considering the signal-to-interference-and-noise ratio of a received signal;
the model was constructed as follows:
the objective function is to minimize the total transmit power;
the constraint conditions are as follows: the signal-to-interference-and-noise ratio threshold, the transmission power constraint and the unmanned aerial vehicle height constraint are met;
the model expression is as follows:
Figure FDA0003101477860000038
Figure FDA0003101477860000039
Figure FDA00031014778600000310
Figure FDA00031014778600000311
wherein p ismRepresents the transmit power of device m;
Figure FDA00031014778600000312
representing the altitude of the drone n with which the device m is in contact; gamma ray0Representing a signal to interference plus noise ratio threshold; sigma2A variance representing additive white gaussian noise;
Figure FDA00031014778600000313
represents the expected channel power of device m and the contacted drone n; pmaxRepresents a maximum value of the transmission power; h isminA minimum value representing the altitude of the drone; h ismaxA maximum value representing the altitude of the drone;
step six, respectively optimizing the total transmitting power of the equipment and the height of each unmanned aerial vehicle based on the idea of alternating iteration, and finally converging to obtain the minimum total transmitting power so as to realize the energy-efficient internet of things data collection assisted by the unmanned aerial vehicles;
firstly, fixing the height of an unmanned aerial vehicle, simplifying the modeling model into a linear programming problem, and solving by utilizing a CVX convex optimization toolkit;
Figure FDA0003101477860000041
Figure FDA0003101477860000042
Figure FDA0003101477860000043
then, fixing the obtained transmitting power
Figure FDA0003101477860000044
The non-convex problem of minimized power is obtained;
Figure FDA0003101477860000045
Figure FDA0003101477860000046
Figure FDA0003101477860000047
for this non-convex problem, the height of each drone is solved in turn to reduce the variable dimension, as follows:
Figure FDA0003101477860000048
Figure FDA0003101477860000049
hmin≤hn≤hmax
for this one-dimensional optimization problem, use is made ofOne-dimensional search algorithms, such as golden section or dichotomy, solve this problem while updating
Figure FDA00031014778600000410
And hn；
Iteratively performing the steps to obtain the final height of the unmanned aerial vehicle and the power of each device, and realizing the minimization of the total transmitting power;
seventhly, when one part of equipment enters the 'dormant' state and the other part of equipment enters the 'active' state within the periodic time, namely mu changes, the second step to the sixth step are carried out again to complete resource reconfiguration and unmanned aerial vehicle deployment;
the method comprises the following steps that Kmeans + + is not used any more when the position of the unmanned aerial vehicle is initialized, and the horizontal position of the unmanned aerial vehicle obtained before is directly used as an initial center; the initial height of the unmanned aerial vehicle also utilizes the height of the unmanned aerial vehicle which is deployed last time;
order set L1Representing N previously deployed drone sequence numbers, set L2Representing the N position sequence numbers determined by the current re-deployment, the optimization problem of each unmanned aerial vehicle movement is written as:
Figure FDA0003101477860000051
Figure FDA0003101477860000052
Figure FDA0003101477860000053
Figure FDA0003101477860000054
wherein C represents an NxN-sized 0-1 matrix, if Cij1 represents noneThe man-machine i will move to position j; eijRepresenting the energy consumed by the unmanned plane i to move to the position j, and relating to the length of the track; therefore, the optimization problem of unmanned aerial vehicle movement is an integer programming problem and is also a typical bilateral matching problem in practice, the MOSEK solving tool or Hungarian algorithm is directly used for solving, a matching relation matrix C between the unmanned aerial vehicle and a new deployment position, namely the track of the unmanned aerial vehicle is obtained, and therefore energy consumption of unmanned aerial vehicle movement is minimized.
2. The unmanned-aerial-vehicle-assisted energy-efficient internet-of-things data collection method of claim 1, wherein the communication scenario applicable in the first step comprises: 1. uplink communication between the multiple unmanned aerial vehicles and the large-scale ground Internet of things equipment; 2. downlink communication between the multiple unmanned aerial vehicles and the large-scale ground Internet of things equipment; 3. the uplink communication between the plurality of ground mobile base stations and the large-scale unmanned aerial vehicle is realized; 4. and the plurality of ground mobile base stations and the large-scale unmanned aerial vehicle perform downlink communication.
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