CN114598721A - High-energy-efficiency data collection method and system based on joint optimization of track and resources - Google Patents

High-energy-efficiency data collection method and system based on joint optimization of track and resources Download PDF

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CN114598721A
CN114598721A CN202210240716.3A CN202210240716A CN114598721A CN 114598721 A CN114598721 A CN 114598721A CN 202210240716 A CN202210240716 A CN 202210240716A CN 114598721 A CN114598721 A CN 114598721A
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唐晓
姜玉丹
张洪瑞
蓝驯强
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Abstract

The invention discloses a high-energy-efficiency data collection method and system based on combined optimization of a track and resources, wherein the energy efficiency of the system is introduced, the transmission energy efficiency of an Internet of things node cluster assisted by an unmanned aerial vehicle is researched, meanwhile, the transmission energy of the Internet of things and the propulsion energy of the unmanned aerial vehicle, the communication service requirement of the Internet of things, the flight path of the unmanned aerial vehicle and the communication scheduling among the nodes of the Internet of things are considered, the energy efficiency of the system is optimized from the two aspects of the transmission energy of the Internet of things and the propulsion energy of the unmanned aerial vehicle, and the combined optimization of the flight path of the unmanned aerial vehicle and the communication resources of the Internet of things is covered; the method comprises the steps of firstly constructing a communication scene between a single unmanned aerial vehicle and multi-ground Internet of things equipment, then respectively solving the total transmission data volume, the total power of the Internet of things and the propulsion power of the unmanned aerial vehicle to obtain an expression of system energy efficiency, converting the expression into a form which is easy to process by using a Dinkelbach method, namely converting an original separation optimization problem into a subtraction optimization problem and approaching the subtraction optimization problem, and further adopting a block coordinate descent method to iteratively update unmanned aerial vehicle tracks and Internet of things communication resources.

Description

High-energy-efficiency data collection method and system based on joint optimization of track and resources
Technical Field
The invention belongs to the technical field of air-to-air communication, and particularly relates to a high-energy-efficiency data collection method and system based on joint optimization of tracks and resources.
Background
With the maturity and commercial deployment of 5G mobile communication networks, people are continuously advancing the research of 6G wireless systems. The internet of things (IoT), one of the basic elements of the 5G era, is evolving towards the interconnection of everything, bringing ubiquitous awareness, computing and communication towards an interconnected and more intelligent world. The Internet of things integrates a large number of nodes with low cost and low power consumption, is convenient for large-scale deployment, and has wide application in a plurality of fields such as agriculture, industry, city management and the like. In this regard, the large number of internet of things devices requires the production of large amounts of data to digitize and intelligentize the physical world. Therefore, timely and effective data collection has become a basic problem in the field of internet of things. Meanwhile, large nodes in the internet of things are usually powered by internal batteries, and the expected working time varies from several days to several years, so that energy-efficient operation is important for prolonging the service life of the nodes and even the whole network.
On the other hand, the 6G era network is an integrated space-air-ground network in which the drone (uav) is playing an increasingly important role. Flexible deployment of drones can provide coverage for higher and wider areas to extend the network range. Furthermore, unmanned aerial vehicles have facilitated various applications in military and civilian areas. Especially in the thing networking field, unmanned aerial vehicle can conveniently establish the communication under the condition that lacks traditional network infrastructure, and the thing networking equipment that is close the low-power consumption carries out efficient data acquisition. The unmanned aerial vehicle not only saves capital and operation expenses, but also saves energy of the internet of things, because the unmanned aerial vehicle can easily reach the vicinity of the internet of things equipment to establish effective communication.
Because the unmanned aerial vehicle assists the communication of the internet of things to have obvious advantages and obvious benefits, a large amount of related research appears in the field, and the research relates to data collection, network management, calculation unloading, information safety, energy efficiency and the like. For example, in the relevant references, different authors consider that drones gather data from sensors located on a straight line, jointly optimizing drone flight trajectory and sensor power to shorten drone flight time; the problem of joint unmanned aerial vehicle track and power control of cooperative transmission among a plurality of unmanned aerial vehicles is researched aiming at the interference problem of a sight distance unmanned aerial vehicle channel. In these research efforts, energy issues are both fundamental and critical and have received considerable attention. Although many energy-efficient communication technologies exist at present, such as discontinuous reception in a narrowband internet of things, when transmission is in an active state, the energy consumption of the internet of things is still quite high, and thus energy-efficient operation is required. In the relevant references, different authors propose dispatching multiple drones for the internet of things to establish communication; the dynamic association of the Internet of things equipment and the unmanned aerial vehicle is provided, and the mobility of the unmanned aerial vehicle is optimized to minimize the total transmission power. In the reference, authors have studied the scenario of multi-hop communication using drones as relays to establish internet of things data collection. In the reference, authors utilize drones for data aggregation in cellular networks with shared spectrum, which utilizes stochastic geometry theory to analyze the average energy efficiency of the network. In the reference, in order to improve the energy efficiency of the network, an author jointly studies the data acquisition and the equipment positioning of the unmanned aerial vehicle-internet of things. In the reference, the energy efficiency optimization of the internet of things is designed from the routing point of view. In the reference, an unmanned aerial vehicle is used for carrying out backscattering communication based on deep learning, and energy efficiency is improved. In the reference, the authors consider a hybrid energy drone from solar and charging stations for internet of things data collection and propose reinforcement learning with and without strategies to decide the trajectory of the drone and discontinuous internet of things reception.
Unmanned aerial vehicles play a key role in future 6G networks, provide convenient and seamless coverage, and realize ubiquitous interconnection application. The application of the internet of things is prosperous, and the research and the enhancement of the unmanned aerial vehicle assisted internet of things are very necessary, wherein the energy of the unmanned aerial vehicle assisted internet of things has a huge bottleneck problem. Although the aforementioned various schemes can improve energy efficiency in different aspects, there is little work to study the unmanned aerial vehicle-assisted internet of things from a system perspective. In particular, in order to improve the energy efficiency of the drone-internet of things system, both the propulsion energy problem and the communication energy problem need to be solved, since the former supports drone flight and the latter supports networked data transmission. Only through the energy consumption of coordinating unmanned aerial vehicle and thing networking can the extension system life-span effectively. In addition, most of the traditional research works independently consider each internet of things node, and when the number of the internet of things nodes is increased, the expandability of the internet of things nodes is greatly challenged. Nowadays, the internet of things equipment cluster can collectively and jointly complete certain tasks.
Through the above analysis, the problems and disadvantages of the prior art include:
(1) the energy of the unmanned aerial vehicle-assisted internet of things has a huge bottleneck problem.
(2) Most of traditional research works independently consider each internet of things node, and when the number of the internet of things nodes is increased sharply, the expandability of the internet of things nodes is greatly challenged.
(3) The existing work is only from the perspective of the system to research the unmanned aerial vehicle auxiliary Internet of things.
In particular, in order to improve the energy efficiency of the drone-internet of things system, both the propulsion energy problem and the communication energy problem need to be solved, since the former supports drone flight and the latter supports networked data transmission. Only through the energy consumption of coordinating unmanned aerial vehicle and thing networking can the extension system life-span effectively.
Disclosure of Invention
The invention aims to solve the technical problem that the defects in the prior art are overcome, and the energy-efficient data collection method and the system based on the combined optimization of the track and the resources are provided.
