CN112866368B - Air-ground remote Internet of things design method and system - Google Patents
Air-ground remote Internet of things design method and system Download PDFInfo
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Abstract
The invention provides a method and a system for designing an air-space-ground remote Internet of things, which comprises the following steps: acquiring a space layer slice model, a space layer slice model and a ground layer slice model, and presetting service scene constraint conditions of the remote Internet of things; constructing a network operator profit model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint conditions of the remote Internet of things; and solving an optimal solution for the network operator profit model to obtain a maximized operator profit value of the network operator profit model. The invention integrally improves the utilization rate of network resources and the intelligent degree of dynamic resource scheduling by the transverse slice and the longitudinal slices of different service types based on the spatial layer, the spatial layer and the ground layer.
Description
Technical Field
The invention relates to the technical field of communication networks, in particular to a method and a system for designing an air-space-ground remote Internet of things.
Background
In recent years, as the related technologies mature, more and more new internet of things applications are produced. Meanwhile, Unmanned Aerial Vehicles (UAVs) are widely used in many fields. The UAV can provide services such as data acquisition, target identification and temporary communication for equipment of the Internet of things, and has the advantages of flexible deployment, wide application range, low cost and the like. Meanwhile, under the condition that the infrastructure such as a base station is limited or even unavailable, the UAV assisted edge computing can provide unloading opportunities for the equipment of the Internet of things and can be deployed in a place close to the equipment so as to save the energy of the equipment and provide low-delay service.
The satellite network has wide coverage range, can provide internet services for islands, remote mountain areas and disaster areas, and the aerial network can provide services with higher requirements by virtue of the characteristic of high flexibility. In order to cover a wider range and extend human activities to Space and deep Space, scholars have proposed Space-Air-group (SAG) integrated networks, seamlessly integrated satellite systems, Air networks, and terrestrial communications. It has become an emerging attractive research topic over the past decade. The SAG network is a large capacity information network capable of acquiring, processing and efficiently transmitting information. Compared with the traditional network, the SAG network has the inherent advantages of wide coverage and large capacity, and can be applied to a plurality of fields such as national defense tasks, intelligent power grids, emergency rescue, environmental protection and the like.
The Internet of Remote Things (IoRT) is one of emerging applications of the Internet of Things, and provides diversified services for Remote areas where ground infrastructure is scarce. The SAG network architecture is gradually becoming a potential solution for supporting remote internet of things. With the increase of business diversity, SAG IoRT needs to support vertical industries with different requirements, including different fields of smart agriculture, smart grid, environmental protection agency, medical search and rescue, and the like. However, because of the complex and varied features of network heterogeneity, self-organization and time-varying property, the SAG network still faces many challenges that have not been achieved before. Different from the traditional ground network, the SAG network has the advantages of strong node mobility, variable network topology, large network difference, complex communication resources and deficient time domain, frequency domain and space domain resources. Meanwhile, the SAG network has a plurality of users, wide service types and different application requirements. Therefore, considering the SAG mobility, the time delay problem and the influence of the resource energy limitation on the communication performance of the whole system, it is crucial to dynamically and reasonably allocate the SAG network resources.
Network Slicing (NS) is a key technology of 5G, and a shared physical Network is divided into a plurality of End-to-End (E2E) logically isolated virtual networks, each logical Network can provide an on-demand highly customizable service for the air-ground remote internet of things, and the logical networks are isolated from each other, managed independently, and created as required.
Disclosure of Invention
The invention provides a method and a system for designing an air-space-ground remote Internet of things, which are used for overcoming the defects in the prior art.
In a first aspect, the invention provides a method for designing an air-space-ground remote internet of things, which comprises the following steps:
acquiring a space layer slice model, a space layer slice model and a ground layer slice model, and presetting service scene constraint conditions of the remote Internet of things;
constructing a network operator profit model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint conditions of the remote Internet of things;
and solving an optimal solution for the network operator profit model to obtain a maximized operator profit value of the network operator profit model.