The invention adopts the following technical scheme:
the energy-efficient data collection method based on the joint optimization of the track and the resources comprises the following steps:
s1, constructing a scene of communication between the single unmanned aerial vehicle and the multi-ground Internet of things equipment;
s2, analyzing the advancing path of the unmanned aerial vehicle and analyzing data transmitted from the nodes to the unmanned aerial vehicle based on the scene constructed in the step S1 to obtain the propulsion power of the unmanned aerial vehicle and the power of the Internet of things;
s3, establishing a model for carrying out virtual MIMO transmission on the unmanned aerial vehicle by using the transmitting antenna and the receiving antenna based on the nodes of the active clusters in the scene constructed in the step S1, and obtaining the minimum data volume transmitted by each cluster;
s4, introducing energy efficiency of the single unmanned aerial vehicle-multi-Internet of things system, and designing an energy efficiency optimization model of the single unmanned aerial vehicle-multi-Internet of things system by using the propulsion power and the Internet of things power of the unmanned aerial vehicle obtained in the step S2 and the minimum data volume transmitted by each cluster obtained in the step S3;
s5, dividing the problem to be optimized into an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem, and solving the unmanned aerial vehicle track and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem by using a BCD algorithm;
s6, converting the primitive optimization problem of the single unmanned aerial vehicle-multiple Internet of things system energy efficiency optimization model expression obtained in the step S4 into a subtractive type and approaching the subtractive type by using a large system analysis and Dinkelbach method, and performing double-loop iteration solving on the unmanned aerial vehicle trajectory and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem in the step S5 to obtain updated data volume and transmission energy;
s7, updating the energy efficiency of the single unmanned aerial vehicle-multiple Internet of things system according to the data volume and the transmission energy updated in the step S6;
s8, repeating the steps S5 to S7 based on the system energy efficiency updated in the step S7 until the single unmanned aerial vehicle-multi-Internet of things system converges to obtain the maximum system energy efficiency, and enabling the unmanned aerial vehicle to approach the Internet of things cluster to achieve energy-efficient Internet of things transmission and further achieve energy-efficient data collection.
Specifically, in step S2, the propulsion power P of the dronenComprises the following steps:
Pn＝P(Vn)
wherein, P (V)n) As a function of the flight speed;
power p of internet of thingsj,nComprises the following steps:
Figure BDA0003541114960000041
wherein p is0Is the circuit power, p, of the vehicle-mounted assembly supporting data transmissionji,nFor unmanned aerial vehicle at qnI cluster j consisting of I nodes total power consumption, IjIs a transmit antenna.
Specifically, in step S3, the virtual MIMO transmission model is:
Figure BDA0003541114960000042
wherein, yj,nFor the K-dimensional reception of signals on the drone,
Figure BDA0003541114960000043
representing the channel, xj,nRepresenting I from a node in cluster jjDimension transmission signal, zj,nRepresenting background noise.
Specifically, in step S3, the minimum amount of data to be transmitted per cluster
Figure BDA0003541114960000044
Comprises the following steps:
Figure BDA0003541114960000045
wherein, tauj,nFor the transmission time slot assigned to cluster j, Rj,nThe transmission rate of the cluster J to the unmanned aerial vehicle is that J forms J clusters for the nodes of a certain area on the ground, and N is a discrete path point of the advancing path of the unmanned aerial vehicle.
Specifically, in step S4, the single drone-multiple internet of things system energy efficiency optimization model specifically is:
Figure BDA0003541114960000051
s.t.q0＝qS,qN+1＝qE
Figure BDA0003541114960000052
Figure BDA0003541114960000053
Figure BDA0003541114960000054
Figure BDA0003541114960000055
Figure BDA0003541114960000056
Figure BDA0003541114960000057
Figure BDA0003541114960000058
Figure BDA0003541114960000059
Figure BDA00035411149600000510
Figure BDA00035411149600000511
wherein p isji,nFor unmanned aerial vehicle at qnOf a cluster of i nodes, qnGround projection corresponding to the path of travel of the unmanned aerial vehicle, tnFor unmanned aerial vehicle at two adjacent waypoints qnAnd q isn+1Time taken to fly therebetween, τj,nFor the transmission time slot assigned to cluster j, Rj,nFor the transmission rate of cluster j to the unmanned aerial vehicle, xi is the energy efficiency of a single unmanned aerial vehicle-multiple Internet of things system, pj,nFor unmanned aerial vehicle at qnThe total power consumption of the cluster j, χ is a weighting factor, E is the propulsive energy consumption of the unmanned aerial vehicle in the whole flight process, q0Ground projection for unmanned aerial vehicle origin, qSTwo-dimensional ground projection for the starting point of the drone, qN+1Ground projection for unmanned aerial vehicle terminal point, qEFor the two-dimensional ground projection of the unmanned aerial vehicle, N is the discrete path point, delta, of the unmanned aerial vehicle's path of travelmaxMaximum distance, V, for unmanned aerial vehiclemaxMaximum speed of unmanned aerial vehicle, EpropIn order to limit the propulsion energy of the unmanned aerial vehicle,
Figure BDA00035411149600000512
is the maximum value of transmission power, I, when the nodes in a cluster j consisting of I nodes transmit data to the unmanned aerial vehiclejIn order to be a transmitting antenna or antennas,
Figure BDA00035411149600000513
for the minimum amount of data transmitted per cluster, PnFor unmanned plane by qnMove to qn+1Consumed propulsion power, omegaj,nAs an auxiliary variable, the number of variables,
Figure BDA0003541114960000061
as the power of the noise, /)ji,nIs a diagonal matrix and K is a dimension.
Specifically, in step S5, the BCD algorithm is used to solve the unmanned aerial vehicle trajectory and scheduling problem, the internet of things node power allocation problem, and the auxiliary optimization problem in an iterative manner until a stagnation point is found, specifically:
s501, initializing iota ← 0, optimizing variables according to the initialization technique, the vector of the initialized variables being SOL (iota);
s502, iteration is carried out, namely iota ← iota +1, the iteration content is an optimization method of an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem, and a solution vector in the current iteration is marked as SOL (iota);
and S503, repeating the step S502 until a convergence condition | SOL (iota) -SOL (iota-1) | < epsilon, wherein epsilon is a threshold value of a predefined termination algorithm, and obtaining a solution of the problem that the target function is subtraction.
Further, the unmanned aerial vehicle trajectory and scheduling problem is as follows:
Figure BDA0003541114960000062
the problem of node power distribution of the Internet of things is as follows:
Figure BDA0003541114960000063
the auxiliary optimization problems are as follows:
Figure BDA0003541114960000064
wherein the content of the first and second substances,
Figure BDA0003541114960000065
for the specific value of the auxiliary variable, zj,nAs an auxiliary variable, ynAs a relaxation variable, qnGround projection corresponding to the path of travel of the unmanned aerial vehicle, tnFor unmanned aerial vehicle at two adjacent waypoints qnAnd q isn+1Time spent flying in between, pji,nFor unmanned aerial vehicle at qnOf a cluster j consisting of i nodes, the total power consumption, τj,nFor the transmission time slot allocated to cluster j, p0The circuit power for supporting data transmission of the vehicle-mounted component is zeta, xi is energy efficiency of a single unmanned aerial vehicle-multiple Internet of things system, chi is a weighting factor, E is propulsion energy consumption of the unmanned aerial vehicle in the whole flight process, N is a discrete path point of an advancing path of the unmanned aerial vehicle, and IjBeing transmitting antennas, omegaj,nAs an auxiliary variable, the number of variables,
Figure BDA0003541114960000066
as the power of the noise, /)ji,nIs a diagonal matrix and K is a dimension.