In one embodiment, the acquiring a spatial layer slice model, an aerial layer slice model, a ground layer slice model, and a preset service scene constraint condition of a remote internet of things specifically includes:
respectively acquiring an equipment set constructed by a plurality of pieces of equipment, a UAV set constructed by a plurality of UAVs and a LEO satellite;
acquiring the calculation task amount and the local calculation CPU cycle frequency of any equipment in the equipment set, dividing the calculation task amount into a local calculation task amount processed on the equipment, an edge calculation amount processed on the UAV and a cloud calculation task amount processed on the LEO satellite, and defining an equipment connection state variable according to whether any equipment is connected with any UAV;
obtaining position coordinates of any device, position coordinates of any UAV, an initial reference channel gain, a distance from any device to any UAV and a LEO satellite altitude, and obtaining a first free space path loss model from any device to any UAV and a second free space path loss model from any device to the LEO satellite based on the position coordinates of any device, the position coordinates of any UAV and the initial reference channel gain;
defining a first transmission power, a channel bandwidth, and a device noise power of any device to any UAV, based on the first transmission power, the channel bandwidth, the device noise power, and deriving a first total data rate of the any device to the any UAV;
defining a second transmission power of any device to the LEO satellite, and obtaining a second total data rate of the any device to the LEO satellite based on the second transmission power, the channel bandwidth, the device noise power and the second free space path loss model;
obtaining a first mission delivery time of any device to any UAV based on the edge calculated amount and the first total data rate, and obtaining a second mission delivery time of any device to the LEO satellite based on the cloud calculated mission amount and the second total data rate;
obtaining total energy consumption for delivering any equipment to a first equipment of any UAV (unmanned aerial vehicle) based on the equipment connection state variable, the first transmission power and the first mission delivery time, and obtaining total energy consumption for delivering any equipment to a second equipment of the LEO satellite based on the second transmission power and the second mission delivery time;
acquiring equipment data calculation intensity, and obtaining calculation processing time of any equipment based on the equipment data calculation intensity, the local calculation task amount and the local calculation CPU cycle frequency;
acquiring effective switching capacity of equipment, and acquiring computing processing energy consumption of any equipment based on the effective switching capacity of the equipment, the local computing task amount and the computing processing time of any equipment;
acquiring the CPU cycle frequency of an MEC server of any UAV and the calculation intensity of any UAV data, and obtaining the calculation processing time of any UAV based on the CPU cycle frequency of the MEC server, the calculation intensity of any UAV data and the edge calculation amount;
determining a preset maximum time delay threshold value in an intelligent inspection task scene, so that the maximum value among the calculated processing time of any equipment, the sum of the first task delivery time and all UAV calculated processing time and the second task delivery time is not greater than the preset maximum time delay threshold value;
determining a preset maximum energy consumption threshold in a video monitoring task scene, so that the sum of total data delivery energy consumption of the first equipment, total data delivery energy consumption of the second equipment and calculated processing energy consumption of any equipment is not greater than the preset maximum energy consumption threshold.
In one embodiment, the constructing a network operator revenue model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint condition of the remote internet of things specifically includes:
acquiring a device-to-UAV spectrum resource unit price, a device-to-LEO satellite spectrum resource unit price, a unit task edge unit price and cloud computing fixed cost, and obtaining a network operator gain based on the device-to-UAV spectrum resource unit price, the device-to-LEO satellite spectrum resource unit price, the unit task edge unit price, the cloud computing fixed cost, the first total data rate, the second total data rate, the device connection state variable, the MEC server CPU cycle frequency and the computing task amount;
obtaining a first infrastructure payment spectrum unit price, a second infrastructure payment spectrum unit price, a payment backhaul link unit price and a calculation unit price, and obtaining network operator expenditure revenue based on the first infrastructure payment spectrum unit price, the second infrastructure payment spectrum unit price, the payment backhaul link unit price, the calculation unit price, the device connection state variable, the channel bandwidth, the second total data rate and the MEC server CPU cycle frequency;
and obtaining the network operator revenue model by the network operator revenue and the network operator expenditure revenue.
In an embodiment, the obtaining a maximum operator revenue value of the network operator revenue model by solving an optimal solution for the network operator revenue model specifically includes:
optimizing the equipment connection state variable, the first transmission power, the second transmission power, the CPU cycle frequency of the MEC server and UAV deployment to obtain an optimal solution set;
and iteratively updating the network operator profit model based on the optimal solution set until a preset convergence condition is met, outputting an updated optimal solution set, and obtaining the maximized operator profit value from the updated optimal solution set.
In an embodiment, the optimizing the device connection state variable, the first transmission power, the second transmission power, the MEC server CPU cycle frequency, and the UAV deployment to obtain an optimal solution set specifically includes:
acquiring a first preset constraint condition based on the maximum transmission power of the equipment, a second preset constraint condition based on the connection constraint of the equipment, a third preset constraint condition based on the preset maximum time delay threshold, a fourth preset constraint condition based on the preset maximum energy consumption threshold, a fifth preset constraint condition based on the total computing capacity of UAV distribution equipment and a sixth preset constraint condition based on the connection state variable of the equipment;
solving the optimal solution set corresponding to the network operator revenue model maximization based on the first preset constraint condition, the second preset constraint condition, the third preset constraint condition, the fourth preset constraint condition, the fifth preset constraint condition and the sixth preset constraint condition.
In an embodiment, the solving the optimal solution set corresponding to the network operator revenue model maximization based on the first preset constraint, the second preset constraint, the third preset constraint, the fourth preset constraint, the fifth preset constraint and the sixth preset constraint further includes:
converting the sixth preset constraint condition into a continuous variable to obtain an updated sixth preset constraint condition;
acquiring an auxiliary variable based on the equipment connection state variable and the first transmission power, and substituting the auxiliary variable and the updated sixth preset constraint condition into the network operator revenue model to obtain an updated network operator revenue model;
and solving the updated network operator revenue model by adopting a preset standard convex optimization solver to obtain an equipment connection state variable optimal solution and a first transmission power optimal solution.
In an embodiment, the solving the updated revenue model of the network operator by using a pre-set standard convex optimization solver to obtain an optimal solution of the device connection state variable and an optimal solution of the first transmission power, and then the method further includes:
and enabling the equipment connection state variable, the first transmission power, the MEC server CPU periodic frequency and the UAV deployment to be constant values, and solving the updated network operator revenue model to obtain a second transmission power optimal solution.
In one embodiment, the making the device connection state variable, the first transmission power, the MEC server CPU cycle frequency, and the UAV deployment constant value, solving the updated network operator revenue model to obtain a second transmission power optimal solution, further includes:
acquiring a non-negative relaxation variable and an additional constraint condition based on the non-negative relaxation variable;
substituting the non-negative relaxation variables and the additional constraint conditions into the updated network operator revenue model, and solving based on the preset standard convex optimization solver to obtain an MEC server CPU cycle frequency optimal solution and an UAV deployment optimal solution.
In a second aspect, the present invention further provides an air-space-ground remote internet of things design system, including:
the acquisition module is used for acquiring a spatial layer slice model, a spatial layer slice model and a ground layer slice model, and presetting service scene constraint conditions of the remote Internet of things;
the construction module is used for constructing a network operator profit model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint conditions of the remote Internet of things;
and the optimization module is used for solving an optimal solution for the network operator profit model to obtain a maximum operator profit value of the network operator profit model.
In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of any one of the methods for designing an internet of things over the air and in the space.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the space-ground remote internet of things design method as described in any one of the above.
According to the air-space-ground remote Internet of things design method and system, the utilization rate of network resources and the intelligent degree of dynamic resource scheduling are integrally improved through the transverse slice formed based on the space layer, the space layer and the ground layer and the longitudinal slices of different service types.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a design method of an air-space-ground remote Internet of things provided by the invention;
FIG. 2 is a schematic diagram of an aerospace-ground remote Internet of things system architecture based on network slices provided by the invention;
FIG. 3 is a schematic structural diagram of a space-ground remote Internet of things design system provided by the invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but 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.
Aiming at the technical challenges in the prior art, the invention provides an air-space-ground remote Internet of things design method, which is based on a network slice and aims to better provide support for various scenes such as military drilling, smart factories, forest fire prevention and smart power grids in IoRT, meet the abundant, unique and customized service requirements of an air-space-ground network and finally realize the profit maximization based on a mobile virtual network operator.
Fig. 1 is a schematic flow chart of a design method of a space-ground remote internet of things provided by the present invention, as shown in fig. 1, including:
s1, acquiring a space layer slice model, a space layer slice model and a ground layer slice model, and presetting service scene constraint conditions of the remote Internet of things;
specifically, the air-space-ground remote internet of things architecture based on network slices, as shown in fig. 2, includes horizontal slices composed of a space layer, and a ground layer, and vertical slices of different service types, where a typical application scenario in the SAG IoRT system, namely, a smart grid, is considered, and two types of slice requests, namely, a smart patrol task slice request and a video monitoring task slice request, are considered.
S2, constructing a network operator profit model based on the spatial layer slice model, the ground layer slice model and the preset service scene constraint conditions of the remote Internet of things;
specifically, for a transverse slice comprising a space layer, a space layer and a ground layer, a plurality of parameter sets comprising a satellite, a UAV and ground equipment are obtained, for the transverse slice comprising an intelligent inspection task slice request and a video monitoring task slice request, different constraint condition limits are carried out according to the characteristics and requirements of respective services, and a network operator revenue model is constructed by the parameter sets and the constraint conditions.
And S3, solving an optimal solution for the network operator profit model to obtain a maximized operator profit value of the network operator profit model.
Specifically, the constructed network operator profit model is solved by the optimal solution, and the maximized operator profit value is obtained by the output optimal solution under the condition of meeting a certain convergence condition.
The invention integrally improves the utilization rate of network resources and the intelligent degree of dynamic resource scheduling by the transverse slice and the longitudinal slices of different service types based on the spatial layer, the spatial layer and the ground layer.