Specifically, in step S6, the Dinkelbach method specifically includes:
s601, initializing iota ← 0, ξ (iota) ← 0, initializing optimized variables according to the technique, and calculating the amount of data transferred and the energy consumed, respectively, the results being denoted by D (iota) and E (iota), respectively, iota representing iteration;
s602, replacing xi (iota) in the objective function in the subtraction form with xi (iota-1), and solving by using a method for solving an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem;
s603, updating the transmission data volume and the energy by using the transmission data volume and the energy calculated in the step S601 respectively;
s604, updating the system energy efficiency by using the updated transmission data amount and energy, namely ξ (iota) ← D (iota)/E (iota);
s605, the above-mentioned steps are repeated until D (iota-1) - ξ (iota) E (iota-1) < oa, which is the threshold value of the predefined termination algorithm.
Specifically, in step S7, the system energy efficiency ξ is:
Figure BDA0003541114960000071
wherein x is a weighting factor, D is total data transmitted by the single unmanned aerial vehicle-multiple Internet of things system, E is total energy consumed by the single unmanned aerial vehicle-multiple Internet of things system, and P is total energy consumed by the single unmanned aerial vehicle-multiple Internet of things systemnIs made withoutMan-machine driven by qnMove to qn+1Consumed propulsive power, tnFor unmanned aerial vehicle at two adjacent waypoints qnAnd q isn+1Time spent flying in between, tauj,nFor the transmission time slot assigned to cluster j, Rj,nThe transmission rate of the cluster J to the unmanned aerial vehicle is shown, N is a discrete path point of a traveling path of the unmanned aerial vehicle, and J is a node of a certain area on the ground to form J clusters.
In a second aspect, an embodiment of the present invention provides an energy-efficient data collection system based on joint optimization of a track and a resource, including:
the scene module is used for constructing a scene of communication between the single unmanned aerial vehicle and the multi-ground Internet of things equipment;
the power module analyzes the advancing path of the unmanned aerial vehicle and data transmission analysis from the nodes to the unmanned aerial vehicle on the basis of the scene constructed by the scene module to obtain the propulsion power of the unmanned aerial vehicle and the power of the Internet of things;
the data module is used for establishing a model for carrying out virtual MIMO transmission on the unmanned aerial vehicle by using the transmitting antenna and the receiving antenna based on the nodes of the active clusters in the scene constructed by the scene module to obtain the minimum data volume transmitted by each cluster;
the optimization module introduces the energy efficiency of the single unmanned aerial vehicle-multiple Internet of things system, and designs a single unmanned aerial vehicle-multiple Internet of things system energy efficiency optimization model by utilizing the propulsion power of the unmanned aerial vehicle and the Internet of things power obtained by the power module and the minimum data volume transmitted by each cluster obtained by the data module 3;
the solution module is used for dividing the problem to be optimized into an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem, and solving the unmanned aerial vehicle track and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem by using a BCD algorithm;
the iteration module is used for converting the primitive optimization problem of the single-unmanned-plane-multi-Internet-of-things system energy efficiency optimization model expression obtained by the optimization module into a subtractive optimization problem and approximating the subtractive optimization problem by using a large-system analysis and Dinkelbach method, and performing double-loop iteration solution on the unmanned-plane trajectory and scheduling problem, the Internet-of-things node power distribution problem and the auxiliary optimization problem in the solution module to obtain updated data volume and transmission energy;
the updating module is used for updating the energy efficiency of the single unmanned aerial vehicle-multiple Internet of things system according to the data volume and the transmission energy updated by the iteration module;
the collection module is used for repeatedly solving the module, the iteration module and the updating module based on the system energy efficiency updated by the updating module until the single unmanned aerial vehicle-multi-Internet of things system converges to obtain the maximized system energy efficiency, and the unmanned aerial vehicle is close to the Internet of things cluster to realize energy-efficient Internet of things transmission and further realize energy-efficient data collection.
Compared with the prior art, the invention has at least the following beneficial effects:
according to the energy-efficient data collection method based on the combined optimization of the track and the resource, the transmission energy efficiency of the Internet of things node cluster assisted by the unmanned aerial vehicle is researched, and meanwhile, various factors including Internet of things transmission energy, unmanned aerial vehicle propulsion energy, Internet of things communication service requirements, unmanned aerial vehicle track and Internet of things node communication scheduling are considered, so that the energy efficiency can be improved to the maximum extent from the system perspective. Only through the energy consumption of coordinating unmanned aerial vehicle and thing networking can prolong system's life-span effectively to can be close to thing networking cluster with Unmanned Aerial Vehicle (UAV), in order to realize the high-energy efficiency data collection.
Further, by setting the propulsion power P of the unmanned aerial vehiclenAnd the power of the Internet of things can be used for obtaining the energy consumption of the single unmanned aerial vehicle-multiple Internet of things system, so that the minimization treatment is facilitated, the energy consumption of the single unmanned aerial vehicle-multiple Internet of things system is minimized, and the service life of the single unmanned aerial vehicle-multiple Internet of things system is prolonged.
Furthermore, by setting the virtual MIMO transmission model, the resources of the spatial dimension can be better utilized, the spectrum efficiency can be improved, and a wider coverage rate and a larger system capacity can be obtained. .
Further, by setting the minimum amount of data to be transmitted per cluster
Figure BDA0003541114960000091
With a minimum constraint, canThe data transmission of the Internet of things cluster can be maximized as far as possible.
Furthermore, a single unmanned aerial vehicle-multiple Internet of things system energy efficiency optimization model is introduced, multiple factors are considered at the same time, the system energy efficiency is improved from the system perspective and is important for prolonging the service life of energy-limited Internet of things devices, and therefore the unmanned aerial vehicle can be close to the Internet of things cluster to achieve high-energy-efficiency Internet of things transmission and further achieve high-energy-efficiency data collection.
Furthermore, the optimal system energy efficiency is found by iteration by repeating the above steps until D (iota-1) - ξ (iota) E (iota-1) < oa, which is the threshold value of the predefined termination algorithm.
Further, in step S5, the BCD algorithm is used to solve the three sub-problems in an iterative manner until a stagnation point is found, which is not solved by the Dinkelbach method, so that the decomposition into three sub-problems is adopted, and then the BCD is adopted to solve the three sub-problems.
Furthermore, the problem is solved through double-loop iteration to obtain updated data volume and transmission energy, the energy efficiency of the single unmanned aerial vehicle-multi-Internet of things system is further updated, and iteration is carried out to obtain the optimal system energy efficiency.
Further, the system energy efficiency xi is defined as the ratio of the total transmission data quantity to the weighted sum of the transmission energy of the internet of things and the propulsion energy of the unmanned aerial vehicle, and then the problem is modeled to be the energy efficiency of the maximized system, so that the problem to be solved is clear.