Based on the above embodiment, step S1 in the method specifically includes:
respectively acquiring an equipment set constructed by a plurality of pieces of equipment, a UAV set constructed by a plurality of UAVs and a LEO satellite;
acquiring the calculation task amount and the local calculation CPU cycle frequency of any equipment in the equipment set, dividing the calculation task amount into a local calculation task amount processed on the equipment, an edge calculation amount processed on the UAV and a cloud calculation task amount processed on the LEO satellite, and defining an equipment connection state variable according to whether any equipment is connected with any UAV;
obtaining position coordinates of any device, position coordinates of any UAV, an initial reference channel gain, a distance from any device to any UAV and a LEO satellite altitude, and obtaining a first free space path loss model from any device to any UAV and a second free space path loss model from any device to the LEO satellite based on the position coordinates of any device, the position coordinates of any UAV and the initial reference channel gain;
defining a first transmission power, a channel bandwidth, and a device noise power of any device to any UAV, based on the first transmission power, the channel bandwidth, the device noise power, and deriving a first total data rate of the any device to the any UAV;
defining a second transmission power of any device to the LEO satellite, and obtaining a second total data rate of the any device to the LEO satellite based on the second transmission power, the channel bandwidth, the device noise power and the second free space path loss model;
obtaining a first mission delivery time of any device to any UAV based on the edge calculated amount and the first total data rate, and obtaining a second mission delivery time of any device to the LEO satellite based on the cloud calculated mission amount and the second total data rate;
obtaining total energy consumption for delivering any equipment to a first equipment of any UAV (unmanned aerial vehicle) based on the equipment connection state variable, the first transmission power and the first mission delivery time, and obtaining total energy consumption for delivering any equipment to a second equipment of the LEO satellite based on the second transmission power and the second mission delivery time;
acquiring equipment data calculation intensity, and obtaining calculation processing time of any equipment based on the equipment data calculation intensity, the local calculation task amount and the local calculation CPU cycle frequency;
acquiring effective switching capacity of equipment, and acquiring computing processing energy consumption of any equipment based on the effective switching capacity of the equipment, the local computing task amount and the computing processing time of any equipment;
acquiring the CPU cycle frequency of an MEC server of any UAV and the calculation intensity of any UAV data, and obtaining the calculation processing time of any UAV based on the CPU cycle frequency of the MEC server, the calculation intensity of any UAV data and the edge calculation amount;
determining a preset maximum time delay threshold value in an intelligent inspection task scene, so that the maximum value among the calculated processing time of any equipment, the sum of the first task delivery time and all UAV calculated processing time and the second task delivery time is not greater than the preset maximum time delay threshold value;
determining a preset maximum energy consumption threshold in a video monitoring task scene, so that the sum of total data delivery energy consumption of the first equipment, total data delivery energy consumption of the second equipment and calculated processing energy consumption of any equipment is not greater than the preset maximum energy consumption threshold.
Specifically, in a network infrastructure layer of the SAG IoRT system, resources such as communication, calculation, power and the like provided by a satellite, a UAV and ground equipment are virtualized and completely isolated, and are distributed to corresponding network slices as required, so that the purpose of customizing resource distribution is achieved. As shown in fig. 2, in the SAG IoRT slicing network, K devices, M UAVs and one LEO (Low Earth Orbit) satellite are included. Let K be {1, …, K },
respectively representing the collection of devices and UAVs. Each UAV m is equipped with an MEC (mobile edge Computing) server and provides Computing services for the device, and the LEO provides cloud Computing services for the device. Considering the SAG three-layer structure, the invention provides a space layer, space layer and ground layer slice model. In consideration of the diversity of IoRT network services, the invention takes two typical services of an intelligent power grid as an example, namely: 1) the intelligent inspection is a delay sensitive task. 2) Video monitoring has strict requirements on energy consumption.
Definition of
For a set of devices, the task for each device can be represented as
Wherein
Representing the CPU cycle frequency, O, of the device k local calculationkIs the amount of tasks that need to be calculated in the task. In consideration of low computing capacity and limited capability of equipment, the method adopts a partial unloading mode to perform auxiliary computing. That is to say a part α of the task for each devicekPerform a local calculation of betakUnloading to UAV and performing edge calculation, and finally thetak＝1-αk-βkAnd partial unloading is carried out on an LEO for cloud computing. Defining binaryVariables of
Meaning that device k is connected to UAV m, otherwise,
in particular, the invention considers the pilot frequency of the UAV and the LEO, and the frequency range is divided between the UAVs, and the channels are orthogonal, namely, no interference exists between the devices.
For simplicity, it is assumed that the communication between the UAV and the device is considered to be line of sight (LoS) transmission, without small-scale fading. Define the horizontal coordinate of device k as
UAV m position is
Thus, the channel power gain of the uplink device k to UAV m follows the free-space path loss model as:
similarly, the channel power gain of device k to satellite follows the free space path loss model as:
β0denotes the reference channel gain when d is 1m, dk,mRepresents the distance from device k to UAV m, | | | | | is the Euclidean norm, HsRepresenting the height of the LEO in m.
In the model of the invention, only two services are considered: defining a set of devices for performing intelligent routing inspection as
The equipment set for performing video monitoring is
Suppose a device has access to at most one UAV, i.e.:
definition of
For device k to UAV m, the total data rate for device k to transmit to UAV m is:
wherein w is the channel bandwidth; sigma2Representing the noise power of the device.
Accordingly, the total transmission rate is given separately:
definition of
For device k to satellite transmission power, the total data rate delivered by device k to satellite transmission is:
wherein w is the channel bandwidth; sigma2Representing the noise power of the device.
The mission delivery to UAV and satellite times for device k are:
the respective energy consumption of the total data volume delivered by the equipment is as follows:
order to
The strength (in cycles/bit, the number of CPU cycles needed to compute a unit of data) is calculated for the data at the device, then the computation processing time at the device is:
the energy consumption consumed by the calculation processing of the device k is as follows:
where κ denotes the effective handover capacity.
Order to
Representing the CPU cycle frequency of the MEC server at the UAV,
computing the intensity for the data at the UAV, then computing the processing time at the UAV accordinglyComprises the following steps:
because the data volume of the calculation result is much smaller than the delivery total data, the delay caused by returning the result to the equipment k is negligible, and compared with the internet of things equipment and the edge server, the cloud calculation has higher calculation capacity and ignores the time delay of calculation and transmission of the calculation result.
Further, different types of slices are described and constrained:
slicing 1: intelligent inspection task
The intelligent inspection task is very sensitive to time delay, and the transmission and calculation of information are very important. To meet the SLA requirements for slice 1, the following constraints are required:
wherein
The maximum delay requirement for slice 1.
And (3) slicing 2: video monitoring task
The video monitoring task has strict requirements on energy consumption, and in order to meet the SLA requirement of slice 2, the following limiting conditions are required:
wherein
The maximum energy consumption requirement for slice 2.
Based on any of the above embodiments, step S2 in the method specifically includes:
acquiring a device-to-UAV spectrum resource unit price, a device-to-LEO satellite spectrum resource unit price, a unit task edge unit price and cloud computing fixed cost, and obtaining a network operator gain based on the device-to-UAV spectrum resource unit price, the device-to-LEO satellite spectrum resource unit price, the unit task edge unit price, the cloud computing fixed cost, the first total data rate, the second total data rate, the device connection state variable, the MEC server CPU cycle frequency and the computing task amount;
obtaining a first infrastructure payment spectrum unit price, a second infrastructure payment spectrum unit price, a payment backhaul link unit price and a calculation unit price, and obtaining network operator expenditure revenue based on the first infrastructure payment spectrum unit price, the second infrastructure payment spectrum unit price, the payment backhaul link unit price, the calculation unit price, the device connection state variable, the channel bandwidth, the second total data rate and the MEC server CPU cycle frequency;
and obtaining the network operator revenue model by the network operator revenue and the network operator expenditure revenue.
In particular, the aim of the invention is to connect by optimizing the equipment
Transmission power
CPU cycle frequency allocation
And UAV deployment qmTo maximize operator revenue. On the one hand, MVNOs (virtual Network operators) obtain revenue by charging slicing equipment:
wherein the MVNO charges mu for spectrum resources utilized by the device-to-UAV communicationkunits/Mbps charging σ for spectrum resources utilized by device-to-satellite communicationskunits/Mbps。λkIndicates the unit price, eta, charged by MVNO for the calculation of the unit task edgekMeaning that the MVNO charges a fixed fee for cloud computing.
MVNOs, on the other hand, pay a spectral fee δ for InP (infrastructure Providers)ka/Hz and
payment backhaul link cost ξk/bps and calculate cost upsilonkin/Hz. It can therefore be expressed as:
thus, the total utility of MVNOs is:
based on any of the above embodiments, step S3 in the method specifically includes:
optimizing the equipment connection state variable, the first transmission power, the second transmission power, the CPU cycle frequency of the MEC server and UAV deployment to obtain an optimal solution set;
and iteratively updating the network operator profit model based on the optimal solution set until a preset convergence condition is met, outputting an updated optimal solution set, and obtaining the maximized operator profit value from the updated optimal solution set.
Specifically, the optimization problem P1 obtained in the foregoing embodiment is used to connect the optimization devices therein
Transmission power
CPU cycle frequency allocation
And UAV deployment qmSolving the optimal solutions, substituting the optimal solutions into a formula (18), and initializing Utility(0)Let the iteration number l equal to 1, update the Utility according to the formula (18)(l)Let l be l +1 until the convergence criterion (Utility) is satisfied(l)-Utility(l-1))/Utility(l-1)≤10-4And outputting the optimal solution set, and outputting a corresponding maximum operator profit value by the optimal solution set.
Based on any of the above embodiments, the optimizing the device connection state variable, the first transmission power, the second transmission power, the MEC server CPU cycle frequency, and the UAV deployment to obtain an optimal solution set specifically includes:
acquiring a first preset constraint condition based on the maximum transmission power of the equipment, a second preset constraint condition based on the connection constraint of the equipment, a third preset constraint condition based on the preset maximum time delay threshold, a fourth preset constraint condition based on the preset maximum energy consumption threshold, a fifth preset constraint condition based on the total computing capacity of UAV distribution equipment and a sixth preset constraint condition based on the connection state variable of the equipment;
solving the optimal solution set corresponding to the network operator revenue model maximization based on the first preset constraint condition, the second preset constraint condition, the third preset constraint condition, the fourth preset constraint condition, the fifth preset constraint condition and the sixth preset constraint condition.