In conclusion, the invention researches the transmission energy efficiency of the Internet of things node cluster assisted by the unmanned aerial vehicle, considers various factors including Internet of things transmission energy, unmanned aerial vehicle propulsion energy, Internet of things communication service requirements, unmanned aerial vehicle tracks, Internet of things node communication scheduling and the like, and can improve the energy efficiency to the maximum extent from the system perspective. In addition, the system life can be effectively prolonged by coordinating the energy consumption of the unmanned aerial vehicle and the Internet of things. Moreover, each internet of things node is considered independently in most of traditional research works, when the number of the internet of things nodes is increased, the expandability of the internet of things nodes is greatly challenged, and the influence caused by the increase of the number of the internet of things nodes can be effectively solved by the solution provided by the invention.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
Fig. 1 is a flowchart of an energy-efficient data collection method based on joint optimization of a track and resources in an unmanned aerial vehicle-assisted internet of things according to an embodiment of the present invention;
FIG. 2 is a flow chart of optimizing energy pricing data transmission based on BCD algorithm according to an embodiment of the present invention;
FIG. 3 is a diagram of a trajectory of a drone with different scenarios and data volume thresholds;
fig. 4 is a diagram comparing performance in terms of system energy efficiency and amount of data transmitted under different schemes for a threshold amount of data.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be understood that the terms "comprises" and/or "comprising" indicate the presence of the stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and including such combinations, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
It should be understood that although the terms first, second, third, etc. may be used to describe preset ranges, etc. in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, the first preset range may also be referred to as a second preset range, and similarly, the second preset range may also be referred to as the first preset range, without departing from the scope of the embodiments of the present invention.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
Unmanned aerial vehicles play a key role in future 6G networks, provide convenient and seamless coverage, and realize ubiquitous interconnection application. The application of the internet of things is prosperous, and the research and the enhancement of the unmanned aerial vehicle assisted internet of things are very necessary, wherein the energy of the unmanned aerial vehicle assisted internet of things has a huge bottleneck problem. Although a great deal of relevant research appears in the field, various schemes can improve energy efficiency in different aspects, the unmanned aerial vehicle auxiliary internet of things is researched from the perspective of a system.
In particular, in order to improve the energy efficiency of the drone-internet of things system, both the propulsion energy problem and the communication energy problem need to be solved, since the former supports drone flight and the latter supports networked data transmission. Only through the energy consumption of coordinating unmanned aerial vehicle and thing networking can the extension system life-span effectively. In addition, most of the traditional research works independently consider each internet of things node, and when the number of the internet of things nodes is increased, the expandability of the internet of things nodes is greatly challenged. Nowadays, the internet of things equipment cluster can collectively and jointly complete certain tasks.
The invention provides an energy-efficient data collection method based on combined optimization of a track and resources, which is used for researching the transmission energy efficiency of an Internet of things node cluster assisted by an unmanned aerial vehicle, and simultaneously considering various factors, wherein the factors comprise Internet of things transmission energy, unmanned aerial vehicle propulsion energy, Internet of things communication service requirements, unmanned aerial vehicle track and Internet of things node communication scheduling, the system energy efficiency is optimized from two aspects of Internet of things transmission energy and unmanned aerial vehicle propulsion energy, and the combined optimization of the unmanned aerial vehicle track and Internet of things communication resources is covered; the method comprises the steps of firstly constructing a communication scene between a single unmanned aerial vehicle and multi-ground Internet of things equipment, then respectively solving the total transmission data volume, the total power of the Internet of things and the propulsion power of the unmanned aerial vehicle to obtain an expression of system energy efficiency, converting the expression into a form which is easy to process by using a Dinkelbach method, namely converting an original separation optimization problem into a subtraction optimization problem and approaching the subtraction optimization problem, and further adopting a block coordinate descent method to iteratively update unmanned aerial vehicle tracks and Internet of things communication resources.
Referring to fig. 1, the present invention provides a method for collecting energy-efficient data based on joint optimization of track and resource, including the following steps:
s1, constructing a scene of communication between the single unmanned aerial vehicle and the multi-ground Internet of things equipment;
the method comprises the steps that the internet of things network is deployed in a ground area, J clusters are shared in the area, and are represented as J ═ 1,2, …, J, and in the jth cluster, I existsjA node, denoted as Ij＝{1,2,…,Ij}; the unmanned aerial vehicle is provided with k antennas, and each ground internet of things node is provided with only one antenna; the drone flies at a fixed altitude, denoted H; it also has fixed start and end points, with q being the respective two-dimensional ground projectionS∈R2And q isE∈R2And (4) showing. The path of travel of the drone may be simplified modeled by a series of discrete waypoints, the corresponding ground projection being represented as qn]n＝0,1,…,N+1，N＝{0,1,…,N}。
S2, obtaining the propulsion power of the unmanned aerial vehicle and the power of the Internet of things based on the analysis of the advancing path of the unmanned aerial vehicle and the analysis of the data transmitted from the nodes to the unmanned aerial vehicle;
analyzing the advancing path of the unmanned aerial vehicle and analyzing the data transmitted from the nodes to the unmanned aerial vehicle to obtain the position q of the unmanned aerial vehiclenThe total power consumption of the cluster j and the power p of the Internet of things cluster jj,nComprises the following steps:
Figure BDA0003541114960000121
wherein the unmanned aerial vehicle is located at qnThe total power consumption of cluster j comprises the total power p of the nodes and the clusterji,nAnd circuit power p of vehicle-mounted component for supporting data transmission0。
Given drone slave qnMove to qn+1Length of elapsed time tnFor the previous condition, an average speed of
Figure BDA0003541114960000122
The propulsion power of the drone is expressed as:
Pn＝P(Vn)
with unmanned aerial vehicle propulsion power establishedMode being a function of flight speed P (V)n) The method specifically comprises the following steps:
Figure BDA0003541114960000131
considering that the propulsion energy of the unmanned aerial vehicle is limited in the whole flight process, an energy constraint is provided to ensure that the unmanned aerial vehicle normally flies, namely the propulsion energy of the unmanned aerial vehicle is lower than a threshold value, as follows:
Figure BDA0003541114960000132
wherein E ispropIs the propulsion energy limit of the drone.
S3, establishing virtual MIMO transmission to the unmanned aerial vehicle by using the transmitting antenna and the receiving antenna based on the nodes in the active cluster in the scene established in the step S1, and obtaining the minimum data volume to be transmitted by each cluster;
all nodes in one cluster are communicated with the unmanned aerial vehicle at the same time, only one active cluster is arranged in the internet of things network, and the nodes in the active cluster are established by utilizing a transmitting antenna IjAnd receiving virtual MIMO transmission from the antenna K to the UAV, wherein the transmission modeling is as follows:
yj,n＝Hj,nxj,n+zj,n
wherein, yj,nFor the K-dimensional reception of signals on the drone,
Figure BDA0003541114960000133
representing the channel, xj,nRepresenting I from a node in cluster jjDimension transmission signal, zj,nRepresenting background noise.
Based on the theory of large system analysis, the channel is decomposed into:
Hj,n＝Sj,nLj,n
wherein L isj,nRepresenting a component of large scale from each node in cluster j to the drone, Sj,nRepresenting a small scale of fading.
Lj,nSpecified as the diagonal matrix:
Figure BDA0003541114960000134
Figure BDA0003541114960000135
representing the distance between node i in cluster j and the drone.
Figure BDA0003541114960000141
Represents the combined effect of line-of-sight (LoS) propagation and non-line-of-sight (NLoS) propagation fading with carrier frequency f and light speed c, respectively; etaLos，ηNLosA, and b depend on the propagation environment and
Figure BDA0003541114960000142
here, the number of the first and second electrodes,
Figure BDA0003541114960000143
is the probability of line-of-sight propagation of the air-to-ground communication link, and is therefore deltaji,nCorresponding to a weighted sum of line-of-sight and non-line-of-sight communications.