Specifically, the overall model is represented as an optimization problem P1:
wherein, each constraint condition is respectively as follows: c1 guarantees the maximum transmission power of the device; c2 is a device connection constraint; c3 and C4 represent latency and power consumption constraints for slice 1 and slice 2 devices; c5 means that the computing resources allocated to the device cannot exceed the total computing power of the UAV. Since the device transmit power and UAV position are coupled and the objective function is not convex, problem P1 is not a convex optimization problem.
Based on any one of the above embodiments, the solving the optimal solution set corresponding to the network operator revenue model maximization based on the first preset constraint condition, the second preset constraint condition, the third preset constraint condition, the fourth preset constraint condition, the fifth preset constraint condition, and the sixth preset constraint condition further includes:
converting the sixth preset constraint condition into a continuous variable to obtain an updated sixth preset constraint condition;
acquiring an auxiliary variable based on the equipment connection state variable and the first transmission power, and substituting the auxiliary variable and the updated sixth preset constraint condition into the network operator revenue model to obtain an updated network operator revenue model;
and solving the updated network operator revenue model by adopting a preset standard convex optimization solver to obtain an equipment connection state variable optimal solution and a first transmission power optimal solution.
Specifically, on the basis of the above embodiment, since P1 is not a convex optimization problem, in order to make the problem P1 easier to handle, the present invention first relaxes the binary variable in C6 into a continuous variable, resulting in the following problem:
P2：
here, the problem P2, although relaxed, is still a non-convex optimization problem due to the objective function, the constraints C3 and C4 being non-convex. Next, an efficient iterative algorithm for solving the relaxation problem P2 needs to be provided by using a successive convex approximation technique.
Given CPU cycleFrequency allocation
Transmission power
And UAV deployment qmThe device connection of problem P2 may be optimized by solving the following problem
Transmission power
Introducing auxiliary variables
Equation (4) can be expressed as
Due to the fact that
Is the perspective function of the function f (x) log x, thus preserving the concave-function property of the primitive function f (x), so the primitive problem translates into:
P3：
therefore, P3 is a convex optimization problem, which can be solved effectively by using a standard convex optimization solver such as CVX (continuously variable X) to obtain the equipment connection
And transmission power
The optimal solution of (1).
Based on any one of the above embodiments, the convex optimization solver pair based on the preset standard is adopted
Solving the new network operator revenue model to obtain an optimal solution of the equipment connection state variable and an optimal solution of the first transmission power, and then:
and enabling the equipment connection state variable, the first transmission power, the MEC server CPU periodic frequency and the UAV deployment to be constant values, and solving the updated network operator revenue model to obtain a second transmission power optimal solution.
In particular, given a fixed equipment connection
Transmission power
CPU cycle frequency allocation
And UAV deployment qmThe transmission power of the problem P2 can be optimized by solving the following problem
P4：
It can be seen that P4 is a convex optimization problem that can be effectively solved by CVX as well, resulting in transmission power
The optimal solution of (1).
Based on any of the above embodiments, the making the device connection state variable, the first transmission power, the MEC server CPU cycle frequency, and the UAV deployment to be constant values, solving the updated network operator revenue model to obtain a second transmission power optimal solution, and then further including:
acquiring a non-negative relaxation variable and an additional constraint condition based on the non-negative relaxation variable;
substituting the non-negative relaxation variables and the additional constraint conditions into the updated network operator revenue model, and solving based on the preset standard convex optimization solver to obtain an MEC server CPU cycle frequency optimal solution and an UAV deployment optimal solution.
Specifically, the final CPU cycle frequency assignment
And UAV deployment qmPerforming combined optimization to convert the original problem P2 into:
P5：
s.t.C1:
C2:
C3:
due to the variable concavity and convexity of C1 and C2 and the non-convexity of the P5 objective function, the invention converts the above formula by using a variable substitution method. In detail, non-negative relaxation variables are introduced
And add additional constraints
Rewrite constraints C1 and C2:
therefore, the objective function of the maximization problem P5 is equivalent to maximizing its lower bound, i.e., the original problem can be translated into P6:
P6：
wherein f islb(qm；qm r) Is that
Linear lower bound of (2), definition
The trajectory of UAV m at the jth iteration is
Therefore, P6 is a convex optimization problem, which can be solved effectively by using a standard convex optimization solver such as CVX (continuously variable X) to obtain the CPU periodic frequency distribution
And UAV deployment qmThe optimal solution of (1).
The air-space-ground remote internet of things design system provided by the invention is described below, and the air-space-ground remote internet of things design system described below and the air-space-ground remote internet of things design system described above can be referred to correspondingly.
Fig. 3 is a schematic structural diagram of a space-ground remote internet of things design system provided by the present invention, as shown in fig. 3, including: an acquisition module 31, a construction module 32 and an optimization module 33; wherein:
the obtaining module 31 is configured to obtain a spatial layer slice model, an aerial layer slice model, a ground layer slice model, and a preset service scene constraint condition of the remote internet of things; the construction module 32 is configured to construct a network operator revenue model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint condition of the remote internet of things; the optimization module 33 is configured to obtain a maximum operator revenue value of the network operator revenue model by solving an optimal solution for the network operator revenue model.
The invention integrally improves the utilization rate of network resources and the intelligent degree of dynamic resource scheduling by the transverse slice and the longitudinal slices of different service types based on the spatial layer, the spatial layer and the ground layer.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)410, a communication interface (communication interface)420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 are communicated with each other via the communication bus 440. Processor 410 may invoke logic instructions in memory 430 to perform a space-ground remote internet of things design method, the method comprising: acquiring a space layer slice model, a space layer slice model and a ground layer slice model, and presetting service scene constraint conditions of the remote Internet of things; constructing a network operator profit model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint conditions of the remote Internet of things; and solving an optimal solution for the network operator profit model to obtain a maximized operator profit value of the network operator profit model.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer being capable of executing the space-sky-ground remote internet of things design method provided by the above methods, the method including: acquiring a space layer slice model, a space layer slice model and a ground layer slice model, and presetting service scene constraint conditions of the remote Internet of things; constructing a network operator profit model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint conditions of the remote Internet of things; and solving an optimal solution for the network operator profit model to obtain a maximized operator profit value of the network operator profit model.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the space-ground remote internet of things design method provided above, the method including: acquiring a space layer slice model, a space layer slice model and a ground layer slice model, and presetting service scene constraint conditions of the remote Internet of things; constructing a network operator profit model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint conditions of the remote Internet of things; and solving an optimal solution for the network operator profit model to obtain a maximized operator profit value of the network operator profit model.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A method for designing an air-space-ground remote Internet of things is characterized by comprising the following steps:
acquiring a space layer slice model, a space layer slice model and a ground layer slice model, and presetting service scene constraint conditions of the remote Internet of things;
constructing a network operator profit model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint conditions of the remote Internet of things;
solving an optimal solution for the network operator profit model to obtain a maximized operator profit value of the network operator profit model;
the acquiring of the spatial layer slice model, the spatial layer slice model and the ground layer slice model, and the preset service scene constraint condition of the remote internet of things specifically include:
respectively acquiring an equipment set constructed by a plurality of pieces of equipment, a UAV set constructed by a plurality of UAVs and a LEO satellite;
acquiring the calculation task amount and the local calculation CPU cycle frequency of any equipment in the equipment set, dividing the calculation task amount into a local calculation task amount processed on the equipment, an edge calculation amount processed on the UAV and a cloud calculation task amount processed on the LEO satellite, and defining an equipment connection state variable according to whether any equipment is connected with any UAV;
obtaining position coordinates of any device, position coordinates of any UAV, an initial reference channel gain, a distance from any device to any UAV and a LEO satellite altitude, and obtaining a first free space path loss model from any device to any UAV and a second free space path loss model from any device to the LEO satellite based on the position coordinates of any device, the position coordinates of any UAV and the initial reference channel gain;
defining a first transmission power, a channel bandwidth, and a device noise power of any device to any UAV, based on the first transmission power, the channel bandwidth, the device noise power, and deriving a first total data rate of the any device to the any UAV;
defining a second transmission power of any device to the LEO satellite, and obtaining a second total data rate of the any device to the LEO satellite based on the second transmission power, the channel bandwidth, the device noise power and the second free space path loss model;
obtaining a first mission delivery time of any device to any UAV based on the edge calculated amount and the first total data rate, and obtaining a second mission delivery time of any device to the LEO satellite based on the cloud calculated mission amount and the second total data rate;
obtaining total energy consumption for delivering any equipment to a first equipment of any UAV (unmanned aerial vehicle) based on the equipment connection state variable, the first transmission power and the first mission delivery time, and obtaining total energy consumption for delivering any equipment to a second equipment of the LEO satellite based on the second transmission power and the second mission delivery time;
acquiring equipment data calculation intensity, and obtaining calculation processing time of any equipment based on the equipment data calculation intensity, the local calculation task amount and the local calculation CPU cycle frequency;
acquiring effective switching capacity of equipment, and acquiring computing processing energy consumption of any equipment based on the effective switching capacity of the equipment, the local computing task amount and the computing processing time of any equipment;
acquiring the CPU cycle frequency of an MEC server of any UAV and the calculation intensity of any UAV data, and obtaining the calculation processing time of any UAV based on the CPU cycle frequency of the MEC server, the calculation intensity of any UAV data and the edge calculation amount;
determining a preset maximum time delay threshold value in an intelligent inspection task scene, so that the maximum value among the calculated processing time of any equipment, the sum of the first task delivery time and all UAV calculated processing time and the second task delivery time is not greater than the preset maximum time delay threshold value;
determining a preset maximum energy consumption threshold in a video monitoring task scene, so that the sum of total data delivery energy consumption of the first equipment, total data delivery energy consumption of the second equipment and calculated processing energy consumption of any equipment is not greater than the preset maximum energy consumption threshold.