Using large system analysis methods, at time tnObtaining the transmission rate R of the cluster j to the unmanned aerial vehiclej,nComprises the following steps:
Figure BDA0003541114960000144
wherein the content of the first and second substances,
Figure BDA0003541114960000145
represents the transmission power, I, of all nodes in the collection cluster jKIs a matrix of units, and is,
Figure BDA0003541114960000146
is the noise power.
Meanwhile, carrying out expected operation on small-scale fading; since each cluster j has a minimum amount of data to be transmitted
Figure BDA0003541114960000147
This results in the constraints given being:
Figure BDA0003541114960000148
based on the above discussion, the incoming system energy efficiency, defined as the ratio of the total amount of data transmitted to the energy consumed, is given by:
Figure BDA0003541114960000149
wherein, χ ≧ 0 is used as the weighting factor. Correspondingly, the problem of maximizing system energy efficiency by jointly optimizing unmanned aerial vehicle optimization tracks and internet of things transmission is provided, and the following expression is given:
Figure BDA00035411149600001410
s.t.q0＝qS,qN+1＝qE
Figure BDA00035411149600001411
Figure BDA00035411149600001412
Figure BDA00035411149600001413
Figure BDA0003541114960000151
Figure BDA0003541114960000152
Figure BDA0003541114960000153
Figure BDA0003541114960000154
Figure BDA0003541114960000155
wherein p isji,nFor unmanned aerial vehicle at qnOf a cluster of i nodes, qnGround projection corresponding to the path of travel of the unmanned aerial vehicle, tnFor unmanned aerial vehicle at two adjacent waypoints qnAnd q isn+1Time taken to fly therebetween, τj,nFor the transmission time slot assigned to cluster j, Rj,nFor the transmission rate of cluster j to the unmanned aerial vehicle, xi is the energy efficiency of a single unmanned aerial vehicle-multiple Internet of things system, pj,nFor unmanned aerial vehicle at qnThe total power consumption of the cluster j, χ is a weighting factor, E is the propulsion energy consumption of the unmanned aerial vehicle in the whole flight process, q0Ground projection for unmanned aerial vehicle origin, qSTwo-dimensional ground projection for the starting point of the drone, qN+1Ground projection for unmanned aerial vehicle terminal point, qEFor the two-dimensional ground projection of the unmanned aerial vehicle, N is the discrete path point, delta, of the unmanned aerial vehicle's path of travelmaxMaximum distance, V, for unmanned aerial vehiclemaxMaximum speed of unmanned aerial vehicle, EpropIn order to limit the propulsion energy of the unmanned aerial vehicle,
Figure BDA0003541114960000156
for a cluster j consisting of i nodes, the nodes in the cluster j are allMaximum transmission power, I, for man-machine data transmissionjIn order to be a transmitting antenna or antennas,
Figure BDA0003541114960000157
for the minimum amount of data transmitted per cluster, PnFor unmanned plane by qnMove to qn+1Consumed propulsion power, omegaj,nAs an auxiliary variable, the number of variables,
Figure BDA0003541114960000158
as the power of the noise, /)ji,nIs a diagonal matrix and K is a dimension.
S4, introducing energy efficiency of the single unmanned aerial vehicle-multi-Internet of things system, and designing an energy efficiency optimization model of the single unmanned aerial vehicle-multi-Internet of things system by using the propulsion power and the Internet of things power of the unmanned aerial vehicle obtained in the step S2 and the minimum data volume transmitted by each cluster obtained in the step S3;
for the obtained system energy efficiency expression, the objective function and the constraint condition are non-convex, and have a plurality of optimization variables, so that the optimization is difficult to achieve.
The transmission rate appears in an expected form of small-scale fading, the calculation is extremely inconvenient, and the small-scale fading is removed to obtain the internet of things-unmanned aerial vehicle transmission rate which is easier to process:
Figure BDA0003541114960000159
definition of ωj,nAre auxiliary variables that satisfy the following equation:
Figure BDA0003541114960000161
instead of the transmission rate and associated constraints in the original equation, furthermore, the objective function has a fractional form and has a linear denominator, so applying the Dinkelbach method to recompose the objective function as a subtraction, so restating the problem in a more manageable form:
Figure BDA0003541114960000162
s.t.q0＝qS,qN+1＝qE
Figure BDA0003541114960000163
Figure BDA0003541114960000164
Figure BDA0003541114960000165
Figure BDA0003541114960000166
Figure BDA0003541114960000167
Figure BDA0003541114960000168
Figure BDA0003541114960000169
Figure BDA00035411149600001610
Figure BDA00035411149600001611
Figure BDA00035411149600001612
where ξ is the subtraction coefficient.
Then, based on the idea of the Dinkelbach method, it is now solved as a series of problems in the above form, and ξ needs to be updated continuously. The convergence of the Dinkelbach process enables the solution of the original problem to exist, and therefore the original system energy efficiency optimization problem is solved.
S5, dividing the problem into three sub-problems based on a Dinkelbach method in the outside: solving the unmanned aerial vehicle track and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem by using a BCD algorithm;
referring to fig. 2, the problem described in the form of subtraction, as a step in the Dinkelbach procedure in step S4, is still unsolved; the method is decomposed into three subproblems and solved under a BCD framework. The first is the unmanned aerial vehicle trajectory and scheduling problem, which is solved by using the SCA technology, and is firstly expressed as:
Figure BDA0003541114960000171
what the above formula shows is when considering unmanned aerial vehicle energy and thing networking data volume restraint, furthest has improved the data transmission of energy price, redefines as:
Figure BDA0003541114960000172
wherein, { zj,n}j∈J,n∈NFor the introduced auxiliary variables, the Taylor approximation yields:
Figure BDA0003541114960000173
s.t.Eqs.(1)-(5)
Figure BDA0003541114960000174
Figure BDA0003541114960000175
Figure BDA0003541114960000176
Figure BDA0003541114960000177
Figure BDA0003541114960000178
Figure BDA0003541114960000179
it is easy to know that the problem is a convex one, and the solution to the original problem appears at convergence by updating the approximate points with the corresponding optimal values obtained in the last iteration.
The update power allocation problem is given the form:
Figure BDA00035411149600001710
this equation represents maximizing data transmission at the expense of power under the constraints of power and data volume of the internet of things. Consider now transmit power optimization for an internet of things node with a fixed trajectory and auxiliary equipment. In this regard, when each individual cluster independently determines the power allocation, the problem is decoupled at each individual cluster, as represented by:
Figure BDA0003541114960000181
wherein is made of
Figure BDA0003541114960000182
Replacing with the objective function, the following equation is obtained:
Figure BDA0003541114960000183
and (3) applying a Lagrange multiplier method to derive an optimal power distribution expression:
Figure BDA0003541114960000184
Figure BDA0003541114960000185
Figure BDA0003541114960000186
the auxiliary variable optimization result is as follows:
Figure BDA0003541114960000187
restated the problem as the following equation:
Figure BDA0003541114960000188
it is easy to verify that this equation describes a convex problem. Although a closed-form solution does not exist in the problem, an effective optimizer is obtained by positioning a zero point of a derivative through a two-stage search method, and therefore for each Internet of things cluster and unmanned aerial vehicle path point, auxiliary variables can be optimized through the method.