2. The air-space-ground remote internet of things design method according to claim 1, wherein the constructing a network operator revenue model based on the spatial layer slicing model, the ground layer slicing model and the preset service scene constraint conditions of the remote internet of things specifically comprises:
acquiring a device-to-UAV spectrum resource unit price, a device-to-LEO satellite spectrum resource unit price, a unit task edge unit price and cloud computing fixed cost, and obtaining a network operator gain based on the device-to-UAV spectrum resource unit price, the device-to-LEO satellite spectrum resource unit price, the unit task edge unit price, the cloud computing fixed cost, the first total data rate, the second total data rate, the device connection state variable, the MEC server CPU cycle frequency and the computing task amount;
obtaining a first infrastructure payment spectrum unit price, a second infrastructure payment spectrum unit price, a payment backhaul link unit price and a calculation unit price, and obtaining network operator expenditure revenue based on the first infrastructure payment spectrum unit price, the second infrastructure payment spectrum unit price, the payment backhaul link unit price, the calculation unit price, the device connection state variable, the channel bandwidth, the second total data rate and the MEC server CPU cycle frequency;
and obtaining the network operator revenue model by the network operator revenue and the network operator expenditure revenue.
3. The air, space and ground remote internet of things design method of claim 2, wherein the obtaining of the maximized operator revenue value of the network operator revenue model by solving the optimal solution to the network operator revenue model specifically comprises:
optimizing the equipment connection state variable, the first transmission power, the second transmission power, the CPU cycle frequency of the MEC server and UAV deployment to obtain an optimal solution set;
and iteratively updating the network operator profit model based on the optimal solution set until a preset convergence condition is met, outputting an updated optimal solution set, and obtaining the maximized operator profit value from the updated optimal solution set.
4. The air-space-ground remote internet of things design method of claim 3, wherein the optimizing the device connection state variable, the first transmission power, the second transmission power, the MEC server CPU cycle frequency and UAV deployment to obtain an optimal solution set specifically comprises:
acquiring a first preset constraint condition based on the maximum transmission power of the equipment, a second preset constraint condition based on the connection constraint of the equipment, a third preset constraint condition based on the preset maximum time delay threshold, a fourth preset constraint condition based on the preset maximum energy consumption threshold, a fifth preset constraint condition based on the total computing capacity of UAV distribution equipment and a sixth preset constraint condition based on the connection state variable of the equipment;
solving the optimal solution set corresponding to the network operator revenue model maximization based on the first preset constraint condition, the second preset constraint condition, the third preset constraint condition, the fourth preset constraint condition, the fifth preset constraint condition and the sixth preset constraint condition.
5. The air, space and ground remote internet of things design method according to claim 4, wherein the solving the optimal solution set corresponding to the network operator revenue model maximization based on the first preset constraint condition, the second preset constraint condition, the third preset constraint condition, the fourth preset constraint condition, the fifth preset constraint condition and the sixth preset constraint condition further comprises:
converting the sixth preset constraint condition into a continuous variable to obtain an updated sixth preset constraint condition;
acquiring an auxiliary variable based on the equipment connection state variable and the first transmission power, and substituting the auxiliary variable and the updated sixth preset constraint condition into the network operator revenue model to obtain an updated network operator revenue model;
and solving the updated network operator revenue model by adopting a preset standard convex optimization solver to obtain an equipment connection state variable optimal solution and a first transmission power optimal solution.
6. The air, space and ground remote internet of things design method according to claim 5, wherein the updated network operator revenue model is solved by using a preset standard convex optimization solver to obtain an equipment connection state variable optimal solution and a first transmission power optimal solution, and then further comprising:
and enabling the equipment connection state variable, the first transmission power, the MEC server CPU periodic frequency and the UAV deployment to be constant values, and solving the updated network operator revenue model to obtain a second transmission power optimal solution.
7. The air-space-ground remote internet of things design method of claim 6, wherein the making the equipment connection state variable, the first transmission power, the MEC server CPU cycle frequency, and the UAV deployment constant values, solving the updated network operator revenue model to obtain a second transmission power optimal solution, further comprises:
acquiring a non-negative relaxation variable and an additional constraint condition based on the non-negative relaxation variable;
substituting the non-negative relaxation variables and the additional constraint conditions into the updated network operator revenue model, and solving based on the preset standard convex optimization solver to obtain an MEC server CPU cycle frequency optimal solution and an UAV deployment optimal solution.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the space-ground remote internet of things design method according to any one of claims 1 to 7.
9. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the space-ground remote internet of things design method according to any one of claims 1 to 7.
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