Initialization is crucial since the optimization involves multiple parameters and is solved through iteration. Here, the track is initialized based on traveler questions. Under the condition of correct initialization, solving three subproblems in an iterative mode until a stagnation point is found, wherein the specific mode is as follows:
s501, initialize ι ← 0, and optimize variables in accordance with the initialization technique described in detail above, and note that the vector of the initialized variables is SOL (ι);
s502, carrying out iteration, namely iota ← iota +1, wherein the iteration content is an optimization method of three subproblems, and recording a solution vector in the current iteration as SOL (iota);
s503, repeating the step S502 until a convergence condition | SOL (l) -SOL (l-1) | < epsilon, wherein epsilon is a threshold value of a predefined termination algorithm, and obtaining a solution of the problem that the target function is subtraction.
S6, converting the primitive optimization problem of the single unmanned aerial vehicle-multiple Internet of things system energy efficiency optimization model expression obtained in the step S4 into a subtractive type and approaching the subtractive type by using a Large system analysis (Large-system analysis) and Dinkelbach method, and performing a double-loop iteration solving problem through the unmanned aerial vehicle track and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem in the step S5 to obtain updated data volume and transmission energy;
the Dinkelbach method specifically comprises the following steps:
s601, initializing iota ← 0, ξ (iota) ← 0, initializing optimized variables according to the technique, and calculating the amount of data transmitted and the energy consumed, respectively, the results being represented by D (iota) and E (iota), respectively, where iota represents iteration;
s602, substituting xi (iota) in the target function in the subtraction form into xi (iota-1), and solving by using a subproblem solving method;
s603, respectively updating the transmission data amount and the energy by using the transmission data amount and the energy calculated in the step S601;
s604, updating system energy efficiency by using the updated transmission data quantity and energy, namely ξ (iota) ← D (iota)/E (iota);
s605, the above-mentioned steps are repeated until D (iota-1) - ξ (iota) E (iota-1) < oa, wherein the oa is the threshold value of the predefined termination algorithm.
S7, respectively updating the transmission data volume and energy, and updating the system energy efficiency;
and S8, repeating the steps S6 to S7 based on the updated system energy efficiency until the system converges to obtain the maximized system energy efficiency, wherein the high energy efficiency is crucial to prolonging the service life of the energy-limited Internet of things device, so that the unmanned aerial vehicle can approach the Internet of things cluster to realize high energy efficiency Internet of things transmission and further realize high energy efficiency data collection.
And when the solutions of the three subproblems are obtained, solving the maximum energy efficiency of the system based on a Dinkelbach method. Wherein iota denotes an iteration and oa denotes convergence. The invention initializes optimization variables based on the problem of traveling salesman, firstly calculates the data quantity transmitted and the energy consumed, and respectively uses the result D(ι)And E(ι)And (4) showing.
In another embodiment of the present invention, an energy-efficient data collection system based on joint optimization of trajectory and resource is provided, where the system can be used to implement the above-mentioned energy-efficient data collection method based on joint optimization of trajectory and resource, and specifically, the energy-efficient data collection system based on joint optimization of trajectory and resource includes a scenario module, a power module, a data module, an optimization module, a solution module, an iteration module, an update module, and a collection module.
The scene module is used for constructing a scene of communication between a single unmanned aerial vehicle and the multi-ground Internet of things equipment;
the power module analyzes the advancing path of the unmanned aerial vehicle and data transmission analysis from the nodes to the unmanned aerial vehicle on the basis of the scene constructed by the scene module to obtain the propulsion power of the unmanned aerial vehicle and the power of the Internet of things;
the data module is used for establishing a model for carrying out virtual MIMO transmission on the unmanned aerial vehicle by using the transmitting antenna and the receiving antenna based on the nodes of the active clusters in the scene constructed by the scene module to obtain the minimum data volume transmitted by each cluster;
the optimization module introduces the energy efficiency of the single unmanned aerial vehicle-multiple Internet of things system, and designs a single unmanned aerial vehicle-multiple Internet of things system energy efficiency optimization model by utilizing the propulsion power of the unmanned aerial vehicle and the Internet of things power obtained by the power module and the minimum data volume transmitted by each cluster obtained by the data module 3;
the solution module is used for dividing the problem to be optimized into an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem, and solving the unmanned aerial vehicle track and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem by using a BCD algorithm;
the iteration module converts the elementary optimization problem of the single unmanned aerial vehicle-multi-Internet-of-things system energy efficiency optimization model expression obtained by the optimization module into a subtractive optimization problem and approaches the subtractive optimization problem by using a large system analysis and Dinkelbach method, and performs a double-loop iteration solving problem by solving the unmanned aerial vehicle track and scheduling problem, the Internet-of-things node power distribution problem and the auxiliary optimization problem in the module to obtain updated data volume and transmission energy;
the updating module is used for updating the energy efficiency of the single unmanned aerial vehicle-multiple Internet of things system according to the data volume and the transmission energy updated by the iteration module;
the collection module is used for repeatedly solving the module, the iteration module and the updating module based on the system energy efficiency updated by the updating module until the single unmanned aerial vehicle-multi-Internet of things system converges to obtain the maximized system energy efficiency, and the unmanned aerial vehicle is close to the Internet of things cluster to realize energy-efficient Internet of things transmission and further realize energy-efficient data collection.
According to the invention, the transmission energy efficiency of the Internet of things node cluster assisted by the unmanned aerial vehicle is researched, and meanwhile, various factors including Internet of things transmission energy, unmanned aerial vehicle propulsion energy, Internet of things communication service requirements, unmanned aerial vehicle flight path, Internet of things node-node communication scheduling and the like are considered, so that the energy efficiency can be improved to the maximum extent from the system perspective. In addition, the system life can be effectively prolonged by coordinating the energy consumption of the unmanned aerial vehicle and the Internet of things. Moreover, each internet of things node is considered independently in most of traditional research works, when the number of the internet of things nodes is increased, the expandability of the internet of things nodes is greatly challenged, and the influence caused by the increase of the number of the internet of things nodes can be effectively solved by the solution provided by the invention.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solution of the present invention is further described below with reference to experiments.
Referring to fig. 3, trajectories of drones with different scenarios and data volume thresholds are shown. Wherein the dotted line, the solid line and the dotted line respectively represent the conditions of the energy efficiency optimization bits, the energy efficiency optimization bits and the data amount optimization bits, and different colors on the trajectory represent different speeds. It can be seen that when the data volume threshold is low (case of dashed lines), only a few clusters, i.e. clusters 1,2, 6, 7 and 9, need to be accessed closely. The resulting path is sufficient to cover all clusters to meet the minimum amount of transmitted data. Therefore, the length of the flight path of the unmanned aerial vehicle can be greatly shortened, so that the propulsion energy is saved, and the energy efficiency of the system is improved. Meanwhile, as the minimum data amount increases (case of solid line), the drone needs to fly closer to the cluster. However, not all clusters need to be closely accessed, particularly those that are relatively isolated, i.e., clusters 5 and 10. In contrast, when maximizing the amount of data transmitted (case of dashed lines), drones tend to traverse all clusters to collect more data. Thus, the resulting trajectory has the longest length and time of flight.
Referring to fig. 4, the system energy efficiency and the performance in terms of the amount of data transmitted are compared against a threshold amount of data. In the above figure, as the data volume threshold increases, the energy efficiency of the system is lower, as higher thresholds may impose tighter constraints on the drone trajectory and the internet of things communication. Furthermore, the higher the weight of the propulsion energy, i.e. the greater χ, the lower the energy efficiency of the system due to the higher total energy consumption.
In contrast, for the case of maximizing the amount of data transmitted, since the drones tend to access all clusters, the limit on the minimum amount of data can be easily satisfied. The constraints are then invalidated during the optimization process, with no significant impact of the thresholds on system performance. For fig. 4(b), the amount of data transmitted is not affected by the threshold variation, similar to the case in fig. 4 (a). To maximize system energy efficiency, the drone must more closely visit the cluster as the threshold amount of data increases, collecting more data. In addition, the higher the weight of the propulsion energy, the lower the relative weight of the internet of things communication energy.
Therefore, the energy efficiency of the system becomes less sensitive to the energy consumption of the communication of the internet of things, which provides more space for the transmission of the internet of things, and the amount of data transmitted also becomes higher. The energy-efficient scheme collects a smaller amount of data than the case of maximizing the amount of transmitted data. This is typically due to a tradeoff between energy and throughput in wireless communications. In particular, in the problem we consider, the introduction of propulsion energy by drones leads to a more conservative flight path, i.e. geographically further away from the internet of things cluster, and therefore the amount of data transmitted will be less due to the deterioration of the channel conditions. Furthermore, the contemplated topology with some "isolated" clusters (e.g., cluster 10) exacerbates this effect.
In summary, the energy-efficient data collection method and system based on the joint optimization of the track and the resource of the invention have the following advantages:
1. a high-energy-efficiency data collection method based on combined optimization of tracks and resources in an unmanned aerial vehicle-assisted Internet of things introduces the energy efficiency of a system;
2. a high-energy-efficiency data collection method based on track and resource combined optimization in an unmanned aerial vehicle-assisted Internet of things is characterized in that the track of the unmanned aerial vehicle, the communication and the dispatching of the Internet of things are jointly optimized while the data volume of the Internet of things and the propulsion energy constraint of the unmanned aerial vehicle are considered, so that the energy efficiency of a system is improved;
3. a high-energy-efficiency data collection method based on combined optimization of tracks and resources in an unmanned aerial vehicle-assisted Internet of things is designed by utilizing a Dinkelbach method, Block Coordinate Descent (BCD) and Sequential Convex Approximation (SCA), and the effectiveness of the method is verified through a numerical result.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. The energy-efficient data collection method based on the joint optimization of the track and the resources is characterized by comprising the following steps of:
s1, constructing a scene of communication between the single unmanned aerial vehicle and the multi-ground Internet of things equipment;
s2, analyzing the advancing path of the unmanned aerial vehicle and analyzing data transmitted from the nodes to the unmanned aerial vehicle based on the scene constructed in the step S1 to obtain the propulsion power of the unmanned aerial vehicle and the power of the Internet of things;
s3, establishing a model for carrying out virtual MIMO transmission on the unmanned aerial vehicle by using the transmitting antenna and the receiving antenna based on the nodes of the active clusters in the scene constructed in the step S1, and obtaining the minimum data volume transmitted by each cluster;
s4, introducing energy efficiency of the single unmanned aerial vehicle-multi-Internet of things system, and designing an energy efficiency optimization model of the single unmanned aerial vehicle-multi-Internet of things system by using the propulsion power and the Internet of things power of the unmanned aerial vehicle obtained in the step S2 and the minimum data volume transmitted by each cluster obtained in the step S3;
s5, dividing the problem to be optimized into an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem, and solving the unmanned aerial vehicle track and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem by using a BCD algorithm;
s6, converting the primitive optimization problem of the single unmanned aerial vehicle-multiple Internet of things system energy efficiency optimization model expression obtained in the step S4 into a subtractive type and approaching the subtractive type by using a large system analysis and Dinkelbach method, and performing double-loop iteration solving on the unmanned aerial vehicle trajectory and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem in the step S5 to obtain updated data volume and transmission energy;
s7, updating the energy efficiency of the single unmanned aerial vehicle-multiple Internet of things system according to the data volume and the transmission energy updated in the step S6;
s8, repeating the steps S5 to S7 based on the system energy efficiency updated in the step S7 until the single unmanned aerial vehicle-multi-Internet of things system converges to obtain the maximum system energy efficiency, and enabling the unmanned aerial vehicle to approach the Internet of things cluster to achieve energy-efficient Internet of things transmission and further achieve energy-efficient data collection.
2. The method for energy-efficient data collection based on joint trajectory and resource optimization according to claim 1, wherein in step S2, the propulsion power P of the dronenComprises the following steps:
Pn＝P(Vn)
wherein, P (V)n) As a function of the flight speed;
power p of internet of thingsj,nComprises the following steps:
Figure FDA0003541114950000021
wherein p is0Is the circuit power, p, of the vehicle-mounted assembly supporting data transmissionji,nFor unmanned aerial vehicle at qnI cluster j consisting of I nodes total power consumption, IjIs a transmit antenna.
3. The method for energy-efficient data collection based on joint trajectory and resource optimization according to claim 1, wherein in step S3, the virtual MIMO transmission model is:
yj,n＝Hj,nxj,n+zj,n
wherein, yj,nIs made ofThe K-dimensional receiving signal on the man-machine,
Figure FDA0003541114950000022
representing the channel, xj,nRepresenting I from a node in cluster jjDimension transmission signal, zj,nRepresenting background noise.
4. The method for energy-efficient data collection based on joint trace and resource optimization according to claim 1, wherein in step S3, the minimum amount of data transmitted in each cluster
Figure FDA0003541114950000023
Comprises the following steps:
Figure FDA0003541114950000024
wherein, tauj,nFor the transmission time slot assigned to cluster j, Rj,nThe transmission rate of the cluster J to the unmanned aerial vehicle is that J forms J clusters for the nodes of a certain area on the ground, and N is a discrete path point of the advancing path of the unmanned aerial vehicle.
5. The trajectory and resource joint optimization-based energy-efficient data collection method according to claim 1, wherein in step S4, the single drone-multiple internet of things system energy efficiency optimization model is specifically:
Figure FDA0003541114950000025
s.t.q0＝qS,qN+1＝qE
tn≥0,
Figure FDA0003541114950000026
τj,n≥0,
Figure FDA0003541114950000027
Figure FDA0003541114950000028
||qn+1-qn||≤min{Δmax,Vmaxtn},
Figure FDA0003541114950000029
Figure FDA00035411149500000210
Figure FDA00035411149500000211
Figure FDA00035411149500000212
Figure FDA0003541114950000031
Figure FDA0003541114950000032
Figure FDA0003541114950000033
wherein p isji,nFor unmanned aerial vehicle at qnOf a cluster of i nodes, qnGround projection corresponding to the path of travel of the unmanned aerial vehicle, tnFor unmanned aerial vehicle on two adjacent air routesPoint qnAnd q isn+1Time taken to fly therebetween, τj,nFor the transmission time slot assigned to cluster j, Rj,nFor the transmission rate of cluster j to the unmanned aerial vehicle, xi is the energy efficiency of a single unmanned aerial vehicle-multiple Internet of things system, pj,nFor unmanned aerial vehicle at qnThe total power consumption of the cluster j, χ is a weighting factor, E is the propulsion energy consumption of the unmanned aerial vehicle in the whole flight process, q0Ground projection for unmanned aerial vehicle origin, qSTwo-dimensional ground projection for the starting point of the drone, qN+1Ground projection for unmanned aerial vehicle terminal point, qEFor the two-dimensional ground projection of the unmanned aerial vehicle, N is the discrete path point, delta, of the unmanned aerial vehicle's path of travelmaxMaximum distance, V, for unmanned aerial vehiclemaxMaximum speed of unmanned aerial vehicle, EpropIn order to limit the propulsion energy of the unmanned aerial vehicle,
Figure FDA0003541114950000034
is the maximum value of transmission power, I, when the nodes in a cluster j consisting of I nodes transmit data to the unmanned aerial vehiclejIn order to be a transmitting antenna or antennas,
Figure FDA0003541114950000035
for the minimum amount of data transmitted per cluster, PnFor unmanned plane by qnMove to qn+1Consumed propulsion power, omegaj,nAs an auxiliary variable, the number of variables,
Figure FDA0003541114950000036
in order to be able to measure the power of the noise,
Figure FDA0003541114950000037
is a diagonal matrix and K is a dimension.
6. The method for collecting energy-efficient data based on trajectory and resource joint optimization according to claim 1, wherein in step S5, the BCD algorithm is used to solve the unmanned aerial vehicle trajectory and scheduling problem, the internet of things node power allocation problem, and the auxiliary optimization problem in an iterative manner until a stagnation point is found, specifically:
s501, initializing iota ← 0, optimizing variables according to the initialization technique, the vector of the initialized variables being SOL (iota);
s502, iteration is carried out, namely iota ← iota +1, the iteration content is an optimization method of an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem, and a solution vector in the current iteration is marked as SOL (iota);
and S503, repeating the step S502 until a convergence condition | SOL (iota) -SOL (iota-1) | < epsilon, wherein epsilon is a threshold value of a predefined termination algorithm, and obtaining a solution of the problem that the target function is subtraction.
7. The trajectory and resource joint optimization-based energy-efficient data collection method of claim 6, wherein the unmanned aerial vehicle trajectory and scheduling problem is:
Figure FDA0003541114950000041
the problem of node power distribution of the Internet of things is as follows:
Figure FDA0003541114950000042
the auxiliary optimization problems are as follows:
Figure FDA0003541114950000043
wherein the content of the first and second substances,
Figure FDA0003541114950000044
for the specific value of the auxiliary variable, zj,nAs an auxiliary variable, ynAs a relaxation variable, qnGround projection corresponding to the path of travel of the unmanned aerial vehicle, tnFor unmanned aerial vehicle at two adjacent waypoints qnAnd q isn+1Time spent flying in between, pji,nFor unmanned aerial vehicle at qnOf a cluster j consisting of i nodes, the total power consumption, τj,nFor the transmission time slot allocated to cluster j, p0The circuit power for supporting data transmission of the vehicle-mounted component is zeta, xi is energy efficiency of a single unmanned aerial vehicle-multiple Internet of things system, chi is a weighting factor, E is propulsion energy consumption of the unmanned aerial vehicle in the whole flight process, N is a discrete path point of an advancing path of the unmanned aerial vehicle, and IjBeing transmitting antennas, omegaj,nAs an auxiliary variable, the number of variables,
Figure FDA0003541114950000045
in order to be able to measure the power of the noise,
Figure FDA0003541114950000046
is a diagonal matrix and K is a dimension.
8. The energy-efficient data collection method based on trajectory and resource joint optimization according to claim 1, wherein in step S6, the Dinkelbach method specifically includes:
s601, initializing iota ← 0, ξ (iota) ← 0, initializing optimized variables according to the technique, and calculating the amount of data transferred and the energy consumed, respectively, the results being denoted by D (iota) and E (iota), respectively, iota representing iteration;
s602, replacing xi (iota) in the objective function in the subtraction form with xi (iota-1), and solving by using a method for solving an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem;
s603, updating the transmission data volume and the energy by using the transmission data volume and the energy calculated in the step S601 respectively;
s604, updating the system energy efficiency by using the updated transmission data amount and energy, namely ξ (iota) ← D (iota)/E (iota);
s605, repeating the steps till
Figure FDA0003541114950000047
Figure FDA0003541114950000048
Is a threshold value of a predefined termination algorithm.
9. The trajectory and resource joint optimization based energy-efficient data collection method of claim 1, wherein in step S7, the system energy efficiency ξ is:
Figure FDA0003541114950000051
wherein x is a weighting factor, D is total data transmitted by the single unmanned aerial vehicle-multiple Internet of things system, E is total energy consumed by the single unmanned aerial vehicle-multiple Internet of things system, and P is total energy consumed by the single unmanned aerial vehicle-multiple Internet of things systemnFor unmanned plane by qnMove to qn+1Consumed propulsive power, tnFor unmanned aerial vehicles at two adjacent waypoints qnAnd q isn+1Time spent flying in between, tauj,nFor the transmission time slot assigned to cluster j, Rj,nThe transmission rate of the cluster J to the unmanned aerial vehicle is shown, N is a discrete path point of a traveling path of the unmanned aerial vehicle, and J is a node of a certain area on the ground to form J clusters.
10. An energy-efficient data collection system based on joint optimization of trajectories and resources, comprising:
the scene module is used for constructing a scene of communication between the single unmanned aerial vehicle and the multi-ground Internet of things equipment;
the power module analyzes the advancing path of the unmanned aerial vehicle and data transmission analysis from the nodes to the unmanned aerial vehicle on the basis of the scene constructed by the scene module to obtain the propulsion power of the unmanned aerial vehicle and the power of the Internet of things;
the data module is used for establishing a model for carrying out virtual MIMO transmission on the unmanned aerial vehicle by using the transmitting antenna and the receiving antenna based on the nodes of the active clusters in the scene constructed by the scene module to obtain the minimum data volume transmitted by each cluster;
the optimization module introduces the energy efficiency of the single unmanned aerial vehicle-multiple Internet of things system, and designs a single unmanned aerial vehicle-multiple Internet of things system energy efficiency optimization model by utilizing the propulsion power of the unmanned aerial vehicle and the Internet of things power obtained by the power module and the minimum data volume transmitted by each cluster obtained by the data module 3;
the solution module is used for dividing the problem to be optimized into an unmanned aerial vehicle track and scheduling problem, an Internet of things node power distribution problem and an auxiliary optimization problem, and solving the unmanned aerial vehicle track and scheduling problem, the Internet of things node power distribution problem and the auxiliary optimization problem by using a BCD algorithm;
the iteration module converts the elementary optimization problem of the single unmanned aerial vehicle-multi-Internet-of-things system energy efficiency optimization model expression obtained by the optimization module into a subtractive optimization problem and approaches the subtractive optimization problem by using a large system analysis and Dinkelbach method, and performs a double-loop iteration solving problem by solving the unmanned aerial vehicle track and scheduling problem, the Internet-of-things node power distribution problem and the auxiliary optimization problem in the module to obtain updated data volume and transmission energy;
the updating module is used for updating the energy efficiency of the single unmanned aerial vehicle-multiple Internet of things system according to the data volume and the transmission energy updated by the iteration module;
the collection module is used for repeatedly solving the module, the iteration module and the updating module based on the system energy efficiency updated by the updating module until the single unmanned aerial vehicle-multi-Internet of things system is converged to obtain the maximized system energy efficiency, and enabling the unmanned aerial vehicle to approach the Internet of things cluster so as to realize energy-efficient Internet of things transmission and further realize energy-efficient data collection.
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